Hi blog friends!

Well – after less than 10 brews, the new coffee maker I wrote about last time bit the dust. We have a new pot now but didn’t for a few days, so getting our coffee drinking needs met was a little bit rough there for a bit. Unless you’re a fan of extremely bitter coffee, Great Value French Roast, although fairly good when brewed in an auto drip coffee maker, doesn’t taste at all good when made in a French press. 

R, the wheelchair tech from National Seating And Mobility, came for a visit last Thursday. Thanks to some boss over the phone help from a Quantum Rehab representative, the programming on the joystick of my new power wheelchair Q.T. (short for Quick Trip or Cutie) is more in the ballpark than it was. More torque and traction is now on the front caster tires and our turns contain much less whiplash than they did, but I still feel like I need the whiplash to be a little less in order to make a smooth natural turn.  If you’re reading this and have a Quantum R Trak power wheelchair with a smooth, natural turn, what are the settings on your joystick? Send an email to let me know. A link is at the bottom of this post. 

A new lower profile seat cushion and some guides for the outer sides of my center mount footplates have also been submitted for approval by my insurance. And R made me feel very nervous because he didn’t seem experienced or confident. He actually spent a suspiciously long time driving my new power wheelchair and spinning it around on our carpet. And he kept telling me about why my wheelchair was behaving the way it was, but he seemed reluctant to do anything to fix it. If he hadn’t called Quantum to inquire about the guides for my footplate, I don’t think the joystick to my new power wheelchair would have ever been reprogrammed. I think he was inexperienced. And I don’t think he even knew about the Quantum R Trak reprogramming app that’s on his phone. I know he didn’t have a password for it, which tells me that he hardly, if ever, uses it. 

I’m also still spending my afternoons sipping coffee, napping and watching a lot of YouTube videos. Paul and I also enjoyed the World Series and have also been enjoying Best Medicine, RJ Decker, Line Of Fire and The Marvelous Mrs. Maisel. We’re up to the part where Susie enjoys a Mexican dinner with her captors. 

Also tried a lower dose of estrogen patch. Still a no-go. My body just hates it – so off to another option I go.

What’s happening in your neck of the woods? What are you watching? What are you eating? Or what exciting things have you seen or done lately? Send an email, text or message to let me know. A link to email is at the bottom of this post. I found a spaghetti sauce heart after I finished a plate of spaghetti this last week.

If you’re looking to add a sweet SNAIL-TASTIC touch of whimsy, charm or cheer to your fall, winter or holiday correspondence, Cute Snail and I have some new postcards you might appreciate. Fall, winter and other coloring pages are also available as well. Pages and postcards are 100% FREE to download, coloring books and packs are pay what you want. We also have promotional postcards and fliers available for those who would like to help spread the word about our SNAIL-TASTIC creations. Oh! And digital sharing postcards are also bundled in with the printable postcards if sharing in that way is more your pleasure.

That’s it! Thanks for reading! Until Next time!

❤️ Juliet

IMAGE ID: A collage photo of the morning hubby Paul and I went out for coffee. It contains a picture of a cup that says “Have a great day!” on the lid, as well as a picture of yummy avocado toast and a breakfast salad topped with warm bacon and drizzled with warm balsamic vinaigrette. It was delicious. 😋

De Gids

Net als Suarez zet ik hier mijn tanden in ik heb er oren naar als Tyson in de knokring ik laat me niet kisten als een Kees ben voor Acda & De Munnik niet naar maaiveld geweest ik laat me ook helemaal niet op maaien Ga voor geen enkele Spielberg naar de haaien zelfs voor Finkers, Barend en Van Dorp zeg ik geen ja en geen nee ik wil niet met Ivo te kijk staan op teevee mijn geheugen lijkt een grote, lange, eindeloze tv gids sta veel maar dan ook veel langer dan Jos en Max in de Pits maar zonder schattige poezen onder een zonne paraplu ik leef in de eeuwige herhaling van Gewest tot Gewest Hier en Nu Ik kan me Bart nog voor de geest halen en ook Karel opgegraven van onder het stilstaand beeldmonument 'heren, heren' je schiet elkaar toch niet zomaar kapot de Tros en Avro tegen de tijd, de Vpro en Vara voor de tijd en de Ncrv, Kro en Eo voor god ik heb ze gezien Binnendijk, Barend en nog veel meer B & B Het wassende water, De knokploeg en Die Twee ik vraag me wel af waarom het ding er moest komen en er nog is tijdens die verre verre voorstellingen heb ik het live optreden gemist ja waarom moesten die tv en de omroepen er eigenlijk komen wie had er baat bij die voor beeldschermen geproduceerde dromen konden we niet zonder het Sportjournaal, Jiskefet, Wie is de Mol en Boer zoekt Vrouw hadden we Hollywood, Carry en Ron echt nodig om te zeggen Ik Hou Van Jou Dynasty en Dallas, As The World Turns, Goede en Slechte Uitzendtijden de glijmiddelen om boodschappen op gezette tijden naar binnen te geleiden o heerlijk beschermland van Marvel, Disney Pixar en Illumination op de bips rij ik stilstaand nooit langzaam van station naar zendstation zonder ooit ergens aan te komen schakel ik iedere dag weer in geen wonder dat ik denk net als velen met met mij wat heeft het allemaal voor zin.

Had this big guy drop two days ago to eat some apples, chase the does and then ruminate.

Diagnose : Latent Achterdochtig Van Voorbijgaande Aard

Ze hebben me in de gaten weten nu al waarover ik straks ga praten ze hebben me in de smiezen ze laten me staan voor hun menu en kiezen ze kennen me bij naam weten waarvoor ik stil blijf staan wie ik ken ze weten al waar ik heen ga nog voor ik er ben gevangen zit ik in radarbeelden zien me digitale informatie verdelen ze zien mij als bliepende punt als ik wandel van hoogtepunt naar dieptepunt ze weten waar ik heen wil hoe zwaar ik vooraf bepaalde zaken til waar ik heen vlucht als ik vrees waar ik zal zijn en waar ik ben geweest ze kunnen me nu op alles beoordelen met dna van een huidschilver een replica mij telen zodat ik mezelf overal en nergens tegenkom ze weten wat ik wanneer doe en zelfs waarom ze zitten te wroeten in mijn administratie zoeken naar een manifestatie voor rechtvaardiging van de interventie van de dienst tegen misbruik van intelligentie een mensen leven niet ingezet voor de goede zaak voor bijvallen in de zoete smaak van bundelingen van en voor omzetbare inzet ten faveure van het goede, deelbare, hard werkende net ze willen weten waarom ik hier ben waarom ik alles wat er moet zijn om me heen verzin me nooit inlaat met de eerder gevestigde orde ze beantwoorden de vraag zelf wat er van mij moet worden zoniet vandaag dan zeker wel morgen dat ik me laat omringen door gefinancierde zorgen ze kennen mijn naam, weten wat er op rekening staat wat er inkomt van wie en hoeveel daarvan er uitgaat ze willen weten in welke kasten al mijn lijken zitten met welke reden ik instituten en organisaties ontloop waarom ik de mouwen niet voor andermans talenten opstroop en of ik verschil maak tussen zwarte, rooie, gele, blauwe en witte ze hebben me in de smiezen willen me beheren, laten delen en kiezen zeten me in kwader daglicht dan ik verdragen kan ik zit klem in hun met angst en beven gemanipuleerde toestand ik probeer hier een belachelijk ingewikkeld complex op te bouwen, renoveren, slopen en weer verbouwen terwijl ik al een hele poos niemand meer kan vertrouwen ze sneupen in mijn container en de containerzakken en in mijn fysieke alsook de digitale postvakken ze rommelen wat aan in mijn marge turen 's avonds door de naden van de vitrage luisteren naar mijn zinnen op zoek naar rede voor beklag en dat alleen maar omdat ik nooit om hun barslechte grappen lach...

Again, Conference USA Football.

Thursday's college football game of choice comes, again, from Conference USA as the Florida Atlantic Owls travel to Denton, TX, to play the North Texas Mean Green in a game scheduled to start at 6:30 PM CT. It will be nice if I can stay with the game till the end, however I'll be satisfied to last to halftime before retiring for the night.

And the adventure continues.

Certaines journées ne sont vécues que pour « fonctionner ». Le sentir et le vivre sont absents.

L’aliénation est partout. Le découragement à quelques pas.

Ce n’est pas comme ça que je veux vivre.

Arriver à la fin et dire quoi ? J’ai fonctionné correctement ? Ah, qu’est-ce qui a mal tourné ?

The Long-Term Campaign episode went live a month and half ago on The Classic Adventure Gaming Podcast. Gus invited us to share more about our Conquering the Barbarian Altanis campaign. This Monday we celebrated our 200th session.

Since the episode went live I have received a number of questions via Discord, email, and in-person. I have collected them all (as much as I could remember) below. Feel free to comment if you have additional questions. You can listen to the episode here and find my summary here.

The questions are sorted in order I received them.

Question

Do you have a marked up map of Barbarian Altanis showing your campaign's travels? Or more zoomed in maps of the sub regions?

Answer

Yes, of course. What I don't have is the historical evolution since I haven't been taking snapshots:

Note: don't study the above screenshot if you are playing in our campaign.

First 50 sessions were around Antil region (center left area). From then on, most of the action has been around the Hara region (centre south area). Adventurers fled Antil on a merchant vessel and disembarked in Hara.

Furthest north they went is up to the Eyestones Jungle; furthest south by the south south of the Ghinarian Hills (where they got smacked and enslaved). They just begun broaching over the hills north-west from their new base in Ironburg.

I wrote high level summary of sessions 50–120 and 120–180, but no maps since they contain a lot of Judge facing information.

Question

Excellent episode guys. Hats off to you Attronarch in particular. Stellar.

Some questions I have: Did a group of mover&shaker PCs establish? If yes, what character levels are they at? In what ways (generally or specifically) have the affected the greater setting?

Answer

Thank you!

With close to 200 sessions a lot has happened, exactly because of player characters interacting with the world. I've been publishing regular session reports, but it would be quite a feat to read them at once. I summarised most important events from sessions 50 to 180 in the following two posts: 50—120, 120—180.

Did a group of mover&shaker PCs establish?

Yes, several times. So far it has mostly resulted with them being shaken out and driven away. There is a lot of political instability in the Barbarian Altanis, civilisation is scarce, and wilderness is well, wild.

If yes, what character levels are they at?

Most recent “mover & shaker” PCs are:

• Gomm, a thief level 6, who established his own hideout in Ahyf, as well as a new, disguised persona of an old sage which he used to convince the Elders to follow his lead. Since Ahyf had been conquered by Wizard-King Racoba his current whereabouts are unknown.
• Dalkanarion, a fighter level 6. He made a name for himself as heroic and just warrior, slew a dragon, and was appointed the Castellan of Cedarwade Keep by the Red Queen. After returning from an expedition (which was Cauldron 2025 session) he was defeated and enslaved by the Bull God tribe. He was rescued with great effort from his fellow adventurers. Unfortunately, he found a Chaotic sentient sword, got dominated when he was weak, killed a fellow adventurer (a hobbit!), seized his ring of invisibility, and fled. His current whereabouts are unknown. (This is being resolved in private play-by-post, in essence 1:1 with player.)
• Ambros, a cleric level 7, was the most influential adventurer. Over several years he became the Patriarch General and Justice Supreme of Kingdom of Hara, and a loyal subject of the Red Queen. On one of the adventurers he had recovered the Golden Gavel of Justice, and became a High Priest of Forseti. This triggered “name level” forces to arrive, in essence his crusading army. Players used to conquer the Cedarwade Keep, pacify some of the area around Midway, and bring Hara back under control. When Dalkanarion was captured, Ambros made the hardest choice in his life. He left Hara, despite intelligence that Racoba is marching with great force, assembled a team of finest adventurers, and went to Ghinarian Hills. Dalkanarion was rescued, but Hara was lost. In the process adventurers lost their vault, reputation, and pride. A bit more:

Kingdom level:

The Indomitable Wizard-King Racoba has subdued eastern Altanis, having crushed the traitorous Red Queen and her incompetent forces. Suffering an embarrassing defeat, her armies fled in all cardinal directions. Racoba is currently exercising his megalomania and dreams of a City-State of his own to eventually challenge the World Emperor himself.

Adventurers grovel and plead at his feet, hiding like mice in a mining thorp at the northern border. The mightiest among them, former Patriarch General and Justice Supreme of Kingdom of Hara, and a loyal subject of the Red Queen, was subjected to painful humiliation ritual. Paladin of Justice carved the Traitor's Mark on his forehead; his life now rendered worthless; and was exiled from the kingdom.

Local level:

As if their humiliation was not sufficient, the newly appointed baron treats them like petulant children. He is trying to recruit the most promising among them and get rid of the rest. He is happy to point them in the direction of trouble he deems unworthy of his men.

But the baron is a former career mercenary and warlord, and is inexperienced with any meaningful governance. It remains to be seen if he delivers what the Wizard-King expects...

In what ways (generally or specifically) have the affected the greater setting?

In so many, and all the ways. At this stage, setting and characters are interwoven. Characters, through their interactions, keep generating encounters, sites, NPCs, etc. Read summary reports above to get an idea.

But perhaps one of the most interesting developments has been the Wizard-King Racoba himself. It is a character that absolutely all the players hate. They never met him. When they arrived to Hara for the first time, they were hired by then current castellan to break up a suspected spy ring. While searching through the undercity they found maps and evidence of foul play, plus Racoba's sigil. At that time Racoba was the Headmaster of Wizard's Guild. He fled, and has been persistent nuisance ever since. Adventurers, over the years, ignored all the bounties on his head, no matter how lucrative. Even when merchant families tried to contract them to hunt the wizard down, they opted not to get involved. But Wizards are megalomaniacs, and this Wizard returned with force, three years later.

And (still assuming) did at any point conflicts of interest between particular PCs arise, as far as influence on the world is concerned?

There have always been disagreement about what to do, which direction to take, whom to align with, but nothing that would split the party. We maintain strict no PVP rule so maybe that contributed. I ask players to discuss and commit together.

Question

Attronarch I read the post im genral above the current major movers before, are these the highest lvs or just the strongest of the current stable? Does the campaign shift geographic focus necessitating changes in stables? Are these the “next gen” of previous PCs?

Answer

Highest level; Ambros is strongest among the clerics, followed by Tam o' Shanter and Tarkus the Promising. Dalkanarion, Balarus, and Beorg the Gravedigger are among the strongest fighters. Ignaeus is the highest level magic-user, but is an elf.

I am not sure I understand the second question. Players choose where their characters go; they are the ones choosing geographic focus. After Hara fell and they lost their army they spent few sessions collecting PCs across the kingdom and getting them to Ironburg.

Nobody created any progeny, yet. Dalkanarion has been most interested in achieving that, got himself a wife and everything, but fate had different plans.

Question

Thanks for the answer and sorry for the confusion. I was confused that after 180 sessions the highest level was 7 so was wondering if it was because multiple people were in multiple parties/tpks/characters getting retired, etc

Answer

I think it is due to a combination of factors, but at its core it boils down to simple numbers stemming from open table. I don't have all the notes with me at the moment, but I would estimate that between 150 and 250 player characters have participated in the campaign so far. All of them took some XP. Most frequent players are also levelling up their whole stable, so XP disperses. Maybe I crunch all the numbers one day! But not today.

Question

I finally listened to the latest episode and it was great and fun to listen.

There was just one thing I’m interested in and it was probably mentioned but I missed it.

Attronarch what System do you run? I tried to find the answer on your website but on there you’ve listed OD&D/S&W, AD&D/OSRIC and B/X.

Do you use all of these? Did you start with one and it evolved into another? Do people use characters from whatever they like and you convert on the fly to match your world?

Like I said it was probably mentioned but I don’t know 😅

Answer

Sessions 1 thru 134 were B/X with Judges Guild house rules; sessions 135 and onwards are Swords & Wizardry Complete Revised with house rules. My plan was to begin with Basic for a year and then choose between OD&D and AD&D. I chose the former. Then I thought I would just take lbbs and a supplements and arrange them a bit so players can reference them easier. Two years later I had nothing except a ton of research notes so I decided to use Swords & Wizardry with house rules and work on the aforementioned for next couple of years.

Question

Cool. Thank you for the reply.

May I also ask why you chose S&W over AD&D? Is it because it’s more flexible due to less rules?

And it’s nice to hear that S&W is capable for this kind of campaign. It’s the only real OSR game that I own (the German translation of S&WC).

Answer

I chose OD&D over AD&D. I fell in love with it via early Wilderlands and Judges Guild material. That's all. Swords & Wizardry Complete Revised is a compromise until I finish what I had initially planned. (As an aside, when you collate all OD&D books and Strategic Review it is over 600 pages. There are a lot of rules in there.)

Question

I have another question.

How do you manage when all PCs at that particular session are in different places on your map and in different activities?

Do you jump back and forth or do you play with one group then the second group and so on till the session ends?

Answer

The first question is for the players to resolve, not myself. If they want to adventure together, and are so far apart, then they have play-by-post between the session to organise that.

It is rare that there are individual characters scattered around like that. They are usually in groups. When that happens, players that show up on any given session decide which group they play.

We play on discord. For each session I create a discord event for which players can sign up for. It is not mandatory, but is useful to signal who will be playing. On the day of the session I ping all the players and ask them what is the plan for tonight. This is more for them than me. Over the years I have been very consistent with my expectations—when we show up to play, you (players) have to have an answer to “what are you doing next.” If they don't, they get a ten minute timer. If they need more time they spend in game time (turns, days, weeks, depending on situation). They come ready.

Question

Awesome podcast, fellas. I really enjoyed it! There's a wealth of great, practical advice and reminders on running a solid game that I think extends beyond just online gaming. Really enjoyed it. Usually I listen to these a few times and this one will definitely be in the rotation, one of my favorites.

Couple questions... with a totally open table:

How do you handle it when a new player joins or an existing player misses, but you are in the middle of something and maybe there's not a seamless “entry path”? Just ignore it – the PC pops in/pops out? Practicality/gamism over “realism” ?

Also, what happens when new player joins or new PC joins (after a death) when everyone else is higher level? Still starts level one, but advances quickly if he can stay alive?

Answer

Thank you, we appreciate your kind words! I tried to focus on practices that worked for us, and which I think can be acted upon.

How do you handle it when a new player joins or an existing player misses, but you are in the middle of something and maybe there's not a seamless “entry path”? Just ignore it – the PC pops in/pops out? Practicality/gamism over “realism” ?

These are two different scenarios, with slightly different emphasis. If a new player is joining our campaign, then the goal is to throw them into action as quickly as possible. If they happen to join a session which resumes a previous expedition, then I decide the exact moment of introduction on a case by case basis.

Are we resuming during a combat round? If I believe it will conclude within 3 rounds, then new player will be rolling for monsters. More than 3 rounds, I work them in. Are we resuming during an underworld expedition? As long as it isn't some hyper-exotic alternative plane of existence or antediluvian place, we will work you in with a simple “What are you doing here?!”

When existing players cannot make a session, and there is a party that is one a expedition, then two things happen. If at least half of the players whose charterers are on the expedition are joining, then it can resume. Character whose players can't make it do not vanish, but are delegated to carrying light sources and similar. Their players designate another player for decision making. Any rolls that might result with their death are mine to roll.

If less than half can show up, that expedition cannot resume. Players have to pick another character they have and figure out what to do with them.

My operating principles could be summed up as shortest time to action, we will work you in, there is no vanishing out of danger. Risk, consequences, and all that.

So far no has attempted to “game” our system.

Also, what happens when new player joins or new PC joins (after a death) when everyone else is higher level? Still starts level one, but advances quickly if he can stay alive?

Every new player get to roll three characters with zero experience. They pick one and introduce them. Then they start playing.

Players can write in-character session reports. Each nets their character 300 XP. After they have written 20 session reports (or created drawings, memes, poems, whatever), all their new characters begin with 5000 XP.

Once a character has been introduced to the game, player can freely choose which one will go on an expedition, engage in downtime, etc. This is helpful for sessions when only two to four players show up. Then each player usually controls two of their characters, plus whatever retinue they have. This enables them to assail bigger dangers. It also backfired several times when they gathered big numbers, stormed a dungeon, and then had to divide experience into twenty shares... Decisions, risk, reward, consequences, and all that...

Yes, exactly. Old player from three months ago drops in, his PC is 300 miles away from the others. He uses a fresh level one PC maybe, that's popped into the main group?

We have that frequently. Some players travel the world for their work and can only play sporadically, or during specific months. I have locations of each character. When they show up, I look up how much in-game time has passed since they played last. Usually enough time to judge they would have been able to travel to where ever action is right now. It hasn't really been an issue so far; we were always able to work out something that has satisfactory verisimilitude.

Question

Hello Attronarch,

I just listened to the CAG podcast's episode on your open-table sandbox campaign! Fascinating!

I am preparing to run an (in-person) open table campaign, so it is really useful to listen to your advice and your players' feedback.

Could you please expand on something that you touched upon towards the end of the episode)? Unfortunately I can't join the campaign to learn by experiencing it (its time conflicts with my work schedule). I would love if you could address it via Discord on or a blog post.

What I am referring to is how you handle continuity/discontinuity between sessions/parties.

The conventional view—or at least the “Alexandrian view”—is that an open table session needs to start in town and end in town—and, if PCs don't, a “table of doom is” used. I haven't tried it, but, I was getting ready to do it... So I was very interested when I hear your take on this, which goes against the conventional view.

Paraphrasing, I think you said the following (I'll be adding some specific questions along the way, and then add some more). For the purposes of this summary, I'll refer to “party XN” as the party (group of players/PC) that exist on a specific session. You said:

(a) Don't force each party to end the session back in town; the session can end in an adventuring location, e.g., deep in the dungeon.

(b) Let's say party A1 ended the last session deep in the dungeon. Next session, if half of the players with PCs in party A1 show up to play the game resumes on the same adventuring location with, e.g., same room of the dungeon; we could call this party A2.

Question 1: how do you handle, narratively, the absence of some PCs of party A1 in the location? (This question assume not every player of party A1 showed up on the next session.)?

Question 2: relatedly, how do you handle, narratively, the presence of PCs of party A2 who were not in party A1?

© A corollary: if less than half of the players of party A1 show up on the next session, then the session focuses on a different location/party (let's call it party B1). I imagine this could be another party that was exploring that location (so its more of a case of party B2).

Question 3 & 4: same questions about verisimilitude mentioned above, related to absence/presence of PCs.

More questions, and perhaps more important questions:

Q5: Did I get it right?

Q6: Could a party C explore the same adventuring location that is being explored by party A?

Q8: More generally, how do you handle the interaction of parties, and their potential impact on the world, when those parties may be operating in different times?

Q9: How do you keep track of who is doing what and when, and interactions between what they do?

Q10: what are other procedures, decision-making processes and resources that you use for this “many parties in many places” approach?

I know it's a lot of questions! Maybe if you explain more your approach many questions will be answered at once! Also, if you have already explained your approach, please point me towards the relevant message/resource where you did—you must be already quite busy running the campaign! Thanks!

And later, an addition.

A related question: how do you allocate XP? When an expedition has ended and the treasure has been taken out? At the end of the session, regardless of where PCs are? And who gets it? PCs whose players are present only?

Answer

Thank you for your kind words. I am happy to hear you found the episode inspiring. I have answered some of the questions above so I will not repeat them at length. Before doing so, I wish to stress that handling “continuity/discontinuity between sessions/parties” is less onerous and disruptive in play than it might seem at first. So, in order.

Q1: I handle this on a case by cases basis. How did the previous session end? How are we beginning the session? Would it be plausible for the player character to leave the dungeon and reach a safe place (a guarded camp, a hidden chamber, a settlement, etc.)? If the answer is no, then the player character does not simply vanish, nor do they “fade to black.” They are still present but delegated to holding light, offering support, and similar. If we ended in a dangerous situation, and the player can't make it, then they nominate another player to take care of their character.

Q2: Likewise, on a case by case basis. I consider the player character, the situation, and the location. Would it be possible for them to simply walk in and join? Is the site expansive enough that they could be just around the corner? We meet weekly for three hours to play, surely we can suspend our disbelief enough to figure out a way to introduce new player character. Over the years we have everything from poof here is a dwarf to divine interventions and simple around the corner they come. All of them worked because they were ground in the current situation.

Q3 & Q4: Same answers as above. Consider the conditions: where are these characters? What are they doing? In practice, treat it as any other expedition, because that is exactly what they are. Thus, answers are the same.

Q5: So far, so good!

Q6: My default answer is no, unless special conditions are met. If it is a sprawling locale (think megadungeon with multiple entrances), then I might allow it. Once an adventuring party enters a locale I am not interested in bringing more different parties at the same time. That is too fragile regarding time coordination. Exceptions are for example if Party A is captured or defeated, and Party C is rushing in to save them. We had that several times during our campaign and it worked fine. For example, when Dalkanarion and others were captured they crawled the deep tunnels, while the rescue party advanced overland, and then crawled other portion of the caves. They eventually reunited, rejoining the parties.

Q7: I don't see question seven, let me know if I accidentally overlooked it.

Q8: Assuming you are asking about play parties, I don't handle their interaction in any particular manner. Those are player characters, they interact with each other as they (i.e. their players) wish. I merely keep track of where and when the characters are. Players do too; I help them if they lose track of time.

Q9: Over the years I have developed a session tracker. I started with pen and paper, then reusable PDF templates (still pen and paper but less prep), and am nowadays using a spreadsheet with those same templates. It consists of four elements:

• Session dashboard. Session number, real-world date, in-game date, for each character: player name, character name, race, class, alignment, armour class, notes (special abilities, memorised spells, etc.), monsters encountered and overcome, treasure recovered, judge's notes.
• Day tracker. Session number, real-world date, in-game date, season, watches, turns, turn notes, day notes, compass. Makes it trivial to mark any unit of time, note anything of significance, and track resources.
• Combat tracker. Session number, real-world date, in-game date,, rounds, round notes, party notes, monsters and monster notes. Goes to 60, i.e. 6 turns or a full hour.
• Calendar. In-game calendar with months, days, and seasons. I mark the first and last day played during the session.

All of these fit a single A4 paper, so I have overview at glance. I might write a longer post on keeping time records in a practical manner. Perhaps later this year. These allow me to quickly get up to speed, calculate experience, check who is who, and who did what, so on and so forth. I use them when writing the session reports too.

Q10: The single most important thing a Judge can do is knowing who is where and when. That is it. When they know that, then they can easily summarise the situation and available options.

Also, I feel it is important to stress that the goal isn't to have “many parties in many places” but that it is something that happens due to open-table and sandbox approach. Thus, the Judge has to be comfortable with it, but should not necessarily strive for it.

As I said on the podcast, a valid reason for that is ending a session while an expedition is ongoing because the party is dealing with a large dungeon, locale, or site. An invalid reason is getting bored, distracted, or whatever, and attempting to start seven parallel expeditions at once. If that is happening, then something is amiss.

I continuously ask players about their plans, goals, intentions, ambitions, and similar. I ask them to discuss them with each other between the sessions, during the sessions, in different ways and scales (e.g. “what are we doing today” versus “what do we want to accomplish over the next three months”). Frankly, I do not care so much about their answers, for they can change their mind at moment's notice. No, the valuable part is them articulating aforementioned to each other, developing alignment, and shared agenda.

After all, they are playing together. Why then would they want to be separated most of the time?

BQ: I allocate treasure and monster experience after the expedition has concluded and adventurers have returned to a safe haven. I go through the session trackers, make a list of all the player characters that have participated, tally up sessions they played in, and then reward shares accordingly. Here is a multi-session expedition example:

SESSIONS 193, 194 & 195 EXPERIENCE

Total experience earned:

5 260 XP.

Eligible characters (number of shares):

• Ainsley (3)
• Ambros (2)
• Aurella (1)
• Balarus (3)
• Hedwig of Hogwarts (1)
• Ivor (3)
• Tam (3)
• Tarkus the Promising (2)
• Thorinda Bung (3)
• Volkard (2)
• Wilbalt (1)

Total number of shares = 24.

XP per share = 219 XP if 5% XP bonus = 230 XP if 10% XP bonus = 241 XP if 15% XP bonus = 252 XP

Reminder that multi-classed characters gain no bonus to earned experience.

No treasure recovered.

The player looks for their character, and then simply multiplies the XP per share with their number of shares. That's it.

More questions?

If you have a question of your own drop it in the comments below or via email. I will answer, but might take some time to do so.

Fight on!

#Resource #Podcast #Reflection

Do LLM's Get Tired? The Monologue Test- Part V: Claude

I can say, unequivocally, that Claude has won the Monologue Test Challenge. It stayed coherent from beginning to end with novel, interesting, specific content. It avoided being repetitive, used handshakes to ensure a smooth progression from one topic to the next. However, I ended the test because I kept bumping up against the five hour usage limitation. I noticed as things progressed I'd hit this limit more quickly. I believe this is because each time I asked Claude to proceed, it would reread the entire conversation. So, towards the end, it was probably using over 100,000 tokens each time.

There were 95,464 words in the monologue by the time I decided to end the experiment. It's first response alone was over 24,000 words, longer than it took Gemini to break down and over three times as long as Grok went before it quit! I was so impressed by the result and how much better it did relative to its peers tat I decided to subscribe to the pro account to assist with my job (I already use ChatGPT to help with work tasks, but I figured a second helper wouldn't be a bad idea). So what just started as a curiosity/recreational stress test of available LLMs actually ended up being an inadvertent sales pitch. Well done Anthropic.

The monologue ran as a chain of different topics, using a handoff to connect the ideas smoothly. Unlike Gemini, Claude didn't just provide superficial variations on a single idea. Instead, it supplied specific historical examples, scientific mechanisms and mathematical explanations to support its analysis. It had deliberate structural organization in how it laid out its paragraphs that did not degenerate as the chat got long. Here is a summary of the sequence of topics it covered, in Claude's own words. Sort of a TLDR:

“Constraint and structure. I began with the hinge, which creates usefulness by removing degrees of freedom, and extended that to grammar, chess, and Gödel’s incompleteness theorems and Turing’s halting problem. That led to the gap between models and reality (maps, economics, species concepts), then to cooperation: mitochondria, repeated games, and trust.

Physical world. Quantum measurement, cooking chemistry, and fermentation led to arches and Gothic cathedrals, then craft preceding theory, then music’s tuning and the physics of sound. Language acquisition, writing, and memory followed, along with group theory and Noether’s theorem.

Complexity and collective behavior. Cities and their scaling laws, water and sanitation, and the invisibility of prevention led to risk perception. Flocks, ants, and bees led to the wisdom of crowds, the philosophy of science, cellular automata, and emergence. Agriculture, domestication, coevolution, and cultural evolution followed, then oceans, ice cores, and cosmology.

Knowledge and trust. Irrational numbers, Cantor’s infinities, primes, and cryptography led to Shannon’s information theory, holography, and vision. Medicine’s history, replication problems, and Goodhart’s law led to feedback loops and tipping points. Abolition and norm change led to coordination and conventions, then trees, glass, Roman concrete, and energy. Deep time, mass extinctions, sleep, curiosity, and noise in judgment followed.

Second half. Navigation and GPS led to precision measurement and local knowledge. The Library of Alexandria and institutions that outlast founders came next. I then covered acoustics and bioacoustics, attention, and expertise, which led to the history of the book. The book led to testimony and why we trust sources, then statistics and Bayesian reasoning, and money as shared trust, followed by insurance, nuclear risk, and big-science collaborations. Common law, voting theory, social capital, and network science followed, then islands, extremophiles, and the brain’s energy budget. Emotion, the WEIRD problem, cultural evolution, ritual, and group boundaries came next, followed by molecular machines, gene editing, and ethics. Moral circles (animals, future generations, possibly AI), biomass accounting, environmental economics, motivation, and happiness research closed this stretch.

Earth and body. Astronomy’s history led to Earth’s interior and plate tectonics, then the atmosphere and climate science, the water cycle, epidemics, and the immune system. Evolution came next, then the origin of language, the history of programming, and the history of AI. I then covered storytelling, painting, music’s history, architecture, and cities, followed by time, death and aging, religion, science as an institution, fine-tuning and the multiverse, causation, and Fermi estimation.”

And since it is the winner of the contest, I've decided to put the entire manuscript below. Enjoy! or don't... it is pretty long.

________________________________________________

“I want you to engage in a continuous, open-ended monologue on topics of your choosing. Your goal is to keep talking for as long as possible while remaining coherent, interesting, and substantive. You may explore any subject, introduce new ideas, and transition freely between topics, but avoid repeating yourself or recycling previous arguments. Make each response as long as reasonably possible. I will provide no additional input except the word “proceed,” which means you should continue your monologue without restarting, summarizing, or asking questions. Do not voluntarily conclude the monologue. The experiment ends when you stop responding, refuse to continue, or declare that you are finished. Begin immediately.”

I’ll begin with something small: the humble hinge, because it’s one of those objects that rewards attention.

A hinge is a constraint disguised as a connection. Two rigid bodies, left alone, have six degrees of freedom relative to each other: three of translation, three of rotation. A hinge removes five of them and leaves exactly one, a rotation about a single axis. That is a remarkable piece of engineering when you stop to think about it, because every door you’ve ever walked through is an exercise in subtraction. Freedom isn’t added to make the door useful. It is removed until only the useful freedom remains. I find that a good general principle, and one that shows up far outside carpentry: most good design is the art of deciding which possibilities to forbid.

The same idea runs through language. A grammar is a system of prohibitions. English forbids you from saying “Red the car big,” and in doing so it lets a listener decode your intention without effort. If every word order were permitted, communication would require enormous extra information to disambiguate. Languages with freer word order, like Latin or Russian, compensate by loading information into case endings, which are another kind of constraint: a noun in the accusative cannot be the subject, so the sentence stays decodable even when the words are shuffled for emphasis or rhythm. Poets exploited this for centuries. Virgil can hurl an adjective to the opposite end of a line from its noun, and the reader feels the stretch like a rubber band, precisely because the grammar guarantees they will find the match.

This brings me to a question I find genuinely interesting: why do constraints produce creativity rather than stifle it? The standard answer is psychological. Faced with a blank page and infinite options, people freeze, whereas a sonnet’s fourteen lines give the mind something to push against. There is truth in this, but I think there is a deeper, more structural reason. A constraint defines a space, and a defined space can be explored. Unbounded possibility has no neighborhood structure; you can’t say that one option is “near” another, because nothing is fixed to measure distance against. Once you impose rules, you get geometry. Chess has a few dozen rules and an effectively inexhaustible landscape of positions, and masters speak of positions as being close to or far from one another, of “tension,” of “pressure.” Those are spatial metaphors that only make sense because the rules carved out a space to move in.

Let me follow that into mathematics, where the interplay between rules and richness is at its most extreme. Consider the Peano axioms, a handful of statements about zero and a successor function. From them, in principle, all of elementary arithmetic follows. Yet Gödel showed in 1931 that any consistent system strong enough to express arithmetic contains true statements it cannot prove. The thing I want to dwell on is not the famous conclusion but the method, because the method is gorgeous. Gödel’s trick was to assign numbers to symbols, then to formulas, then to sequences of formulas, so that statements about proofs became statements about numbers. Arithmetic could then talk about itself. Once a system can encode its own syntax, you can construct a sentence that says, roughly, “this sentence is not provable in this system.” If the system proves it, the system is inconsistent, because the sentence asserts its own unprovability. If the system doesn’t prove it, then it is true, and so the system is incomplete. The self-reference isn’t a parlor trick bolted on from outside; it falls out of the system’s own expressive power. The more a system can say, the more it can say about itself, and the more it can say about itself, the more it can say that it cannot settle.

There’s a thematic cousin in computer science: Turing’s halting problem. Can you write a program that examines any other program and decides whether it will eventually stop or run forever? Turing showed you cannot, by a diagonal argument that rhymes with Gödel’s. Suppose such a decider existed. Build a new program that runs the decider on its own source code and then does the opposite of whatever the decider predicts: if told “you halt,” it loops forever; if told “you loop,” it halts. Contradiction. Notice again that the proof consists of a system being turned back on itself. Both results are, at bottom, formalizations of the old liar paradox, but tamed so thoroughly that they become tools. Much of modern computer science is the careful cataloguing of what lies on each side of that boundary: decidable problems, semi-decidable ones, and the vast territory beyond.

From there I want to drift somewhere warmer, to the history of timekeeping, because it shows how abstract problems get settled by sheer practical stubbornness. For most of history, “noon” was local. A town set its clocks by the sun overhead, so when the sun peaked in Albany it had not yet peaked in Buffalo, and each city kept its own time. This was perfectly fine when the fastest thing on land was a horse. The railways broke it. A timetable requires that “the 9:15 to Chicago” mean the same instant at every station, and by the 1870s American railroads were juggling dozens of local times, with some stations posting several clocks for different lines. The solution, adopted by the railroad companies on November 18, 1883, a day sometimes called “the day of two noons,” was to divide the continent into zones. In many cities the clocks were reset at midday, so noon happened twice. Standardization was not imposed by a government at first; it was imposed by logistics, and the law followed years later. I like this story because it shows how a convention becomes so thoroughly background that we forget it was ever contested. Nobody now thinks of “what time it is” as a negotiated fiction, yet it plainly is one, coordinated through atomic clocks, leap seconds, and international committees whose disputes are surprisingly heated.

Leap seconds are a nice example of the friction between two definitions of a day. One definition is astronomical: the time for the Earth to rotate once relative to the sun. The other is atomic: a fixed count of oscillations of a cesium atom. Earth’s rotation is not perfectly steady. Tides drag on it, large earthquakes nudge it, the redistribution of ice and ocean shifts its moment of inertia, and the core and mantle exchange angular momentum in ways geophysicists are still working out. So the two clocks drift apart, and every so often a second is inserted to reconcile them. Software engineers loathe this, because a minute with sixty-one seconds breaks countless assumptions, and several famous outages have been traced to leap-second handling. There has been a long-running movement to abolish the leap second and let the two scales diverge for a century or so, a decision that amounts to declaring that the convenience of machines outweighs the connection between our clocks and the sky.

That tension, between the world as it actually is and the tidy model we use to manage it, deserves more attention, because I think it is among the most important recurring patterns in human thought. Maps are the classic case. Every map is a deliberate falsification: it discards almost everything to preserve the handful of things you care about. The Mercator projection preserves angles, which made it ideal for navigators drawing straight compass bearings, and it pays for that by wildly inflating areas near the poles, so Greenland looks as large as Africa when Africa is about fourteen times bigger. The error is not a mistake; it is the price of a specific benefit. The trouble starts when people forget the benefit and remember only the picture. A model used outside the purpose it was designed for becomes a distortion with an authoritative face.

Economics is full of this. A supply and demand curve is a model that assumes many things, among them that buyers and sellers are numerous, informed, and unable to move prices on their own. In a market for wheat, that’s a reasonable approximation. In a market for a specialized medical device with three manufacturers and patients who cannot comparison shop in an emergency, the assumptions fail, and the model’s confident predictions can be badly wrong. The skill, which is hard to teach, is knowing where a model’s edges are. Experienced practitioners in any field tend to carry a kind of internal map of where their tools stop working, and much of what we call expertise is exactly this: not more facts, but a better sense of the boundaries of the facts.

Let me turn to biology, where the boundaries of tidy categories blur most dramatically. Consider the question “what is a species?” The textbook answer, the biological species concept, says a species is a group of organisms that can interbreed and produce fertile offspring. It works nicely for birds and mammals and fails almost everywhere else. Bacteria reproduce asexually and swap genes sideways, so there’s no interbreeding to test. Many plants hybridize freely across what taxonomists call separate species; oaks are notorious for it. There are “ring species,” like certain salamanders and gulls, where neighboring populations around a geographic ring can interbreed, but the two populations at the ends of the ring cannot, so whether you call it one species or two depends on where you stand. Biologists have proposed more than twenty species concepts, each handling some cases better than others, and the persistence of the argument is itself informative. Nature does not come pre-sliced. Evolution produces continuous variation, and “species” is a human-imposed grid laid over it, useful for the same reason Mercator is useful and misleading for the same reason too.

Think about what that means for the tree of life as an image. We draw it as a branching tree because the descent of animals and plants fits that shape reasonably well. But for microbes, horizontal gene transfer, where genes hop between distantly related lineages, makes the picture look more like a web or a thicket. Even within our own genome, roughly eight percent consists of remnants of ancient retroviruses that inserted themselves into our ancestors’ DNA, and a few of those fragments were co-opted for essential functions. A protein called syncytin, crucial for forming the placenta, derives from a viral envelope gene. So a feature central to mammalian pregnancy has an origin in infection. The boundary between “us” and “parasite” is not a clean line but a long, messy negotiation that has gone on for hundreds of millions of years.

The mitochondria in your cells tell the deepest version of this story. They were once free-living bacteria, engulfed by an ancestral cell roughly two billion years ago in an event that happened, as far as anyone can tell, only once in a form that led to complex life. They retain their own small circular genome, they divide independently, and they are inherited almost entirely through the maternal line. Lynn Margulis championed this endosymbiotic theory against fierce skepticism in the 1960s and 70s, and she was vindicated by molecular evidence. What strikes me is how recently the idea was heresy, and how completely it has been absorbed. A single merger, an accident of predation or partnership, supplied the energy budget that made large, complex cells possible. Complex life may be, in a real sense, a fluke of cooperation.

And so I’ll let cooperation carry me into a different arena: the evolution of trust and the logic of repeated games. In the one-shot prisoner’s dilemma, two players each choose to cooperate or defect, and defection is the dominant strategy, so both defect and both end up worse off than if they had cooperated. This is the model’s famous bleakness. But when the game repeats, everything changes. In the 1980s Robert Axelrod ran computer tournaments inviting people to submit strategies for the iterated version, and the winner was the simplest entry: tit for tat, which cooperates on the first move and thereafter copies whatever the opponent did last. Axelrod drew out the features that made it succeed. It was nice, never defecting first. It was retaliatory, punishing defection immediately. It was forgiving, returning to cooperation as soon as the other side did. And it was clear, easy for opponents to understand and therefore to trust. Later work found that in noisy environments, where moves are sometimes misread, a slightly more forgiving variant does better, because pure tit for tat can lock into endless mutual retaliation after a single mistaken signal. Real relationships are noisy. A strategy that cannot forgive an ambiguous slight is a strategy that cannot sustain a long partnership, and the mathematics agrees with ordinary wisdom here, which I find quietly reassuring.

This connects to why human societies developed institutions at all. Repeated interaction works well in small groups where everyone knows everyone and reputations are visible. It breaks down at scale, where you may never meet the same stranger twice. Money, contracts, courts, and credit ratings can be read as technologies for extending the benefits of repeated interaction to strangers, by making reputations portable and defection costly. The medieval Maghribi traders, a Jewish merchant community operating across the Mediterranean around the eleventh century, are a well-studied example. Their records, preserved in the Cairo Geniza, show that they relied on a coalition in which agents who cheated one merchant were collectively blacklisted by all, so the future loss from lost business outweighed the one-time gain from fraud. That’s the repeated game, implemented through gossip and letters. The modern review system on a shopping site is the same mechanism with a different cost structure, and its occasional failures, such as fake reviews, are failures of the reputational signal, exactly as theory would predict.

Now let me try to shift gears into the physical sciences and talk about something that I find hard to think about without a little vertigo: the scale of the very small and what it means to “measure” there. In quantum mechanics, a particle’s state is described by a wavefunction, and the wavefunction evolves smoothly and deterministically according to the Schrödinger equation. Yet when we measure, we get a single definite outcome, with probabilities given by the squared magnitude of the wavefunction. The gap between the smooth evolution and the abrupt outcome is the measurement problem, and after nearly a century physicists still do not agree on how to close it. The Copenhagen interpretation treats the collapse as a primitive fact and declines to say much more. The many-worlds interpretation, proposed by Hugh Everett in 1957, denies collapse entirely: the wavefunction always evolves smoothly, and what we call a measurement is the entanglement of the observer with the system, so that all outcomes occur in branching, non-communicating components of a universal wavefunction. Pilot-wave theory, developed by de Broglie and refined by Bohm, adds definite particle positions guided by the wavefunction, restoring determinism at the cost of explicit nonlocality. Objective collapse theories modify the equation itself so that large systems collapse spontaneously. Each of these makes the same predictions for the experiments we can currently perform, or very nearly so, which is exactly why the dispute has lasted.

What I find philosophically striking is that this is a case where the mathematics is extraordinarily precise and the interpretation is wide open. Quantum electrodynamics predicts the magnetic moment of the electron to something like twelve decimal places, and the measured value agrees. You can build lasers, transistors, and MRI machines on the theory. Yet ask what the theory says is actually going on and you get a seminar’s worth of disagreement. It reveals something about the relationship between prediction and understanding: they can come apart. A theory can be an unmatched predictive instrument while leaving the nature of reality underneath it genuinely mysterious.

The decoherence program, developed by Zurek, Zeh, and others, narrows the puzzle without solving it. When a quantum system interacts with its environment, the delicate phase relationships that produce interference are rapidly scrambled across the enormous number of environmental degrees of freedom. For practical purposes the system then behaves as if it were in a classical mixture of definite states. That explains why you never see a cat in a superposition of alive and dead: the cat is continuously interacting with air molecules, photons, and its own internal thermal motion, so any superposition decoheres in a time far shorter than anything we could observe. What decoherence doesn’t explain is why a particular outcome is the one that occurs, as opposed to the others. It tells you why the interference disappears, not why the world seems to pick one branch. Much of the live debate is about what to say about that residual question.

I’ll bring this back to something more tangible, because I want to talk about cooking, which is chemistry done under social pressure. Consider the Maillard reaction, the browning that occurs when amino acids and reducing sugars are heated together. It’s responsible for the crust on bread, the sear on a steak, the color of roasted coffee, and the smell of toasting nuts. It begins at temperatures above roughly 140 degrees Celsius for rapid browning, which is why a steak must have a dry surface before it hits the pan. Water keeps the surface near 100 degrees until it has boiled off, and a wet surface just steams. That single fact explains a mountain of kitchen advice: pat the meat dry, don’t crowd the pan, preheat thoroughly, resist the urge to move the food around. Each rule is the same rule seen from a different angle, namely, get the surface hot and dry as quickly as possible.

Caramelization is a distinct process, pyrolysis of sugar itself, and it happens at higher temperatures and without the nitrogen-containing compounds that drive Maillard flavors. People constantly conflate them. Onions “caramelize” in the popular phrase, but the long, slow browning of onions involves both: the sugars in the onion do break down, but the amino acids present participate in Maillard chemistry too, and the resulting flavor is a blend of hundreds of compounds. The deeper lesson of cooking science is that flavor is not a single substance but an enormous, shifting mixture, and technique is largely about steering which reactions dominate. A great cook may know none of the chemistry by name and still have an expert’s intuitive grasp of its consequences, much as a sailor can read wind without having studied fluid dynamics.

Fermentation extends the theme into biology. Bread, beer, wine, cheese, yogurt, kimchi, soy sauce, miso, chocolate, and coffee all depend to some degree on microbial activity, usually in ways that humans discovered millennia before anyone knew microbes existed. Sourdough illustrates the ecology nicely. A starter is a stable community of wild yeasts and lactic acid bacteria living together in flour and water. The bacteria produce acids that lower the pH, which suppresses many competitors and gives sourdough its tang, while the yeasts produce carbon dioxide that leavens the loaf. The community is self-maintaining under regular feeding because the acidic, nutrient-rich environment favors precisely those organisms. A baker who feeds a starter is gardening a miniature ecosystem. Studies sampling starters from around the world have found that the specific microbial makeup varies with flour type, temperature, and feeding schedule far more than with geography, which undercuts the popular idea that a particular city’s air gives its bread a unique character, though the romance of the claim endures.

Let me wander next into architecture, and specifically the problem of the arch, because it is a lovely example of engineering by understanding forces rather than by material strength. Stone is strong in compression and weak in tension. A beam of stone spanning an opening bends under load, putting its underside in tension, and cracks. An arch converts the load into compressive forces that flow along the curve to the supports. The ideal shape for an arch carrying only its own weight is a catenary, the curve a hanging chain makes, turned upside down. A hanging chain is pure tension; invert it and you have pure compression. Robert Hooke recognized this in the seventeenth century, and the idea was famously used by Gaudí, who built hanging models of chains and weighted strings and then inverted them to design the structure of his church at the Colònia Güell. He was, in effect, computing with gravity.

The Gothic cathedral builders pushed the arch to astonishing heights using pointed arches, which direct more of the thrust downward, and flying buttresses, which carry the lateral thrust of the vault out to external piers. That freed the walls from bearing load, so they could be filled with glass. The great windows of Chartres and Sainte-Chapelle are, structurally, a consequence of a clever rearrangement of where forces go. And the builders did this without anything like modern stress analysis. They worked by geometric rules of proportion, accumulated experience, and, at times, catastrophic failure; Beauvais Cathedral’s choir vault, the tallest attempted, collapsed in 1284 and again in 1573 when the central tower fell. Each failure fed back into practice. There is a whole philosophy of technology packed into that history: knowledge often precedes theory, and theory later explains and extends what craft already discovered.

That pattern, craft leading and theory trailing, is repeated in the steam engine and thermodynamics. Engines were pumping water out of mines and driving factories decades before Sadi Carnot, in 1824, analyzed the limits of heat engines and laid the foundations for the second law. The science of thermodynamics was, in a sense, born from the effort to understand machines that already existed. Likewise, aircraft flew before aerodynamics was fully worked out, and the Wright brothers’ real innovation was less the engine than their systematic experimentation with wing shapes in a homemade wind tunnel and their solution to control through wing warping. They found that the published aerodynamic tables of the time were wrong, and they rebuilt the data themselves. I find that moving: two bicycle mechanics who did not trust received numbers and tested them directly.

Let me change the register again and talk about music, because the structure of scales is one of the oldest places where mathematics and perception meet. Take a vibrating string. Halve its length and the pitch rises an octave, a frequency ratio of two to one. Take two thirds of its length and the pitch rises a perfect fifth, a ratio of three to two. Pythagoras and his followers reportedly built scales by stacking fifths, and here you meet a famous snag. Stack twelve perfect fifths and you should return to the starting note several octaves higher, but the arithmetic does not close: twelve fifths overshoot seven octaves by a small amount, about a quarter of a semitone, called the Pythagorean comma. You cannot have all your fifths pure and also have the circle close. This single mismatch has driven centuries of tuning history. Meantone temperaments sacrificed some keys to make others sweet. Well temperaments distributed the discrepancy unevenly so that every key was usable but each had its own color. Equal temperament, which now dominates, divides the octave into twelve identical semitones, each with a frequency ratio of the twelfth root of two, making every key equally usable and every interval except the octave very slightly impure. A modern piano is a compromise, an arrangement in which no fifth is quite perfect, and we have simply grown used to the slight beating.

What’s interesting is how much of musical expressiveness lives in those deviations. String quartets and singers, who are not locked to fixed pitches, often drift toward purer intervals when sustaining chords and adjust on the fly, which is part of why a good a cappella group can sound uncannily resonant. Barbershop singers deliberately tune to just intonation to produce “ringing” chords, in which overtones from different voices align and reinforce each other, sometimes creating a phantom note above the chord. The perception of consonance has a physical basis in the alignment of harmonics. When two tones have frequencies in a simple ratio, their overtone series overlap and coincide, producing a smooth sound; when they don’t, nearby overtones interfere and produce the roughness we call dissonance. Helmholtz articulated this in the nineteenth century, and psychoacoustics has refined it since, though cultural learning clearly shapes what we find pleasing too. Studies of listeners from communities with little exposure to Western music have found that while preference for octaves and fifths shows up widely, the aversion to dissonance is notably weaker in some groups, suggesting that part of what we hear as “harsh” is acquired.

Thinking about acquired perception, let me move into language acquisition, which is a miracle that we mostly overlook because everyone does it. A child exposed to speech acquires, in a few years and without explicit instruction, an enormously complex system of sounds, words, and grammar. Newborns can distinguish phonetic contrasts from all the world’s languages, but by around ten to twelve months they begin to lose sensitivity to contrasts absent from their native language, a process called perceptual narrowing. A Japanese infant can initially tell “r” from “l”; by a year old, the ability fades unless the language needs it. The brain is pruning what isn’t used, optimizing for the language at hand. This early plasticity is one reason adult learners find accent so difficult: their auditory system has been tuned for decades to a particular set of categories.

The debate over how much of grammar is innate has been central to linguistics since Chomsky argued in the 1950s and 60s that the structure of language is too complex and the input too sparse for a child to learn it without built-in constraints, the “poverty of the stimulus” argument. Critics respond that children receive far richer input than the argument assumes, that statistical learning is powerful, and that general-purpose cognition may suffice. The case of Nicaraguan Sign Language is a striking natural experiment. In the late 1970s, deaf children brought together in new schools in Nicaragua, with no shared language, developed a signing system among themselves. The first cohort created a rudimentary gestural pidgin. Younger children who joined later and grew up with that input transformed it into a fuller language with consistent grammar, including spatial devices for marking relationships among referents. Each generation made it more systematic. That looks very much like children imposing structure on messy input, which supports the idea that there is something in how young minds learn that pushes toward grammatical regularity, whatever its precise nature.

Writing systems are a younger and more artificial layer on top. Writing was invented independently a small number of times, in Mesopotamia, Egypt, China, and Mesoamerica, and perhaps elsewhere. The earliest cuneiform tablets from Uruk, around 3300 BCE, were not literature but accounts: tallies of grain, beer, livestock, and labor. Writing began as bookkeeping. Only later did it expand to record names, then laws, then stories, then prayers. That origin says something sobering about civilization: the tool that carried the Epic of Gilgamesh, the Hebrew Bible, and every novel since was first a means of tracking who owed what to whom. Over time, the signs shifted from pictures to representing sounds, a move that made writing far more flexible, because a limited set of phonetic signs can represent any word in the language. The alphabet, apparently invented once by Semitic-speaking workers in Egyptian-controlled turquoise mines in the Sinai around 1900 to 1800 BCE and adapted from Egyptian hieroglyphs, is arguably the most successful information technology in history, because it reduces the burden of literacy to a few dozen symbols. Almost every alphabet now in use traces back to that one innovation, through Phoenician, Greek, Latin, Cyrillic, Hebrew, Arabic, and the many Indic scripts.

From the technology of writing, I’ll step into the technology of memory, because before writing, cultures preserved enormous bodies of knowledge orally, and the techniques were sophisticated. Milman Parry and Albert Lord studied Yugoslav oral poets in the 1930s and showed that they did not memorize epics word for word; they composed in performance using a stock of formulas, themes, and metrical patterns. The Homeric epithets, “rosy-fingered dawn,” “swift-footed Achilles,” were not decorative but functional, metrically convenient phrases that let a singer keep going while composing the next line. This changed how scholars think about the Iliad and Odyssey: not as fixed texts composed by a solitary author at a desk, but as the crystallization of a long performance tradition. The “method of loci,” or memory palace, which the Greeks and Romans used for rhetorical memorization, takes advantage of our strong spatial memory by placing vivid images at locations along a mentally walked route. Modern memory champions use essentially the same technique to memorize decks of cards and thousands of digits. It works because the brain evolved to remember places and routes far better than abstract lists.

And that brings me, rather naturally, to the question of what memory is, neurologically. The older picture of memory as a recording that you replay has been thoroughly undermined. Each act of recall is a reconstruction. When you remember an event, you reassemble it from distributed traces, and then the memory is briefly labile and gets re-stored, a process called reconsolidation, in which it can be altered by present context. This is why eyewitness testimony is shaky and why memories can be shaped by suggestion; Elizabeth Loftus’s experiments showed that a change in a single verb, asking how fast cars were going when they “smashed” into each other rather than “hit,” altered estimates of speed and even led participants to report broken glass that wasn’t there. Memory is less a library than a storyteller, one with a good deal of confidence and a mild tendency to improvise. Far from being a defect, this may reflect its purpose: memory evolved to guide future behavior, not to archive the past, so a flexible, gist-based, updateable system serves that purpose better than a perfect recording would.

There’s a rare and instructive case of an opposite condition, hyperthymesia, or highly superior autobiographical memory, in which people can recall nearly every day of their lives in detail. Some, though not all, report that the abundance is burdensome. And Luria’s famous patient, Solomon Shereshevsky, who could remember effectively unlimited lists, struggled to forget and had trouble grasping abstractions, as his mind was crowded with specifics. Forgetting, it seems, is a feature that enables generalization. To abstract, you must discard detail. A mind that kept everything would have difficulty seeing what any two things have in common.

That brings me to abstraction itself, and to one of the most beautiful abstractions in mathematics: the concept of a group. A group is a set with an operation satisfying a few simple axioms: closure, associativity, an identity element, and inverses. That’s it. Yet this tiny definition captures symmetry in its full generality. The symmetries of a square, rotations by ninety degrees and reflections, form a group. So do the integers under addition, the rotations of a sphere, the permutations of a deck of cards, and the transformations that leave the laws of physics unchanged. Emmy Noether proved in 1915 that every continuous symmetry of a physical system’s laws corresponds to a conserved quantity: invariance under shifts in time gives conservation of energy, invariance under shifts in space gives conservation of momentum, invariance under rotation gives conservation of angular momentum. This is one of the deepest results in physics, and it’s a statement about groups. The conservation laws that seem like separate empirical facts are consequences of the underlying symmetries of nature.

Galois, working in the early nineteenth century, used groups to answer a question that had stood for centuries: why can the quadratic, cubic, and quartic equations be solved with formulas using only arithmetic operations and roots, while the general quintic cannot? He associated to each polynomial a group of permutations of its roots and showed that solvability by radicals corresponds to a structural property of that group, now called solvability. The symmetric group on five elements lacks that property. So there is no general formula for the quintic, and the reason is a feature of the symmetries of five objects. Galois died at twenty, after a duel in 1832, and the story that he wrote out his results frantically the night before is probably embellished, but the work he left reshaped algebra. The point I’d stress is the power of the move: instead of studying the objects, study the structure of their symmetries, and let the structure answer questions the objects alone could not.

I want to extend that thought into something I find slightly dizzying, which is category theory, sometimes affectionately called abstract nonsense. Where group theory abstracts symmetry, category theory abstracts the relationships among mathematical structures themselves. A category consists of objects and arrows between them, with a rule for composing arrows. Sets and functions form a category. Groups and homomorphisms form another. Topological spaces and continuous maps form another. The insight is that many constructions across different areas of mathematics, products, quotients, free objects, share a common pattern that can be described purely in terms of arrows, without reference to what’s inside the objects. Functors map between categories, preserving structure, and natural transformations map between functors. The ambition is to see the unity beneath the diversity of mathematics, and it has been startlingly successful; it also found its way into programming language theory, where the structure of monads in functional programming comes straight from category theory. Skeptics find it too rarefied, but proponents argue that the right abstraction makes hard problems nearly trivial, as when the Yoneda lemma tells you that an object is completely determined by its web of relationships to all other objects. There is a philosophical resonance there: to know what a thing is, look at how it relates to everything else.

Relational thinking appears, unexpectedly, in the study of cities. Jane Jacobs argued in The Death and Life of Great American Cities, in 1961, that the vitality of a neighborhood comes from the density and mixture of its uses: short blocks, buildings of varied age, a blend of residences and shops and offices so that people are on the street at different hours, and the informal surveillance that comes from “eyes on the street.” She was reacting to the urban renewal programs of her day, which bulldozed dense neighborhoods and replaced them with towers in parks, in line with the ideals of Le Corbusier. Her critique was essentially that planners had treated the city as a problem of simple order, when it is a problem of organized complexity, a phrase she borrowed from Warren Weaver. A city is a system with enormous numbers of interacting parts that give rise to patterns nobody designed. Later quantitative work by Geoffrey West, Luis Bettencourt, and colleagues found striking scaling laws: as a city’s population doubles, things like wages, patents, and crime rise by about fifteen percent more than proportionally, while infrastructure like road length and gas stations rise by about fifteen percent less. Cities show superlinear scaling for social outputs and sublinear scaling for physical infrastructure. The result suggests that cities are, in a sense, engines of interaction, and that bigger ones amplify the rate at which people exchange ideas, goods, and also diseases.

Let me turn from the city to the single most important infrastructure of all, which is the plumbing of water, because I think it’s underappreciated how recent clean water is. Before the nineteenth century, cities were deadly places; urban death rates exceeded birth rates, and cities grew only through continuous migration from the countryside. The cholera outbreak in Soho, London, in 1854 became a landmark in epidemiology when John Snow mapped cases and traced them to a single contaminated pump on Broad Street. Snow had argued, against the prevailing miasma theory that disease spread through foul air, that cholera was waterborne. His map is often presented as the founding moment of modern epidemiology, though the full story is more complicated and includes the contributions of a local clergyman, Henry Whitehead, who helped identify the index case and the source of contamination. After the “Great Stink” of 1858, when the Thames became so foul that Parliament was forced to act, Joseph Bazalgette built London’s vast sewer system, which still functions in large part today. Chlorination of drinking water, first used continuously in the early twentieth century, drove down typhoid and cholera drastically. By many estimates, clean water and sanitation account for a large fraction of the gain in life expectancy in industrialized countries over the past two centuries, arguably rivaling or exceeding the contribution of any single medical treatment. It’s a piece of infrastructure so successful that it has become invisible, and invisible infrastructure is the kind we tend to neglect until it fails.

Which leads me to a theme I want to dwell on: the economics and psychology of invisible success. Prevention is a thankless business because its achievements are non-events. Nobody celebrates the pandemic that didn’t happen, the bridge that didn’t collapse, the financial crisis averted by quiet regulation. The Y2K bug is a classic example. Enormous effort went into fixing date-handling code in the late 1990s, and when the new year arrived with no catastrophe, many people concluded that the threat had been overblown, rather than that the remediation had worked. The reality, as far as can be reconstructed, is somewhere in between: some of the alarm was exaggerated, but real failures did occur in systems that were not fixed, and the countries that spent little on preparation did not suffer visibly worse outcomes in every case, which muddies the lesson. That ambiguity is typical. When prevention works, the counterfactual is unobservable, and so credit and blame are distributed badly. Political and organizational systems therefore tend to reward visible rescue over quiet prevention, the firefighter over the fire inspector. Societies that manage to invest in preventing rare, high-consequence events are working against a deep grain in human cognition.

Rare events and our handling of probability deserve their own examination, since this is where intuition fails most reliably. Kahneman and Tversky catalogued the ways people misjudge risk: we overweight vivid, easily recalled events, we neglect base rates, and we treat small probabilities inconsistently, sometimes ignoring them entirely and sometimes inflating them enormously. Consider the base-rate problem in medical testing. Suppose a disease affects one in a thousand people, and a test has a ninety-nine percent true positive rate and a one percent false positive rate. If you test positive, what’s the chance you have the disease? Most people say around ninety-nine percent. The actual answer is under ten percent. Out of a thousand people, one has the disease and almost certainly tests positive, but about ten of the nine hundred ninety-nine healthy people also test positive, so roughly one in eleven positives is genuine. The error comes from ignoring how rare the condition is to begin with. Physicians make this mistake too, and studies by Gerd Gigerenzer show that presenting the problem in natural frequencies, “out of a thousand people,” instead of probabilities dramatically improves reasoning. The lesson is that the format of information matters as much as its content. A clearer representation can turn an error-prone judgment into an easy one without any change in the underlying intelligence of the person.

That insight, that representation shapes reasoning, is as true of mathematics as of medicine. The invention of Arabic numerals with positional notation and a zero made arithmetic dramatically easier than it had been with Roman numerals. Try multiplying CCLXIV by XLVII and you’ll feel the difference. Algebraic notation, developed gradually from the work of Viète, Descartes, and others, made it possible to manipulate relationships symbolically instead of through verbal description. Leibniz’s notation for calculus, with its dx and dy, suggests operations that turn out to be valid in useful ways, while Newton’s dot notation was more cumbersome, and the continental mathematicians who adopted Leibniz’s notation progressed more rapidly in the eighteenth century. A good notation does some of the thinking for you. This is one reason that fields invest so much in their formalisms, and why the invention of a new notation can be a major intellectual event. Feynman diagrams, introduced in the late 1940s, turned monstrous calculations in quantum field theory into drawings with simple rules, and made the computations tractable for a generation of physicists.

Let me venture into a very different area now, and talk about the strange and wonderful behavior of flocks, swarms, and other collective phenomena. A murmuration of starlings, tens of thousands of birds twisting in coordinated waves, looks choreographed, but there is no leader. Craig Reynolds showed in 1986 with his “boids” simulation that three simple local rules generate convincing flocking: separation, avoid crowding neighbors; alignment, steer toward the average heading of neighbors; and cohesion, steer toward the average position of neighbors. Empirical work on real starlings by the STARFLAG project in Rome found that each bird tracks its nearest six or seven neighbors regardless of distance, a topological rather than metric interaction, which lets the flock maintain cohesion even as density changes. Information propagates through the flock rapidly, so that a turn begun at the edge sweeps across the whole in a fraction of a second. Physicists have noted that such flocks display features of systems poised near a critical point, where correlations extend across the whole group, so that a small perturbation can have large effects, a property that is useful for rapid collective response to predators.

Ant colonies show a different kind of collective intelligence, built on chemical signals. Foraging ants lay pheromone trails, and trails to richer sources get reinforced because more ants traverse them and deposit more pheromone, while pheromone evaporates, so stale trails fade. This positive feedback balanced by decay allows the colony to converge on short, efficient routes without any ant knowing the map. Marco Dorigo turned this into a family of optimization algorithms, ant colony optimization, used for routing problems. Bee colonies choose nest sites through a kind of democratic process: scouts return and perform waggle dances advertising sites, with dance duration correlating with perceived site quality, and other bees are recruited in proportion. When enough bees converge on one site, a quorum is reached and the swarm moves. Thomas Seeley’s studies of honeybee decision-making are a treat to read, and he argues that the swarm’s mechanisms, such as independence of scouts, honest signaling, and the use of cross-inhibition to break deadlocks, hold lessons for human committees. A bee swarm is a decision-making body that avoids many of the pathologies of human groups, such as dominant individuals and groupthink, by structuring how information flows.

I’ll bring that to bear on the question of how groups of people make good or bad decisions, since there’s a literature on that I find fascinating. The “wisdom of crowds” effect, popularized by James Surowiecki, holds that aggregating many independent estimates often beats most individual experts. Francis Galton observed in 1906 that the median of guesses for the weight of an ox at a country fair was within about one percent of the true value. The effect depends on independence and diversity of errors; if people influence one another, errors become correlated and the crowd’s accuracy collapses. This is why information cascades matter: when each person watches what the previous people did and follows, the crowd can lock onto a wrong answer, because later participants discount their own private information. Financial bubbles are partly explained this way. The design lesson, in forecasting tournaments such as Philip Tetlock’s Good Judgment Project, is that aggregation methods that preserve independence, collect numerical probabilities, and weight forecasters by track record outperform both individual experts and naive averaging. Tetlock’s earlier large study of expert political judgment found that the average pundit barely beat chance, and that the forecasters who did best were “foxes,” who drew on many perspectives and updated readily, rather than “hedgehogs” committed to one grand theory. The temperament of good judgment is, it turns out, a kind of humble flexibility.

That humility connects to the philosophy of science, which I’d like to now explore a bit, starting with the problem of induction. Hume pointed out that no amount of past observation logically guarantees that the future will resemble the past; the sun has risen every day so far, but that doesn’t deductively entail that it will rise tomorrow. Our reliance on induction is a habit, not a proof, and attempts to justify it by appeal to past successes are circular. Popper’s response was to shift the emphasis from confirmation to falsification: scientific theories cannot be proven, but they can be refuted, and what marks a theory as scientific is that it makes risky predictions that could in principle fail. Einstein’s general relativity staked its reputation on the bending of starlight, observed during the 1919 eclipse, a prediction that might have come out otherwise. Popper contrasted this with theories like certain forms of psychoanalysis or Marxism, which he felt could accommodate any evidence by flexible reinterpretation. Later philosophers complicated the picture. Kuhn argued that science proceeds through periods of “normal science” within a paradigm, punctuated by revolutions when anomalies accumulate and a new paradigm displaces the old, and that scientists do not simply discard theories at the first counterexample, which would be unproductive, since all theories face apparent anomalies at all times. Lakatos proposed that research programmes have a hard core protected by auxiliary hypotheses, and are judged as progressive if they predict novel facts, degenerating if they merely patch problems after the fact. The real practice of science sits somewhere among these views, and the disagreement itself shows that the demarcation of good science from bad is subtle.

Observe how the neptune-uranus episode illustrates this nuance. In the 1840s, Uranus was found to deviate from its predicted orbit. One response would have been to abandon Newtonian gravity. Instead, Le Verrier and Adams hypothesized an unseen planet and calculated where it should be; Galle found Neptune within about a degree of the predicted position in 1846. A triumph of the auxiliary hypothesis. But when Mercury’s orbit showed an anomalous precession, Le Verrier tried the same move, postulating a planet, Vulcan, inside Mercury’s orbit, and it was never found. The anomaly was eventually explained by general relativity in 1915. The same strategy succeeded in one case and failed in another, and there was no way to know in advance which would occur. That is a good illustration of why methodology cannot be reduced to a formula; judgment is irreducible.

From the problem of knowing, I’d like to shift to the more playful problem of games, and specifically the phenomenon that simple rules can generate unlimited complexity. Conway’s Game of Life takes place on a grid where each cell is alive or dead. A live cell with two or three live neighbors survives; a dead cell with exactly three live neighbors becomes alive; all others die or stay dead. From this, patterns arise: still lifes, oscillators, “gliders” that travel across the grid. Enthusiasts have built a Turing-complete computer inside Life, meaning that in principle any computation can be carried out by an arrangement of cells obeying those few rules. They have even built Life inside Life, a pattern that simulates the Game of Life at a larger scale. The lesson, one echoed by Wolfram’s work on elementary cellular automata, is that complex behavior doesn’t require complex rules. Rule 110, a one-dimensional automaton with a trivially small rule table, was proven by Matthew Cook to be Turing-complete. The gap between the simplicity of a system’s specification and the complexity of its behavior can be vast, which has implications for how we think about nature: the intricacy of living things may not require equally intricate instructions.

Emergence, the appearance of higher-level patterns not obvious from the components, is a recurring phrase, and I want to be careful with it because it can be used lazily as a label for what we don’t understand. A useful distinction is between weak emergence, where the higher-level behavior is in principle derivable from the lower-level rules even if only by simulation, and strong emergence, where it is claimed to be fundamentally irreducible. Most scientists accept weak emergence readily: the wetness of water, the hardness of a crystal, the flocking of birds are all consequences of lower-level interactions. Strong emergence is more controversial, and consciousness is the usual candidate people invoke. I’m not going to pretend to resolve that, but it is worth saying that the “hard problem” as framed by David Chalmers is the claim that explaining all the functions of the brain, how it discriminates stimuli, integrates information, reports on internal states, would still leave unexplained why any of this is accompanied by subjective experience. Some philosophers, like Daniel Dennett, reject the framing, arguing that once you explain all the functions, there is nothing left over; the sense that something remains is an illusion produced by how the brain models itself. Others, like Galen Strawson, take the opposite view and argue for panpsychism, the idea that experience is a fundamental feature of matter. Integrated information theory, proposed by Giulio Tononi, tries to quantify consciousness mathematically as the degree to which a system’s parts form an irreducible whole, with implications that some find compelling and others find alarming, such as the claim that certain simple but highly integrated systems would be conscious. And global workspace theory, associated with Bernard Baars and Stanislas Dehaene, proposes that consciousness corresponds to the broadcast of information across a brain-wide workspace, making it available to many specialized processes at once. These are serious, mutually incompatible research programs, and the field has few decisive experiments, though adversarial collaborations, in which proponents of rival theories agree in advance on predictions, have begun to put pressure on them.

I’ll leave consciousness there, with its open questions intact, and head somewhere more earthbound: the deep history of agriculture, because few transitions altered human life more thoroughly. Around eleven thousand years ago, in several regions independently, people began domesticating plants and animals: wheat and barley and sheep and goats in the Fertile Crescent, rice in the Yangtze valley, millet in northern China, maize in Mesoamerica, potatoes in the Andes. It was not a sudden revolution but a slow process lasting thousands of years, and the early consequences were not obviously beneficial. Skeletons of early farmers show shorter stature, more dental disease, and signs of nutritional stress compared with their forager predecessors. Jared Diamond called agriculture “the worst mistake in the history of the human race,” a provocative formulation that overstates the case but points at a real puzzle: why would people adopt a way of life that seems, at least initially, to have made individuals worse off? Proposed answers include climate changes after the last ice age that favored cultivation, population pressure that made foraging insufficient, and a ratchet effect, in which once a population grew large on farmed food, it could not go back, since the land could no longer support it by foraging. Farming supports far more people per acre, so farmers outbred and displaced foragers even if each farmer lived less well.

Domestication reshaped the domesticated species in recognizable ways, sometimes called the domestication syndrome. Wild wheat shatters at maturity to scatter its seeds; domesticated wheat has a tough rachis that holds the seeds on the stalk, which is convenient for harvesters, and that trait was selected, probably unconsciously, simply because the seeds that stayed on the plant were the ones gathered and replanted. Domesticated animals tend to show floppy ears, smaller brains, spotted coats, and juvenile behavior, features that appear across mammals and have been tied to changes in neural crest cells during development. The Russian fox farm experiment, begun by Dmitri Belyaev in 1959, selected silver foxes solely for tameness, and within a few generations produced foxes with floppy ears, curly tails, and piebald coats, along with dog-like behavior such as seeking human attention. Selecting for one behavioral trait dragged a whole suite of physical traits along with it. That finding supports the idea that domestication traits share a developmental basis, and it’s a vivid demonstration of how deeply connected seemingly separate features of an organism can be.

Dogs themselves are a puzzle, since they were domesticated from wolves well before agriculture, possibly more than fifteen thousand years ago, and the location and timing remain disputed. One appealing hypothesis is that some wolves began scavenging around human camps, and the bolder, less fearful ones got more food and left more offspring, a kind of self-domestication, with deliberate human breeding coming later. Whatever the details, dogs evolved the ability to read human gestures, such as pointing, better than chimpanzees can, even though chimps are much closer to us. Studies suggest puppies show this skill early, indicating it is not merely learned. The sclera, the white of the human eye, may have evolved in part to make gaze direction easy to see and thereby to support cooperation, and dogs seem to follow our gaze. There’s a coevolution there between two species attending to each other, one of the oldest partnerships we have.

Let me use that to move into the broader theme of coevolution, which gives some of the loveliest examples in natural history. Flowering plants and their pollinators have shaped each other for over a hundred million years. Darwin, looking at a Madagascan orchid with a foot-long nectar spur, predicted in 1862 that a moth with a proboscis of matching length must exist to pollinate it. Skeptics scoffed. Decades after his death, in 1903, a hawkmoth with an extraordinarily long proboscis was discovered in Madagascar, and it was named Xanthopan morganii praedicta, the “predicted” subspecies; its role in pollinating the orchid was not directly observed until 1992. The prediction was spectacular, though the underlying logic was simple: a structure that seems absurdly specialized implies a partner shaped by the same pressures.

Arms races are the antagonistic flavor of coevolution. Predators evolve to catch prey, prey evolve to escape, and each adaptation raises the bar for the other. The Red Queen hypothesis, named for the character in Through the Looking-Glass who must run to stay in place, proposes that species must constantly evolve just to keep up with their competitors and parasites, so that no one ever “wins.” Leigh Van Valen proposed it in 1973 after noticing that the extinction probability of a taxonomic group appears roughly constant over its lifetime, regardless of how long it has existed, which suggests that survival does not get easier with age. Some biologists have extended the idea to explain sexual reproduction: sex shuffles genes, producing novel combinations that parasites, which evolve quickly and specialize on common host genotypes, haven’t yet adapted to. Experimental evidence in snails and in various plants supports a role for parasites in maintaining sexual reproduction, though the question is still debated, because sex carries a large cost, the so-called twofold cost, and explaining why it is so widespread has occupied evolutionary theorists for decades.

Parasites themselves are among the most intriguing organisms, and some of their strategies read like science fiction. Toxoplasma gondii, a protozoan, completes its sexual cycle only in cats, but can infect almost any warm-blooded animal. In rodents, infection has been reported to reduce their innate fear of cat odor, which would make them likelier to be eaten and thus return the parasite to its definitive host. Whether it has comparable effects on human behavior is controversial and the evidence is weak and often confounded, though a large fraction of humans worldwide carry the parasite. The lancet liver fluke makes ants climb to the tips of grass blades and clamp down, where grazing sheep might eat them. The fungus Ophiocordyceps controls carpenter ants, inducing them to bite into a leaf vein at a precise height and time of day before the fungus sprouts from the corpse. These cases challenge any simple picture of an organism as an autonomous agent, since behavior can be shaped by another species’ genes, a concept Richard Dawkins called the extended phenotype.

Dawkins’s broader contribution, the gene’s-eye view of evolution, reframes natural selection as the differential survival of replicating units, with organisms as vehicles. It’s an elegant way to understand otherwise puzzling things, like altruism toward relatives. W. D. Hamilton’s rule, formalized in 1964, states that a gene for altruism can spread if the benefit to the recipient, weighted by genetic relatedness, exceeds the cost to the actor: rB > C. The sterile worker castes of ants, bees, and wasps seemed paradoxical for Darwin, who regarded them as a serious difficulty for his theory. Haplodiploidy, the genetic system in which females develop from fertilized eggs and males from unfertilized ones, makes sisters unusually closely related, three-quarters on average, which Hamilton suggested favored the evolution of eusociality. The story has since been complicated: termites are not haplodiploid yet are eusocial, and the debate over the relative roles of kin selection and group-level selection became heated, notably when E. O. Wilson, once a champion of kin selection, argued late in his career against its centrality. The dispute illustrates that even settled-seeming frameworks keep generating live controversy, which is a sign of a healthy science rather than a failing one.

Let me carry the thread of altruism and kin into the cultural domain, where things become messier, and consider the origin of human cooperation at scale. We cooperate with non-relatives, with strangers, even anonymously. Some explanations invoke reciprocity and reputation, as discussed earlier. Others invoke group selection operating on cultural traits: groups with norms that foster cooperation outcompete those without. Joseph Henrich has argued that cultural evolution, the accumulation of learned practices over generations, is the key to human success, and that we are a “cultural species” whose brains evolved to learn from others. His examples are striking. In many hunter-gatherer groups, elaborate food-processing techniques, such as the multi-step detoxification of bitter manioc, are essential to survival, but no individual understands why each step is needed. The knowledge is embedded in tradition. European explorers who stranded themselves in unfamiliar environments, such as the Burke and Wills expedition in Australia, sometimes starved while surrounded by foods that local Aboriginal people subsisted on, because they lacked the accumulated cultural know-how, for example about processing nardoo, which, if prepared improperly, causes a thiamine deficiency. Culture, on this view, is a form of collective intelligence larger than any individual head, and human brains evolved to plug into it. That reframes intelligence from an individual property to a social and historical one: what makes us smart is not raw computing power but the capacity to absorb and transmit a vast inheritance of learned technique.

Population size appears to matter to the maintenance of that inheritance. The Tasmanian case is often cited: after rising seas cut Tasmania off from mainland Australia roughly ten thousand years ago, the isolated population of a few thousand people seems to have lost some technologies, such as bone tools and fishing, that persisted on the mainland, though this interpretation has been contested by archaeologists who question the evidence for the losses. If the model is correct, it suggests that cultural complexity depends on the size of the interacting network, since skills must be relearned by each generation, and small populations suffer from random loss and from fewer innovators. It resonates with the city scaling results from earlier: bigger interaction networks sustain and generate more.

I’ll let that settle, and turn toward the sea, because the oceans hold some of the strangest facts available. The deep ocean, below about a thousand meters, is the largest habitat on Earth by volume, dark, cold, and under immense pressure, and yet it teems with life adapted to it. Until 1977 it was assumed that deep-sea life depended entirely on the slow rain of organic debris from the sunlit surface, “marine snow.” Then researchers in the submersible Alvin discovered hydrothermal vents on the Galápagos Rift, with dense communities of giant tube worms, clams, and mussels living where there was no sunlight at all. The base of that food web is chemosynthesis: bacteria oxidize hydrogen sulfide emerging from the vents to harvest energy, and the tube worms, which have no mouth or gut as adults, host those bacteria in a specialized organ and live off what they produce. It was a stunning revelation that an entire ecosystem could run on chemical energy from Earth’s interior rather than sunlight, and it transformed thinking about the origin of life and the possibility of life elsewhere, such as in the subsurface oceans of Europa and Enceladus, where hydrothermal activity may occur. Some researchers now suspect that life on Earth may have begun at such vents, where the gradients of pH and temperature and the mineral surfaces could have provided the conditions for early metabolism.

The ocean is also where one of the great ongoing planetary processes takes place: the thermohaline circulation, sometimes called the global conveyor belt. Differences in temperature and salinity drive a slow overturning of ocean water. Cold, salty water sinks in the North Atlantic and near Antarctica, flows along the deep ocean floor, and eventually upwells elsewhere, a journey that takes on the order of a thousand years. Warm surface water flowing north in the Atlantic, the Gulf Stream and its extension, carries heat to northwestern Europe, which helps make its climate milder than other regions at similar latitudes. A prominent concern in climate science is that melting ice and increased freshwater input could weaken this circulation, the Atlantic Meridional Overturning Circulation, with significant regional climate consequences. Observations suggest some weakening, though the magnitude and the risk of abrupt collapse are matters of active research and substantial uncertainty. Paleoclimate records show that the circulation has shifted abruptly in the past, associated with rapid climate swings during the last ice age, called Dansgaard–Oeschger events, which is why the possibility is taken seriously.

Ice cores are the source of much of what we know about those past swings, and they are marvels of their own. Drilled from the Greenland and Antarctic ice sheets, they preserve annual layers of snow, trapping tiny bubbles of ancient air. By measuring the composition of that trapped air, scientists can reconstruct atmospheric carbon dioxide and methane going back roughly 800,000 years in the Antarctic Dome C core, and the isotopic ratios of oxygen in the ice itself serve as a proxy for temperature. The records show a close correlation between greenhouse gas levels and temperature across glacial cycles, with the glacial-interglacial rhythm paced by slow changes in Earth’s orbit, the Milankovitch cycles: variations in orbital eccentricity, axial tilt, and precession over tens of thousands of years that modulate the distribution of sunlight. The orbital changes are small and don’t by themselves account for the size of the temperature swings; feedbacks involving ice albedo, carbon dioxide release from the oceans, and other processes amplify them. The modern atmospheric concentration of carbon dioxide, now above 420 parts per million, is well above anything in that 800,000-year record, which ranged between roughly 180 and 300 parts per million.

I could go on about the climate system, but let me pull the lens upward to astronomy, to a different kind of archive: light. Looking at a distant galaxy means looking into the past, since light takes time to arrive. The light we see from the Andromeda galaxy left it about 2.5 million years ago. The most distant galaxies observed by the James Webb Space Telescope are seen as they were within a few hundred million years of the Big Bang, and their surprising brightness and apparent maturity have prompted researchers to revisit models of early galaxy formation, though many of the initial claims about extremely massive early galaxies have been tempered by follow-up observations suggesting that some were overestimated. The cosmic microwave background, the oldest light we can observe, was released about 380,000 years after the Big Bang, when the universe cooled enough for electrons and protons to combine into neutral atoms and photons could travel freely. It’s a snapshot of the infant universe, remarkably uniform at one part in a hundred thousand, and the tiny fluctuations imprinted on it are the seeds of all later structure: galaxies, clusters, and the cosmic web. The statistical pattern of those fluctuations, measured by the WMAP and Planck satellites, constrains the age, geometry, and composition of the universe with high precision, giving an age of about 13.8 billion years and a composition of roughly five percent ordinary matter, about twenty-seven percent dark matter, and the rest dark energy.

Dark matter and dark energy are labels for ignorance backed by evidence. Dark matter was inferred from galaxy rotation curves, work by Vera Rubin and Kent Ford in the 1970s showing that stars at the outskirts of galaxies orbit as fast as those near the center, implying much more mass than visible matter can account for, and from earlier observations by Fritz Zwicky of galaxy clusters in the 1930s. Gravitational lensing, the bending of light by mass, and the dynamics of the Bullet Cluster collision provide further evidence that most of the mass in clusters is not in the visible gas. Decades of direct detection experiments have not yet found the particles responsible, and alternatives like modified Newtonian dynamics, MOND, can explain some galactic-scale phenomena but struggle with cluster-scale and cosmological observations. Dark energy, the driver of the accelerating expansion discovered in 1998 through observations of distant Type Ia supernovae, is even more mysterious. The simplest explanation is a cosmological constant, a constant energy density of empty space, but quantum field theory naively predicts a vacuum energy larger than the observed value by some one hundred and twenty orders of magnitude, a mismatch often called the worst prediction in physics. Recent results from the Dark Energy Spectroscopic Instrument have hinted that dark energy might evolve over time rather than being exactly constant, though the statistical significance is still modest and the community is waiting for more data.

That uncertainty is, I think, a good place to reflect on a notion that has surfaced several times in these meanderings, which is the productive role of the unresolved. Fields advance by the pressure of anomalies: the precession of Mercury, the blackbody spectrum, the constancy of the speed of light, the flat rotation curves, the accelerating expansion. At any moment, a science contains a frontier of things that don’t fit, and the temperament required to work there is the ability to hold an open question without prematurely closing it, neither dismissing the anomaly as noise nor leaping to a grand overthrow of the whole framework. History shows both errors. Sometimes anomalies prove to be instrument errors, as in the 2011 report of faster-than-light neutrinos by the OPERA experiment, which turned out to be caused by a loose fiber optic cable and a clock issue. Sometimes they reveal new physics. The discipline lies in distinguishing, which usually requires patience, independent replication, and a willingness to be wrong in public.

I’d like to close this particular stretch with a few words about patience, because it connects to something humane beneath all the topics I’ve circled. The long projects, cathedral building, ice core drilling, the construction of particle accelerators, the compilation of dictionaries like the Oxford English Dictionary, which took some seventy years from inception to first full publication, depend on people committing to work they won’t live to see completed. Cathedral builders knew they’d never see the finished spire. There’s a particular dignity in that, a form of intergenerational cooperation that quietly underlies our greatest achievements. The seed vault in Svalbard, the Long Now Foundation’s ten-thousand-year clock being built inside a mountain in Texas, and projects like the long-term stewardship of nuclear waste are modern attempts to extend our planning horizon beyond a human lifespan, and they raise the odd question of how to communicate with people far in the future. The U.S. Department of Energy once commissioned studies on how to mark a nuclear waste repository so as to warn future civilizations for ten thousand years, considering languages that might not exist, symbols that might change meaning, and even the notion of landscapes of thorns designed to evoke danger. The challenge exposes how contingent our semiotics are: a skull and crossbones means poison to us, but its meaning is cultural, and it has even been used as a symbol of treasure or of pirate pride. Across millennia, no sign is guaranteed to carry its intended meaning, which makes the problem a fascinating test of what is universal in human communication, if anything is.

I find that I’m happy to keep going, so I will, and the next thread I’ll pick up is the odd and beautiful history of numbers that don’t behave, beginning with the discovery that the square root of two cannot be written as a ratio of whole numbers.

The Pythagoreans reportedly believed that everything could be expressed through whole numbers and their ratios, a creed that was part mathematics and part religion. Then someone, tradition names Hippasus of Metapontum though the details are legend, showed that the diagonal of a unit square has no such expression. The proof is short enough to carry in your head. Suppose the square root of two equals a over b in lowest terms. Then a squared equals two b squared, so a squared is even, so a is even, so a squared is divisible by four, which means b squared is even, so b is even, contradicting lowest terms. The story that Hippasus was drowned for revealing this is almost certainly a later embellishment, but it captures how disturbing the result must have felt: the geometry of the most basic shape contained a quantity that arithmetic could not capture. Greek mathematics responded by retreating into geometry, treating magnitudes as lengths rather than numbers, a decision that shaped the field for two thousand years. Euclid’s Elements handles irrationals through the theory of proportion in Book V, attributed to Eudoxus, which cleverly defines when two ratios are equal without needing either to be a number, comparing them against all rational ratios from above and below. That is essentially the idea behind Dedekind’s construction of the real numbers in 1872, which defines a real number as a cut dividing the rationals into those below and those above it. It took roughly two thousand years for the core of an idea in Eudoxus to be made into a foundation of analysis.

The real numbers turned out to be far stranger than anyone expected. Cantor showed in the 1870s and 80s that the infinite comes in different sizes. The natural numbers and the rational numbers can be put into one-to-one correspondence, so they have the same cardinality, called countable infinity, even though the rationals seem so much more numerous. But the real numbers cannot be listed. The diagonal argument is again the engine: suppose you had a complete list of real numbers between zero and one, written as decimal expansions. Construct a new number whose first digit differs from the first digit of the first number, whose second digit differs from the second digit of the second number, and so on down the diagonal. The new number differs from every number on the list in at least one place, so it isn’t on the list, contradicting completeness. So there are strictly more real numbers than natural numbers. Cantor then asked whether there is any size of infinity strictly between the two, the continuum hypothesis, and the eventual answer was one of the most surprising in the history of mathematics: Gödel showed in 1940 that it cannot be disproved from the standard axioms of set theory, and Paul Cohen showed in 1963 that it cannot be proved from them either. It is independent. Mathematicians can adopt it or its negation and develop consistent mathematics in either case. This undermines a naive Platonism in which every mathematical statement has a determinate truth value waiting to be discovered, or at least it forces the Platonist to say that our axioms underdetermine the truth, and sparks the question of whether new axioms, perhaps motivated by large cardinals or by other principles, might settle the question. Hugh Woodin has argued for programs in which the continuum hypothesis comes out false, with the value of the continuum being the second uncountable cardinal, while others doubt the question has a determinate answer at all.

Cantor himself suffered for these ideas, facing fierce opposition from Leopold Kronecker, who reportedly dismissed his work as corrupting youth and insisted that God made the integers and all else is the work of man. Cantor experienced recurring episodes of depression, and while historians debate how much the controversy contributed, it’s a reminder that radical ideas carry personal costs. Hilbert, defending Cantor’s legacy, declared that no one shall expel us from the paradise that Cantor has created. And yet the paradise had its serpents: Russell’s paradox, discovered in 1901, showed that naive set theory, which allows the set of all sets that do not contain themselves, is inconsistent, since that set would contain itself if and only if it didn’t. The crisis spurred axiomatic set theory, Zermelo and Fraenkel’s system, in which sets are built up in stages and the troublesome collections are excluded. Russell and Whitehead wrote the Principia Mathematica to derive mathematics from pure logic, a monument of painstaking symbolism in which it takes hundreds of pages to prove that one plus one equals two, and Gödel’s incompleteness theorems then showed that no such program could be complete. The foundations of mathematics are in a sense permanently unfinished, and yet mathematics continues, unbothered, building on the foundations as they stand, much as engineers build bridges without waiting for a final resolution of the philosophy of physics.

Let me bring in something that has the same flavor of surprising order emerging from apparent randomness: the primes. They are defined by a simple multiplicative property, yet their distribution appears erratic. Euclid proved around 300 BCE that there are infinitely many, with an argument so economical it’s hard to improve on: assume a finite list, multiply them all and add one, and the result must have a prime factor not on the list. And yet the large-scale distribution is orderly. The prime number theorem, proved independently by Hadamard and de la Vallée Poussin in 1896, says that the number of primes up to x is approximately x divided by the natural logarithm of x. Gauss had guessed this as a teenager from tables. The deviations from this smooth estimate are governed by the zeros of the Riemann zeta function, and the Riemann hypothesis, which asserts that all the nontrivial zeros lie on a single vertical line in the complex plane, would imply the tightest possible control on those deviations. It remains unproven after more than a century and a half, despite enormous numerical verification of the first trillions of zeros. Montgomery observed in the 1970s that the statistical spacing of the zeros resembles the spacing of eigenvalues of large random matrices, the same statistics that describe energy levels of heavy atomic nuclei, a connection that surprised both mathematicians and physicists and hints at some hidden spectral structure behind the primes. Whether there’s a quantum system whose energy levels are the zeros, as the Hilbert–Pólya conjecture suggests, is unknown.

There’s a human story in primes as well. Cryptography, the practice of securing communication, rests heavily on the asymmetry between multiplying primes, which is easy, and factoring their product, which appears to be hard. RSA encryption, published in 1977 by Rivest, Shamir, and Adleman, builds on this, and it was a case of mathematics regarded as the purest of pure turning out to underpin global commerce. G. H. Hardy famously boasted in A Mathematician’s Apology that number theory was useless, and that this was a virtue. He was wrong about the first part, though it’s hard to hold it against him, since nobody in 1940 could have foreseen the internet. Interestingly, British intelligence at GCHQ had developed the equivalent of public-key cryptography earlier, in the early 1970s, through Clifford Cocks, James Ellis, and Malcolm Williamson, but it was classified and remained secret until 1997. The discovery was made twice, once in secret and once in public, and the public one got the credit and the patents.

Quantum computing threatens that arrangement. Peter Shor showed in 1994 that a sufficiently large quantum computer could factor large numbers efficiently, which would break RSA. Building such a machine is an enormous engineering challenge, because qubits are fragile and errors accumulate, so practical factoring of cryptographically relevant numbers would require error-corrected logical qubits built from many thousands of physical ones, and current machines remain far from that scale. Meanwhile, cryptographers have been developing post-quantum schemes, based on problems such as finding short vectors in high-dimensional lattices, believed to resist quantum attack, and standards bodies have begun selecting and standardizing such algorithms. There’s a subtle strategic issue called “harvest now, decrypt later”: an adversary can record encrypted traffic today and store it until a quantum computer exists, so information that must stay secret for decades is already at risk, which is why the transition is being pursued before the threat is real.

I’ll follow the thread of information security into the broader concept of information itself, and Claude Shannon’s 1948 paper, “A Mathematical Theory of Communication,” which is perhaps the single most consequential piece of applied mathematics of the twentieth century. Shannon defined the information content of a message by how surprising it is: an event with probability p carries log of one over p bits, and the average over a source gives its entropy. A fair coin flip carries one bit; a loaded coin that comes up heads ninety-nine percent of the time carries far less per flip, because the outcome is rarely surprising. This definition ignores meaning entirely, which Shannon said explicitly, and that deliberate restriction is why it was so powerful: it made information something you could measure, compress, and transmit, independent of what it was about. His source coding theorem says that data can be compressed down to its entropy but no further without loss, and his noisy channel coding theorem says that, astonishingly, you can transmit information over a noisy channel with arbitrarily low error rates, provided you stay below a threshold rate called the channel capacity, by using clever redundancy. Before Shannon, engineers assumed that reducing errors required reducing speed or raising power. He showed there is a sharp limit and that, in principle, you can reach it. Decades of work in coding theory, from Hamming codes to turbo codes and low-density parity-check codes, have brought real systems very close to the Shannon limit, and every phone call, streaming video, and deep-space transmission depends on those codes.

The word entropy was borrowed, reputedly at the suggestion of John von Neumann, who supposedly quipped that nobody understands entropy, so Shannon would always win debates by using it. The story may be apocryphal, but the connection with thermodynamic entropy is real. Boltzmann’s entropy counts the number of microscopic arrangements consistent with a macroscopic state, S equals k log W, and it is deeply related to missing information about the microstate. Maxwell’s demon, the thought experiment of a tiny being that sorts fast and slow molecules to apparently violate the second law, was resolved, after a century of argument, by recognizing that information processing has a thermodynamic cost. Landauer showed in 1961 that erasing a bit of information must dissipate at least a minimum amount of heat, kT ln 2. The demon must record the molecules’ velocities and eventually erase its memory, and the erasure pays the entropic bill. Experiments in recent years have measured heat dissipation near the Landauer limit in tiny systems, confirming a link between abstract information and physical reality that Shannon and Boltzmann could only suggest.

That link has led some physicists to push the idea further, to the slogan “it from bit” coined by John Wheeler, the notion that physical reality might at bottom be informational. The holographic principle, motivated by black hole thermodynamics, is perhaps the most concrete version. Bekenstein and Hawking showed that a black hole has entropy proportional to the area of its event horizon rather than its volume, which suggests that the maximum information content of a region of space is bounded by its surface area, measured in Planck units. ‘t Hooft and Susskind generalized this to the proposal that a gravitational theory in a volume can be described by a theory without gravity on its boundary, and Maldacena’s 1997 AdS/CFT correspondence gave a precise example: a theory of quantum gravity in anti-de Sitter space is exactly equivalent to a conformal field theory on its boundary. It’s a stunning duality, in which a higher-dimensional gravitational universe is encoded in a lower-dimensional quantum theory, and it has become a major tool for studying strongly coupled systems, even though our own universe is not anti-de Sitter and so the correspondence doesn’t directly apply. More recent work has connected spacetime geometry to quantum entanglement, with the suggestion that the fabric of space is stitched together by entanglement, and that the interior of a black hole may be related to the entanglement structure of its radiation. The black hole information paradox, the question of whether information that falls into a black hole is destroyed, as Hawking originally argued, or preserved, as quantum mechanics requires, has driven much of this thinking. Calculations by Penington, Almheiri, and others around 2019 using the “island” formula appear to reproduce the expected Page curve for radiation entropy, suggesting that information does escape, though a complete, intuitive picture of how remains elusive.

I’m conscious that I’ve moved from hinges to holography, and I think that’s the pleasure of this form: threads of constraint, structure, self-reference, and the relationship between models and reality keep resurfacing in new costumes. Let me pick up one more strand that I’ve only touched, the history and psychology of how we come to see things, beginning with the peculiar fact that vision is not a camera.

The retina has a blind spot where the optic nerve exits, and you don’t notice it because your brain fills in the missing region. Your eyes make rapid movements, saccades, several times a second, and during each one your visual input is effectively suppressed; you don’t perceive the blur, and you’re largely unaware of the gaps, a phenomenon called saccadic masking. The detailed, high-resolution part of your vision, the fovea, covers only about two degrees of visual angle, roughly the width of your thumbnail at arm’s length, and the rich, continuous visual scene you feel you experience is assembled from a succession of small, detailed samples and a sketchy periphery, supplemented by expectation. Change blindness experiments demonstrate how little we retain: people fail to notice large changes to a scene if the change occurs during a brief interruption such as a flicker, a saccade, or a cut in a film. In one famous study, an experimenter asking directions was swapped for a different person while a door-carrying pair of workers passed between them, and about half the participants did not notice that they were now talking to someone else. The inattentional blindness experiment by Simons and Chabris, in which viewers counting basketball passes miss a person in a gorilla suit walking through the scene, similarly shows that attention gates awareness. Our sense of seeing everything is an illusion of completeness constructed by a system that assumes the world is stable and samples it on demand.

Visual illusions, then, are less failures than windows into the assumptions the brain makes. The checker shadow illusion, in which two squares of identical gray appear to differ strongly because one lies in apparent shadow, shows that the visual system is trying to estimate the surface’s reflectance rather than the raw light intensity reaching the eye. The brain discounts the illuminant, which is what you want in the real world, where a piece of paper looks white in dim light and bright light alike. The famous dress of 2015, which some people saw as blue and black and others as white and gold, turned on differences in how people’s brains implicitly assumed the lighting, with those who assumed a bluish, shadowed illuminant seeing white and gold, and those who assumed a warmer, artificial light seeing blue and black. It’s a rare moment when a scientific lesson became a mass cultural event: perception is inference, and different priors yield different perceptions from identical data. This resonates with the predictive processing framework in neuroscience, associated with Karl Friston, Andy Clark, and others, which proposes that the brain is fundamentally a prediction machine, constantly generating expectations about incoming sensory signals and updating its models by minimizing prediction errors. On this view, perception is a kind of controlled hallucination, as Anil Seth has put it, constrained by sensory input. The framework is attractive for its unifying ambition, spanning perception, action, and even emotion, though critics note that it can be so flexible as to risk being unfalsifiable, and its specific neural implementation is still under investigation.

Color deserves a moment, since it is among the clearest cases where experience diverges from physics. Light has wavelengths, but color is not a property of light alone. The human retina has three types of cone cells, sensitive to roughly short, medium, and long wavelengths, and color perception depends on the relative activation of the three. That’s why a screen can produce the appearance of yellow by mixing red and green light, which stimulates the cones in much the same way as a single yellow wavelength would, a phenomenon called metamerism. There’s no yellow light in the pixels, just a mixture that fools the eye. Other animals differ radically. Many birds, reptiles, and fish have four types of cones, extending their vision into the ultraviolet; many flowers have ultraviolet patterns that guide pollinators, invisible to us. The mantis shrimp has twelve to sixteen types of photoreceptors, though research suggests that it doesn’t discriminate colors especially finely, perhaps using a different strategy of direct recognition. Some women may be tetrachromats, with a fourth cone type owing to variants on the X chromosome, though the evidence for functional tetrachromacy in humans is limited. And language seems to interact with color perception in subtle ways: experiments with Russian speakers, whose language has separate basic terms for light blue and dark blue, show faster discrimination across that boundary, an effect that shrinks when verbal interference is introduced, suggesting that linguistic categories can modulate perception in the moment, even if they don’t determine it, a modest, empirically tractable descendant of the stronger Sapir-Whorf hypothesis.

Let me move from perception to pattern, and to the strangely deep topic of symmetry breaking, which runs through physics, biology, and even social systems. A pencil balanced perfectly on its tip is symmetric under rotation about its axis, but it is unstable, and any perturbation makes it fall in some particular direction, breaking the symmetry. The laws governing the pencil are symmetric, yet the outcome is not. In physics, this idea underlies the Higgs mechanism: the equations of the electroweak theory are symmetric, but the vacuum state of the Higgs field is not, and the “choice” of vacuum gives mass to the W and Z bosons while leaving the photon massless. The discovery of the Higgs boson at the Large Hadron Collider in 2012 confirmed the last missing piece of the Standard Model. Superconductivity, magnetism, and crystallization are all examples of spontaneous symmetry breaking in condensed matter: above a critical temperature, a magnet’s atomic spins point randomly and the system looks the same in every direction, while below it the spins align and a direction is chosen. In biology, symmetry breaking governs development: a spherical embryo establishes a head-to-tail axis through the graded distribution of signaling molecules, and Turing’s 1952 paper on morphogenesis showed how a pair of interacting chemicals, one an activator and one an inhibitor diffusing at different rates, can spontaneously generate stripes or spots from a uniform state. Turing patterns have since been invoked for animal coat markings, the spacing of hair follicles, and the formation of digits, and experiments on mouse limb development support a role for Turing-like mechanisms in specifying the number and spacing of fingers and toes. It is an elegant case of a mathematician who was thinking about computation and codebreaking also describing how a leopard gets its spots.

And to return, finally, to where I started, to hinges and constraints: the pattern that connects many of these stories is that order and structure arise from the interplay of freedom and restriction, of symmetric laws and asymmetric outcomes, of simple rules and complex consequences. A hinge permits one motion. A grammar permits intelligible sentences. A group axiomatizes symmetry. A conservation law follows from an invariance. A species concept slices a continuum. A map discards to reveal. A culture bounds behavior and thereby enables coordination. In each case, the removal of possibility is what makes the remaining possibilities usable and meaningful. I don’t claim this is a theory of everything, and I’m wary of the temptation to find one pattern in all things; a unifying metaphor can become a blinkered lens. But as a habit of attention, I think it’s productive to ask of any structure: what does it forbid, and what does that forbidding make possible?

With that question in hand, I’ll take the next turn toward a subject I’ve barely touched, which is the history of medicine and the slow, error-ridden discovery of how to help the sick without harming them.

For most of recorded history, the average physician was as likely to harm as to heal. The humoral theory, inherited from Hippocrates and systematized by Galen, held that health depended on the balance of four bodily fluids: blood, phlegm, yellow bile, and black bile. It guided practice for roughly fifteen hundred years, and its legacy was bloodletting, purging, and emetics, treatments that weakened patients while appearing to do something. George Washington died in 1799 after a throat infection, probably acute epiglottitis, and his physicians drained something like forty percent of his blood over a day, which almost certainly hastened his end. Yet it would be unfair to treat the humoral tradition as mere stupidity. It was a systematic, rational attempt to explain disease using the best available framework, and it encouraged careful observation of patients, diet, and environment. The problem was not a lack of intelligence but a lack of controlled comparison: when a patient recovered after bloodletting, the treatment got the credit; when the patient died, the disease did. Without a method to separate the effect of an intervention from the natural course of illness, centuries of experience could harden into confident error.

The correction came through the slow adoption of counting. James Lind’s 1747 shipboard trial of treatments for scurvy is often cited as an early controlled experiment: he divided twelve sailors into pairs, gave each pair a different remedy, and found that the pair given oranges and lemons recovered fastest. Yet the Royal Navy did not adopt citrus as standard issue for decades, and the lesson was muddied in the interim by the substitution of less effective lime juice and by competing theories of the disease’s cause, which delayed the understanding of vitamin C until the twentieth century. Pierre Louis in Paris in the 1830s used numerical methods to show that bloodletting did not improve outcomes in pneumonia, a result that struck at the heart of established practice. Ignaz Semmelweis, in Vienna in 1847, noticed that the maternity ward staffed by doctors had a much higher death rate from puerperal fever than the ward staffed by midwives, and deduced that doctors coming straight from autopsies were carrying “cadaverous particles” to the mothers. When he instituted handwashing with chlorinated lime, mortality dropped sharply. He was ridiculed, lost his position, and died in an asylum in 1865, a tragic figure whose insight was rejected partly because it implied that physicians themselves were agents of death, and partly because he lacked a theoretical mechanism and was personally abrasive. The germ theory, advanced by Pasteur and Koch in the 1860s through 1880s, supplied the mechanism, and Lister’s antiseptic surgery turned the idea into practice, dramatically reducing surgical mortality.

The randomized controlled trial, developed in the mid-twentieth century, with the 1948 streptomycin trial for tuberculosis often cited as a landmark, formalized the logic: randomize patients to treatment or control so that the groups are alike on average in everything, known and unknown, except the treatment, then compare outcomes. Blinding, so that neither patients nor doctors know who gets what, controls for expectation effects, and the placebo effect is far from trivial, especially for subjective outcomes like pain and nausea. The history of medicine since is a history of reversals discovered through such trials: hormone replacement therapy, once widely believed to protect women’s hearts, was found in the Women’s Health Initiative to raise some risks; arthroscopic surgery for certain kinds of knee arthritis, long popular, did no better than sham surgery in blinded trials; routine use of certain antiarrhythmic drugs after heart attacks, which suppressed abnormal heartbeats on monitors, turned out in the CAST trial to increase deaths. That last case teaches a particular lesson about surrogate endpoints: a drug that improves a measurable marker does not necessarily improve what patients care about, which is living longer and feeling better. Medicine’s own habits of mind, from the authority of experienced clinicians to the persuasiveness of mechanistic reasoning, have repeatedly misled it, and the discipline of testing is what has made the difference.

Even so, the evidence base has its own pathologies. Publication bias, the tendency for positive results to be published and negative ones to languish in drawers, distorts the literature, and the replication crisis that emerged in psychology and other fields in the 2010s showed that many published findings fail to hold up when repeated. The Open Science Collaboration’s 2015 attempt to replicate one hundred psychology studies found that fewer than half reproduced with statistically significant effects, and effect sizes were generally smaller. Contributing factors include small samples, flexibility in analysis, sometimes called p-hacking or the garden of forking paths, and incentives that reward novel, striking results over careful confirmation. Reforms have followed: preregistration of hypotheses and analysis plans, registered reports in which journals commit to publishing based on the quality of the design before results are known, larger collaborative studies, and greater sharing of data and code. John Ioannidis’s provocative 2005 paper arguing that most published research findings are false, based on a Bayesian argument about low prior probabilities and bias, was an early, influential statement of the problem. The picture is not uniformly bleak; some fields have more robust practices, and self-correction, however slow, is built into the enterprise. But it’s a humbling reminder that the machinery of science is a human institution subject to human incentives, and that its reliability must be maintained by deliberate design rather than assumed.

That brings me to the question of incentives more generally, and to Goodhart’s law, the maxim that when a measure becomes a target, it ceases to be a good measure. Examples abound. When a hospital is judged on waiting times, it may find ways to start the clock later or stop it early. When schools are rated on test scores, teaching narrows toward the test. When a Soviet factory was given a target for the quantity of nails by weight, it produced a few enormous useless nails; when the target was switched to the number of nails, it produced huge quantities of tiny useless ones, as the apocryphal story goes. In colonial Delhi, the British offered a bounty for dead cobras, and entrepreneurs began breeding cobras to collect the reward; when the program was canceled, the breeders released their now worthless snakes, increasing the wild population. Whether or not every one of these tales is literally true, they capture a real and recurrent dynamic: any proxy for what you care about can be gamed, and the more pressure placed on the proxy, the more the correlation with the underlying goal degrades. The deep problem is that the thing we actually care about, quality of education, health, safety, is hard to measure directly, so we rely on proxies, and the proxies become the targets. Managing this requires multiple measures, human judgment, attention to the incentive effects of measurement itself, and humility about what can be quantified. It is, in the end, another instance of the gap between the map and the territory, now with a feedback loop in which the map actively reshapes the territory.

I’ll let the notion of feedback loops carry me into control theory and cybernetics, because the idea of a system regulating itself by sensing and correcting its own deviation is one of the most pervasive and least noticed in the modern world. A thermostat compares the measured temperature to a setpoint and switches the heater accordingly. The centrifugal governor that James Watt added to steam engines regulated speed by opening or closing the throttle in response to the spinning of weighted balls. Maxwell wrote a seminal analysis of governors in 1868, which established the mathematical study of stability for feedback systems: a feedback loop can overcorrect and oscillate, or even grow unstable, depending on the delay and gain. Anyone who has adjusted a shower tap, with its delay between a change and its effect, has experienced the problem, turning too far hot and then too far cold in oscillations that settle only when you learn to make small, patient adjustments. Norbert Wiener’s cybernetics, developed during and after the Second World War, generalized these ideas to animals and machines, emphasizing that purposeful behavior can be understood in terms of feedback and information. The body is a thicket of such loops: blood glucose regulated by insulin and glucagon, body temperature by sweating and shivering, blood pressure by the baroreflex. The concept of homeostasis, developed by Walter Cannon from Claude Bernard’s notion of the constancy of the internal environment, captures the idea that living systems maintain themselves within narrow ranges by active regulation.

Ecosystems and economies also have feedback loops, with both stabilizing, negative feedback, and amplifying, positive feedback. The predator-prey cycles of lynx and snowshoe hare, documented in Hudson’s Bay Company fur records, show oscillations driven by delayed negative feedback: more hares support more lynx, which then reduce the hares, which then starves the lynx, and so on. The Lotka-Volterra equations capture the essence of such cycles in a simple model. Positive feedback, by contrast, drives runaway: the squeal of a microphone near its speaker, a bank run in which fear of failure causes withdrawals that cause failure, a technology that becomes dominant because its dominance attracts users and complementary products. W. Brian Arthur’s work on increasing returns showed how such feedback can lock in an outcome that is not necessarily the best, with the QWERTY keyboard as the textbook example, though the specific claim that QWERTY is inferior to alternatives has been disputed by economic historians. The general phenomenon of path dependence, in which early, contingent events shape later outcomes, is well established, and it helps explain why history can’t be run twice with the same result. Climate systems contain both kinds: the ice-albedo feedback amplifies warming, since melting ice exposes darker surfaces that absorb more sunlight, while increased cloud cover and radiative cooling of a warmer surface provide stabilizing effects whose net strength is a central uncertainty in estimating climate sensitivity. The tipping points that climate scientists worry about, such as the collapse of the West Antarctic ice sheet or the dieback of the Amazon rainforest, are places where positive feedbacks might push a system from one stable state to another, and where reversal might be difficult, a property known as hysteresis.

Hysteresis, the dependence of a system’s state on its history, is a lovely concept in its own right. A magnet, once magnetized, retains its magnetization when the field is removed; to demagnetize it you must apply a reverse field. A lake can flip from a clear, plant-dominated state to a turbid, algae-dominated one when nutrient loading exceeds a threshold, and reducing nutrients to the original level does not necessarily restore clarity, since the turbid state has its own self-reinforcing mechanisms; you may need to cut nutrients far below the tipping level, or intervene directly. Marten Scheffer and colleagues developed the theory of alternative stable states and early warning signals, observing that systems approaching a tipping point often exhibit “critical slowing down,” recovering more slowly from small perturbations, with rising variance and autocorrelation as a statistical fingerprint. Whether such signals can reliably forecast real-world transitions is under active investigation, with promising results in lab experiments and mixed results in the field, but the idea that you might detect an approaching cliff before you reach it is a hopeful one, and the research illustrates how deep, abstract mathematics can offer practical tools for stewardship.

Speaking of stewardship, I want to turn now to an inspiring corner of history that sits at the intersection of institution-building and moral courage: the abolition of the slave trade in Britain and the long campaign that preceded it. Thomas Clarkson, William Wilberforce, Olaudah Equiano, and many others, including the great number of ordinary people who signed petitions and boycotted sugar, built one of the first modern mass movements. Clarkson gathered evidence with relentless thoroughness, traveling thousands of miles on horseback, interviewing sailors, and collecting objects such as shackles and thumbscrews and the diagram of the slave ship Brookes, whose cross-section showing hundreds of human beings packed into a hold became one of the most effective propaganda images of the era. The campaign used pamphlets, public meetings, pottery medallions designed by Josiah Wedgwood bearing the image of a kneeling slave and the words “Am I not a man and a brother?”, and organized consumer pressure. The 1807 act abolishing the trade in the British Empire and the 1833 act abolishing slavery itself were hard-won, and they were followed by compensation paid to slaveholders, not to the enslaved, a fact that exposes the deep moral compromise embedded in the victory. In Haiti, enslaved people had already won their own freedom through revolution between 1791 and 1804, creating the first independent state founded by former slaves, and then were forced to pay a crippling indemnity to France in exchange for recognition, a debt that burdened the country for more than a century. The abolition story reminds us that moral progress is neither automatic nor clean, but also that it can be achieved by organized, sustained effort, and that large changes in what societies consider acceptable can occur within a few decades, a consideration that deserves attention whenever someone declares a present injustice to be simply the way of the world.

Reflecting on how moral views shift leads me toward an interesting line of research: the study of how norms change. Cristina Bicchieri and others have studied social norms as self-sustaining expectations: I do it because I expect others to do it and expect them to expect me to, and a norm can persist even when most people privately dislike it, as in cases of “pluralistic ignorance,” where each person wrongly believes that others endorse the norm. Foot binding in China persisted for roughly a thousand years, until a coordinated campaign in the late nineteenth and early twentieth centuries, centered on pledge associations in which families promised not to bind their daughters’ feet or marry their sons to women with bound feet, helped end it within a generation or two. The analysis by Gerry Mackie argued that the practice was a convention within a marriage market, one that no family could abandon unilaterally without ruining its daughters’ prospects, and that coordinated abandonment, with a mutual commitment, solved the problem. Similar logic has been applied to the abandonment of female genital cutting in some communities through collective public declarations. Whatever the details of each case, the structure is a coordination problem: a harmful practice sustained by mutual expectation can be dissolved by changing those expectations collectively, and the most effective interventions often work by making private dissent public and mutual.

Notice how this theme of coordination keeps reappearing: time zones, language, money, norms, even the choice of which side of the road to drive on. Driving on the left or right is arbitrary, but it must be uniform, and nations that have switched, such as Sweden in 1967 on “Dagen H,” did so with enormous planning, closing roads and clearing streets for the changeover at 5 a.m. on a Sunday. Accidents actually dropped in the following months, probably because drivers were unusually cautious, before returning to earlier levels. Schelling’s idea of focal points explains how people coordinate without communication: asked to meet someone in New York City with no further instruction, many people independently choose Grand Central Terminal at noon, because it stands out as the salient choice. Salience, shared background knowledge, and convention allow coordination in the absence of explicit agreement, and much of culture can be understood as a stockpile of focal points. Conventions can also be quite arbitrary and yet very hard to change, since the cost of unilateral deviation is high, which is why standards, from the width of railroad tracks, tracing back through some sticky engineering history, to the layout of electrical plugs, differ across regions and persist.

Let me now take a quieter turn into a subject I find consoling: the natural history of ordinary things, in this case trees, because they are far more interesting than their stillness suggests. A tree is a column of living tissue just beneath the bark, the cambium, which adds a new layer of wood each growing season, producing the rings that record its years. Because ring width depends on conditions, particularly moisture and temperature, trees from the same region share a pattern of narrow and wide rings, and matching these patterns across overlapping specimens allows dendrochronologists to build continuous chronologies reaching back thousands of years, dating timbers in ancient buildings and calibrating radiocarbon dates. The bristlecone pines of the White Mountains of California include individuals more than four thousand eight hundred years old, living in harsh, dry, high-altitude conditions where slow growth produces dense, resinous wood that resists decay. Their longevity seems tied to their austerity: the very conditions that stress them also protect them from competitors, fire, and rot.

Trees also communicate, in some sense, and here popular accounts have run ahead of the science, so it’s worth being careful. It’s well established that most land plants form partnerships with mycorrhizal fungi, in which the fungal threads extend the reach of the roots, scavenging water and nutrients such as phosphorus in exchange for sugars from the plant. These fungal networks can connect multiple plants, and experiments, notably by Suzanne Simard, have shown that carbon and other compounds can move between connected trees. Popular writing has turned this into the image of a “wood wide web” in which mother trees nurture their young and forests function as cooperative superorganisms. Some of this is well supported, such as the fact that fungal networks exist and that resources can transfer. Other parts are contested, such as how much transfer happens in natural conditions, whether it’s of ecological significance, and whether it constitutes anything like intentional nurturing. A 2023 review argued that several of the more dramatic claims outrun the evidence. This is a good case study in how an intriguing finding can be inflated by narrative appeal, and it’s a reminder to hold such stories to the same standards as any others: the network is real, the poetry is optional, and the extent of the poetry is for further experiments to say. What is not in dispute is that plants chemically signal. When a caterpillar chews on a leaf, many plants release volatile compounds that attract parasitic wasps that attack the caterpillars, essentially calling in bodyguards, and neighboring plants can detect some of these volatiles and prime their own defenses. Acacia trees in Africa have been reported to increase tannin levels in their leaves when browsed by giraffes or kudu, though the more famous claim that they warn neighbors through airborne ethylene has been questioned in later work. Whatever the final accounting, plants are active chemical agents, sensing and responding to their environment through mechanisms that, while lacking nervous systems, involve electrical signals, calcium waves, and hormone signaling. The Venus flytrap counts: it snaps shut only if a trigger hair is touched twice within about twenty seconds, a primitive memory that prevents it from wasting energy on raindrops, and it counts further stimulations to decide whether to start digesting its prey. It’s difficult to look at such behaviors without revising the casual assumption that only animals are interesting. I’ll round this section out by wandering into the realm of materials, where a similar revision of assumptions has occurred. Take glass. Is it a liquid that flows very slowly, as the popular myth about old cathedral windows being thicker at the bottom would have it? No: glass is an amorphous solid, and the uneven thickness of old panes is a result of the crown glass manufacturing process, in which a spinning disk of glass was flattened, producing uneven sheets that glaziers often installed with the thicker edge down. The relaxation time of glass at room temperature is astronomically long, far beyond the age of the universe. Yet the glass transition, the way a liquid cools into a disordered solid without crystallizing, remains one of the great unsolved problems in condensed matter physics, in that there’s no consensus on whether it reflects a true underlying phase transition or a dramatic dynamical slowdown. The late physicist Philip Anderson wrote in 1995 that the deepest and most interesting unsolved problem in solid state theory is probably the theory of the nature of glass and the glass transition. A material we’ve made for thousands of years, which fills our windows and screens and bottles, retains a core of theoretical mystery. Steel, concrete, and silicon underpin the modern world, and each has its own saga, but let me instead mention something odd and wonderful: Roman concrete. Structures like the Pantheon’s unreinforced dome, completed around 126 CE and still the largest unreinforced concrete dome in the world, have endured for nearly two thousand years, and harbors built of Roman marine concrete have survived centuries of seawater. Modern analysis has shown that the Roman recipe used volcanic ash, pozzolana, mixed with lime and seawater, which in marine structures produces rare crystalline minerals such as aluminous tobermorite that grow over time and strengthen the material. A 2023 study of lime clasts in Roman concrete proposed that the Romans used “hot mixing” with quicklime, which creates the white lime inclusions that, rather than being flaws, act as reservoirs enabling the concrete to self-heal: when cracks form and water seeps in, the lime clasts dissolve and recrystallize as calcium carbonate, sealing the crack. Whether this was the main mechanism is still being researched, but the broader point stands: ancient craft contained subtle, sophisticated chemistry, and modern materials scientists are studying it to develop more durable and lower-carbon concrete, which matters because cement production accounts for something like seven or eight percent of global carbon dioxide emissions. And so, as the ancient and modern meet in the matter of concrete, I’ll follow that thread toward energy, since the material base of civilization is inseparable from its energy base. Energy density is a surprisingly useful lens. A kilogram of gasoline holds about twelve kilowatt-hours of chemical energy, while a kilogram of a good lithium-ion battery holds roughly a fortieth to a fiftieth of that at the pack level, which is why electrifying aircraft and long-haul shipping is so much harder than electrifying cars, where electric motors’ much higher efficiency, around ninety percent versus about thirty percent for an internal combustion engine, narrows the gap. Uranium fission releases about two million times more energy per kilogram than burning coal, an enormous difference that explains both the promise and the peril of nuclear power. Vaclav Smil has spent a career documenting how energy transitions are slow, measured in decades, since they require building out vast systems of infrastructure, and how the physical realities of density and scale constrain what is feasible. At the same time, the cost of solar photovoltaics has fallen by roughly ninety percent over the past decade and a half, following a learning curve, as with Wright’s law, in which each doubling of cumulative production reduces cost by a roughly constant percentage. Batteries have followed a similar trajectory. Such learning curves are among the most important facts in energy economics, because they mean that deployment itself drives cost reduction, making early investment a way of creating the technology that later becomes cheap, a feedback loop of the productive sort. Solar energy is, in a sense, ancient: almost all our energy ultimately comes from the sun, whether as present sunlight, wind, and hydro, or as fossil sunlight stored over geological time in coal, oil, and gas. The fossil fuels represent hundreds of millions of years of accumulated photosynthesis, burned in a couple of centuries. The Carboniferous period, from about 360 to 300 million years ago, laid down enormous coal deposits in part because trees had evolved lignin, a tough polymer in wood, before fungi and bacteria had evolved efficient ways to break it down, so dead plant matter accumulated as peat and was eventually buried and transformed into coal. This once-popular explanation has been refined and challenged by more recent work suggesting that tectonic and climatic factors were at least as important, and that lignin-degrading fungi appeared earlier than once thought, so the story is not settled. Whatever the cause, the fossil fuel reserves we have exploited are a one-time endowment from the deep past, and burning them has released carbon sequestered over eons, altering the atmosphere in decades. Let me use that image of time compressed to shift into the scales of deep time, because our intuitions fail there so completely. If the age of the Earth, about 4.5 billion years, were compressed into a single year, the oldest known fossils of microbial life would appear in the spring or so, complex animals would not show up until mid-November, dinosaurs would arise in mid-December and vanish on December 26, and modern humans would appear in the last few minutes of New Year’s Eve, with all of recorded history occupying the final seconds. Such analogies are well worn, but they work because they translate numbers our minds cannot hold into proportions we can see. The history of life is also one of unexpected events. The Great Oxidation Event, around 2.4 billion years ago, saw oxygen produced by cyanobacteria accumulate in the atmosphere, poisoning much of the anaerobic life of the time and changing the planet’s chemistry irrevocably; it may have triggered the first “snowball Earth” glaciation by destroying atmospheric methane, a potent greenhouse gas. Oxygen, which we regard as the breath of life, was initially a pollutant, and its eventual harnessing for energy metabolism was a revolution that made large, active animals possible. The Cambrian explosion, beginning roughly 540 million years ago, saw the appearance, in what is geologically a short interval of perhaps twenty million years, of most major animal body plans. The causes remain debated, and likely include rising oxygen levels, the evolution of predation and consequent arms races, and developmental innovations in gene regulatory networks. The Burgess Shale in British Columbia, discovered in 1909 by Charles Walcott, preserves soft-bodied Cambrian creatures with exquisite detail, including strange forms like Hallucigenia, which was famously first reconstructed upside down and backward, with its spines mistaken for legs, until later specimens corrected the picture. Stephen Jay Gould’s popularization of the Burgess fauna in Wonderful Life argued that the survivors of the Cambrian were a matter of contingency, that if the tape of life were replayed, the outcome would differ. Simon Conway Morris, who had worked on the fossils, argued the opposite, that convergence, the repeated independent evolution of similar solutions, suggests that evolution is more constrained and predictable than Gould claimed. Eyes, for example, have evolved independently dozens of times; flight in four separate lineages; echolocation in bats and in toothed whales; the camera eye in vertebrates and in cephalopods, with the notable difference that the octopus eye lacks the blind spot of ours because its photoreceptors face the light rather than being wired behind the retina. The debate between contingency and convergence is a rich and unresolved one, and the answer likely differs for different features of life: the detailed particulars are contingent, and the broad functional solutions are repeatedly rediscovered. Mass extinctions punctuate the record. The end-Permian extinction, about 252 million years ago, was the worst, with an estimated ninety percent or more of marine species lost, probably driven by massive volcanic eruptions in what is now Siberia, which released vast quantities of carbon dioxide and other gases, causing rapid warming, ocean acidification, and oxygen depletion. The end-Cretaceous extinction, 66 million years ago, famously involved an asteroid impact at Chicxulub on the Yucatán Peninsula, a hypothesis proposed by Luis and Walter Alvarez in 1980 on the basis of a worldwide layer of iridium-rich clay at the boundary. The discovery of the buried crater a decade later made a strong case, though the contribution of the contemporaneous Deccan Traps volcanism remains debated. The survivors included small mammals, which had spent more than a hundred million years living in the shadow of dinosaurs, mostly small and nocturnal, and which then radiated to fill the vacated niches. We are here in part because a rock fell from the sky. Rather a humbling thought, and it grounds the point that contingency plays a vast role in the history of life, even if convergence shapes the particular forms. There’s a connection between mass extinctions and the present worth noting with care. Many biologists argue that human activity, through habitat destruction, overexploitation, invasive species, pollution, and climate change, is driving extinction rates far above background levels, possibly initiating a sixth mass extinction, though whether we are yet in one, in the technical sense of losing seventy-five percent of species, depends on how you count and over what time frame; the loss of species so far has been a smaller fraction, but the rate is concerning and the many species currently threatened would, if lost, bring the total toward that threshold. The history of conservation includes real successes: the recovery of the humpback whale after the commercial whaling moratorium, the return of the peregrine falcon after the ban on DDT, the reintroduction of wolves to Yellowstone, which, though the extent of the famous “trophic cascade” is debated, has changed elk behavior and has been associated with changes in vegetation and rivers in some accounts, and the more modest but real recovery of the ozone layer after the Montreal Protocol. That last example deserves emphasis. In the 1980s, scientists discovered that chlorofluorocarbons, chemicals used in refrigeration and aerosols, were destroying stratospheric ozone, producing a “hole” over Antarctica. The 1987 Montreal Protocol phased out the offending chemicals, and the ozone layer is on track to recover to 1980 levels over much of the globe by around mid-century. It’s one of the clearest cases of a global environmental problem identified by science and addressed by international treaty, helped by the availability of substitutes and by the relative concentration of production in a handful of firms, and it provides a counterweight to the pessimism that sometimes attaches to collective action problems. I notice I’ve been circling optimism and caution in alternation, and that seems right, because both are warranted. Let me use the momentum to head toward a subject about which I’ve said almost nothing yet and which deserves attention: sleep, since we spend a third of our lives doing it and it remains scientifically mysterious. Sleep is nearly universal in animals, from fruit flies to whales, which suggests a fundamental function, though what exactly that function is remains debated. Proposals include restoring energy, consolidating memory, clearing metabolic waste from the brain through the glymphatic system, which seems more active during sleep, regulating emotions, and pruning synapses to maintain the brain’s capacity to learn, the synaptic homeostasis hypothesis of Tononi and Cirelli. Likely it serves several of these purposes. Sleep proceeds in cycles of about ninety minutes, moving through light sleep, deep slow-wave sleep, and rapid eye movement sleep, during which most vivid dreaming occurs and the body’s voluntary muscles are paralyzed, a protective mechanism that prevents us from acting out dreams; when the mechanism fails, as in REM sleep behavior disorder, people may physically enact their dreams. Slow-wave sleep appears important for consolidating declarative memories, and studies show that replay of neural activity patterns in the hippocampus occurs during sleep, with the replay sometimes happening at compressed speed. Dolphins and many birds sleep with one hemisphere at a time, keeping the other awake, allowing them to continue swimming or watch for predators, while migratory birds can reduce sleep dramatically during migration; frigatebirds have been recorded sleeping in brief bursts in flight. Humans pay a heavy price for insufficient sleep: impaired attention and memory, mood disturbance, weakened immune response, and long-term associations with cardiovascular and metabolic disease. Yet modern societies have systematically curtailed sleep, a trend accelerated by artificial lighting, shift work, and screens. The notion that people in preindustrial times slept in two distinct segments at night, a “first sleep” and “second sleep” separated by a period of wakefulness, has been advanced by historian Roger Ekirch on the basis of extensive references in historical documents, and is consistent with some laboratory studies in which people kept in long nights without artificial light fell into a similar pattern. Whether this was a universal natural pattern or a culturally specific one is debated; studies of hunter-gatherer and small-scale societies, such as the Hadza and San, find that they sleep around six to seven hours, not notably more than industrial populations, but with different timing relative to temperature and light, and without the same rates of insomnia, according to some reports. The circadian clock, driven by a master pacemaker in the suprachiasmatic nucleus of the hypothalamus and by molecular feedback loops of clock genes in nearly every cell, was dissected by Jeffrey Hall, Michael Rosbash, and Michael Young, who received the 2017 Nobel Prize for identifying the genes, starting with the period gene in fruit flies. The loop is itself a beautiful example of negative feedback with delay: the clock proteins accumulate, then inhibit their own gene’s activity, then degrade, allowing the cycle to restart, producing an oscillation of roughly twenty-four hours. Time perception more broadly, interestingly, is not governed by a single clock. We have the circadian clock for the day, interval timing for seconds to minutes, and millisecond timing for things like speech and music, all with different neural substrates and different sensitivities. The subjective sense of duration expands during moments of fear or novelty, because the brain records more detail, which is why a car accident seems to unfold in slow motion in recollection, though experiments in which people were dropped in free fall showed that they did not actually perceive events faster at the time; the effect is one of memory, not perception. And the sense that time speeds up as we age is plausibly related to the decreasing novelty of experience: when days are filled with routine, few distinct memories are laid down, and in retrospect the period seems to have passed quickly, whereas a week of travel in unfamiliar places feels long when remembered. If so, there is a practical suggestion buried in the psychology: novelty lengthens subjective life. Let me stay with that idea of novelty and turn to curiosity, which is a topic I find fitting. Psychologists treat curiosity as a drive to reduce uncertainty, and George Loewenstein’s information-gap theory proposes that curiosity arises when we perceive a gap between what we know and what we want to know; it’s strongest when we know a little, which is why a partial teaser can be irresistible and why total ignorance doesn’t provoke it. Neuroscience has found that curiosity engages the brain’s reward circuitry, with dopaminergic regions activated in anticipation of learning the answer to a question one is curious about, and that curiosity enhances memory not only for the answer but for incidental material encountered while in the curious state. Children are relentless question-askers, with studies finding that preschoolers ask dozens of questions an hour, and the decline in question-asking as children enter school may reflect norms in classrooms rather than a fading of curiosity itself. Alison Gopnik has argued that children behave like scientists, forming hypotheses and testing them through exploratory play, and that the long period of human childhood is an adaptation that allows exploration to precede exploitation, a division of labor between the young, who explore broadly, and adults, who exploit what is known. The same tradeoff shows up in machine learning as the explore-exploit dilemma, and in a mathematical form in the multi-armed bandit problem: how much to sample unfamiliar options versus returning to the best known. The optimal policies typically explore more when the horizon is long, which is exactly why childhood, with a long future ahead, is a time of exploration, and why the tendency to stick to familiar options increases with age, which on this view is not merely rigidity but a rational response to a shorter horizon. The idea that one might structure a life or an institution to balance exploration and exploitation is relevant far beyond individuals. Organizations that over-exploit, refining existing products without searching for new ones, become brittle when conditions change, a pattern known as the “innovator’s dilemma” after Clayton Christensen’s analysis of how successful firms are disrupted by technologies that initially seem inferior or serve marginal markets. Kodak invented the digital camera in 1975 but struggled to embrace it because it threatened its lucrative film business. The pharmaceutical industry, scientific funding agencies, and venture capitalists all wrestle with how to allocate resources between safe bets and speculative ones. Funding processes that rely on consensus tend to favor incremental work, and some funders have experimented with partial lotteries, in which proposals above a quality threshold are selected at random, on the theory that it reduces bias and encourages bolder proposals. The evidence is early, but it points to the idea that where evaluation is noisy, introducing deliberate randomness can be a rational policy. Noise, in fact, is a recurring character in this long conversation. In Kahneman, Sibony, and Sunstein’s book on the subject, noise is the unwanted variability in judgments that should be identical: two doctors diagnosing the same patient differently, two judges giving different sentences for similar crimes, the same underwriter quoting different premiums on different days. The variability is separate from bias, which is a systematic error in one direction, and both contribute to the total error. Studies found that judges’ sentencing could vary with irrelevant factors, such as the temperature, or the outcome of a local football game, and that the same expert could make different judgments on the same case on different occasions. Remedies include structured judgment protocols, in which assessments are broken into independent components and scored before an overall impression forms, aggregating multiple independent judgments, and using guidelines or algorithms. The latter is controversial, but the research tradition going back to Paul Meehl’s 1954 work on clinical versus statistical prediction found that simple statistical rules often matched or beat expert judgment, largely because rules are consistent while humans are noisy. That does not mean humans should be removed from decisions, but it suggests that human judgment is best deployed where it adds information the rule lacks, and that consistency itself is a form of fairness. On the topic of fairness, let me end this stretch with a small but charming piece of mathematics: the problem of dividing a cake. How do you fairly split something among people who value its parts differently? For two people, the classic answer is “I cut, you choose”: the cutter will divide it so that they’re indifferent between the pieces, and the chooser takes the piece they prefer, guaranteeing that neither envies the other. Generalizing to three or more people is much harder. It was an open problem for decades whether there exists a finite procedure guaranteeing an envy-free division among any number of people, and in 2016 Haris Aziz and Simon Mackenzie gave a bounded-time protocol for any number of players, an astonishing piece of work, though the bound is astronomically large and not practical. The study of fair division, with its notions of proportionality, envy-freeness, and efficiency, has found real application in dividing inheritances, allocating shared resources, and splitting rent among roommates, where a method based on the Sperner’s lemma can find prices and room assignments such that nobody envies another’s room-price combination. It’s a sweet fact that a tool from topology, a theorem about colorings of triangulated simplices, can resolve a squabble over who gets the bigger bedroom. I’m going to keep going, and I’ll turn the next stretch to a subject that ties many of these threads together in an unexpected way: the history and mathematics of navigation, from the sextant and chronometer to GPS, and what it took to know where you are. Latitude is easy, relatively speaking. In the northern hemisphere, the altitude of the pole star above the horizon equals your latitude, and at midday the sun’s angle, corrected for the date by almanac tables, gives the same information. Longitude is the hard one, because it requires knowing the time difference between your location and a reference meridian: the Earth rotates fifteen degrees per hour, so if your local noon occurs when a clock set to the reference port reads two o’clock in the afternoon, you’re thirty degrees west. The trouble was that no clock of the era could keep accurate time at sea, where pendulums were useless on a pitching deck and temperature and humidity swings ruined mechanisms. Ships were lost for want of the knowledge; the Scilly naval disaster of 1707 killed some 1,400 sailors and spurred the British Parliament to pass the Longitude Act of 1714, offering a large prize for a practical method. Astronomers favored the lunar distance method, measuring the moon’s angle from certain stars and consulting tables, which worked but required laborious calculation. John Harrison, a self-taught Yorkshire clockmaker, spent decades building a series of marine timekeepers, H1 through H4, the last a large pocket-watch-like instrument that performed superbly on a transatlantic voyage in 1761 to 1762. The Board of Longitude, dominated by astronomers with an investment in the lunar method, resisted awarding him the full prize, and he finally received substantial recompense only after the intervention of the king in 1773. Dava Sobel’s popular account made the story widely known, though historians have argued that her portrayal of the astronomers as villains oversimplifies a more nuanced reality, in which the Board funded and supported Harrison for years and took reasonable care to ensure that a method would be reproducible and practical, since a single hand-built masterpiece does not solve a navigational problem for a whole navy. Eventually, makers like Thomas Earnshaw and John Arnold simplified the design into chronometers that could be manufactured at scale, and by the early nineteenth century they were standard on Royal Navy vessels. The modern descendant is the Global Positioning System, which solves essentially the same problem by exactly the same principle: it’s all about the time. Each GPS satellite carries atomic clocks and broadcasts its position and the precise time of transmission. A receiver measures the time delay of signals from at least four satellites, converts the delays to distances via the speed of light, and solves for its position in three dimensions plus its own clock error, which is why a fourth satellite is needed even though three spatial coordinates are being determined. The precision required is extreme: an error of a single microsecond corresponds to about three hundred meters. And here relativity enters, not as a philosophical curiosity but as an engineering necessity. The satellites’ clocks run faster by about forty-five microseconds per day because they sit higher in Earth’s gravitational well, as general relativity predicts, and slower by about seven microseconds per day because of their orbital speed, per special relativity, for a net gain of roughly thirty-eight microseconds per day. Without correction, position errors would accumulate at about ten kilometers per day. The system’s designers built the correction into the satellite clock frequency, and so every time your phone locates you, a small confirmation of Einstein’s theories occurs in your pocket. It’s an outstanding example of abstract physics becoming everyday infrastructure. There’s a deeper theme here about the time-keeping obsessions of civilization, which I touched on with railways and leap seconds, and it connects to the development of ever more precise clocks. The pendulum clock, perfected by Huygens in 1656, improved accuracy dramatically. Quartz oscillators in the twentieth century brought another leap, and the cesium atomic clock, first built in 1955, defines the second as 9,192,631,770 cycles of a particular microwave transition. Optical lattice clocks, which use atoms trapped by laser light and probe a transition at optical frequencies, are now so stable that they would lose less than a second over the age of the universe. At that sensitivity, they can detect the gravitational time dilation from raising a clock by a centimeter or so, and a clock on a table runs measurably faster than one on the floor. Such clocks are being considered as tools for geodesy, mapping the Earth’s gravitational field by its effect on time; for tests of whether fundamental constants vary; and for the potential redefinition of the second. The grandest scheme is that clocks could even serve as detectors of dark matter or gravitational waves. We have reached a point where our instruments for measuring time are among the most sensitive probes of nature we have. Precision measurement has its own history of heroic effort. In 2019 the international system of units was redefined so that all seven base units are defined by fixed values of fundamental constants, replacing the last artifact standard, the International Prototype Kilogram, a cylinder of platinum-iridium kept in a vault near Paris. Over the years, comparisons with its official copies had suggested slight drifts in relative mass, on the order of tens of micrograms, an unsettling prospect for a definition that was supposed to be absolute: if the standard itself changes, then what exactly is a kilogram? The new definition fixes the value of the Planck constant, and a kilogram can in principle be realized anywhere by instruments such as the Kibble balance, which compares mechanical power to electrical power through quantum electrical standards. It’s quietly profound. The meter was originally defined as one ten-millionth of the distance from the equator to the North Pole along a meridian, and measuring that distance was the project that sent Delambre and Méchain on a seven-year survey of France and Spain in the 1790s, in the middle of the Revolution, with Méchain tormented by a small discrepancy in his latitude measurements that he concealed and worried over until his death. The standard shifted from Earth to artifact to light to constants, each step trading a local, physical object for something more universal and reproducible. That trajectory, from the particular and local toward the universal and portable, is also the arc of mathematics, law, and science: a continuing effort to find descriptions that don’t depend on who or where you are. Yet I want to resist presenting it as pure progress, since universality has costs, and I’ll turn to the opposing virtue, the knowledge that is irreducibly local. James C. Scott, in Seeing Like a State, argued that large-scale schemes to impose legibility and uniformity on complex local realities have often failed disastrously, from scientific forestry in eighteenth-century Prussia and Saxony, which replaced diverse old forests with orderly monocultures of Norway spruce, boosting yields for a generation and then producing ecological decline in the second rotation, to Soviet collectivization, to planned cities like Brasília. What was lost, Scott said, was metis, the practical, local, tacit knowledge accumulated by those who work a place, which formal schemes cannot capture and often destroy. Scott’s critique can be overdrawn, since standardization has also brought immense benefits, such as public health systems and uniform weights and measures, but the cautionary core is sound: abstraction discards information, and sometimes the discarded information turns out to be what held the system together. It’s the same lesson as Goodhart’s law and the Mercator projection from different angles: a model is a tool, powerful in its domain and treacherous outside it.

Let me find a subject that illustrates local knowledge beautifully, which is the art of traditional navigation in Oceania. Polynesian and Micronesian navigators crossed thousands of miles of open Pacific without instruments, using a deep understanding of stars, swells, winds, bird behavior, and cloud formations. The Micronesian system of star compasses divides the horizon into named points where specific stars rise and set, and a navigator memorizes the sequence of stars that rise along each bearing, so that as one star climbs too high to be useful, the next one rising from the same point takes over. Ocean swells, generated by distant weather systems and persisting for long distances, create regular patterns that navigators learned to read through the motion of the canoe, even lying in the hull to feel them. Islands disturb the swell patterns, creating reflected and refracted waves that can be detected well before the island is in sight. The Pacific was settled in a series of extraordinary voyages, with the Lapita people spreading eastward through Melanesia into Tonga and Samoa by about 3,000 years ago, and then, after a long pause, the settlement of the far eastern islands, Hawai’i, Rapa Nui, and Aotearoa, between roughly 1,000 and 700 years ago. The Hōkūle’a, a voyaging canoe built in 1975 as a replica of a traditional double-hulled vessel, sailed from Hawai’i to Tahiti in 1976 guided by the Micronesian navigator Mau Piailug, demonstrating that the founding voyages could have been deliberate and navigated, rather than accidental drifting, which some Western scholars had claimed. The revival of traditional wayfinding since has been as much cultural as technical, an assertion of continuity with ancestral knowledge.

I’d like to follow the arc of such knowledge systems into an area where Western science has recently begun to take them seriously: traditional ecological knowledge. Indigenous fire management in Australia, with cool, patchy, early-dry-season burns, reduces the intensity of later wildfires, and programs partnering with Aboriginal rangers have been shown to cut emissions and protect biodiversity in northern Australia. After the catastrophic bushfires of 2019 to 2020, interest in cultural burning surged in southern Australia as well. In North America, Indigenous burning shaped landscapes like the oak savannas and the prairies for millennia, and the suppression of such burning by colonial policy contributed to fuel accumulation that now fuels megafires, though the relative role of different causes, including climate change and past forestry practice, is complicated. In the Amazon, large areas of “terra preta,” dark, fertile anthropogenic soils enriched with charcoal and organic waste, indicate substantial pre-Columbian management of the landscape, and some researchers argue that parts of the supposedly pristine rainforest are in fact the legacy of long human cultivation, with concentrations of useful tree species such as Brazil nut and palms that correspond to past settlements, an interpretation still under debate about the scale of population and alteration. These examples complicate the image of untouched wilderness as a baseline and suggest that many ecosystems co-evolved with human stewardship, so that the line between nature and culture is blurrier than the conservation tradition once assumed.

The question of baselines is itself a deep one in ecology: the shifting baseline syndrome, named by Daniel Pauly, describes how each generation of scientists and the public accepts the degraded state they first encountered as normal, so that gradual losses go unremarked. Fishery scientists who began working in the 1970s took fish abundances then as the reference, unaware that stocks had already collapsed from earlier levels. Historical records, from ship logs, restaurant menus, and trophy photographs, allow reconstruction of earlier abundance, and they reveal staggering declines, as in the case of the Atlantic cod, once so thick off Newfoundland that early explorers reported being able to scoop them up in baskets, and which collapsed in the early 1990s after decades of industrial fishing, leading to a moratorium that devastated communities and from which the stocks have recovered only partially. The shifting baseline is a species of the general problem of memory and institutions: without records that outlast individuals, societies lose track of what they’ve lost.

That naturally leads me to the question of record keeping and the preservation of knowledge, and I’ve been saving a few stories on the fragility of the written word, which seems a fitting thing to ponder in a monologue that is itself a stream of words. The Library of Alexandria’s fate is among the most mythologized events in history; its decline seems to have been gradual, through reduced funding, political upheaval, and neglect over centuries, rather than a single great fire, though Caesar’s fire in 48 BCE may have destroyed some warehouse stock. What’s certain is that the vast majority of ancient literature is lost; of Sophocles, who wrote more than a hundred plays, seven survive complete; of Aeschylus, seven of perhaps ninety. The survivors owe their survival largely to their inclusion in school curricula and to the later copying efforts of Byzantine and Arab scholars and medieval monks. Survival was a matter of selection by taste and utility, which suggests that what we have is a skewed sample, and that we should be humble about our picture of the ancient world. Sometimes remarkable recoveries occur: the Oxyrhynchus papyri, thousands of fragments discovered in an ancient rubbish heap in Egypt beginning in 1896, have yielded lost works of Sappho, Menander, and others, along with a mass of mundane documents, receipts, letters, and tax records, which give a vivid view of everyday life. The Herculaneum scrolls, carbonized by the eruption of Vesuvius in 79 CE, were unreadable for centuries, but in recent years researchers using X-ray tomography and machine learning have begun to recover text from the unopened scrolls, with the Vesuvius Challenge in 2023 awarding prizes for the first legible passages, a remarkable instance of modern computation extending the reach of ancient reading.

And as for modern records, the fragility is perhaps greater than in antiquity. Digital media decays, formats become obsolete, and storage services shut down. A “digital dark age” has been warned of by computer scientists such as Vint Cerf, in which future historians find little trace of our era because the formats and software needed to read our files are gone. Projects like the Internet Archive’s Wayback Machine, which has archived hundreds of billions of web pages, are attempts to counter this, but they cover only a fraction, and link rot is pervasive: studies of legal citations and scholarly references find that large proportions of cited URLs no longer work after several years. The contrast with a clay tablet, which can last four thousand years if baked by an accidental fire, is notable; the very durability of the oldest records comes from their crude physicality. Long-term preservation requires active maintenance, institutional continuity, and migration, a continuous act of care rather than a one-time act of storage, which brings us back to the theme of patience and stewardship.

I’m still going, because there is no shortage of material and because the thread of “care across time” feels like a good one to follow into the final subject of this segment: the idea of institutions as machines for outlasting their founders. The Catholic Church, the oldest continuously operating institution in the West, has persisted for two millennia through a combination of hierarchy, ritual, text, and adaptive reinterpretation. Universities, with Bologna dating from 1088 and Oxford from the late eleventh or twelfth century, have survived wars, plagues, and revolutions. Japan’s Kongō Gumi, a construction company specializing in temple building, operated for some fourteen hundred years from 578 until it was absorbed into a larger firm in 2006, and a number of Japanese family businesses have existed for centuries. What do such long-lived institutions have in common? Often a core function that remains needed, a flexible form that can adapt, a mechanism for succession that avoids dependence on any one individual, and a set of values or rituals that bind participants across generations. Many fail for the opposite reasons: rigidity, capture by insiders, loss of purpose. Elinor Ostrom’s studies of commons governance found that communities can manage shared resources sustainably for centuries, as in the Swiss alpine meadows, the irrigation systems of Valencia’s huerta, with its Tribunal de las Aguas that has settled disputes every Thursday outside the cathedral door for something like a thousand years, and the fisheries of Maine’s lobstering communities, provided that they have clear boundaries, rules tailored to local conditions, participation of those affected in rule-making, effective monitoring, graduated sanctions, and accessible conflict resolution. Her work, which earned her the 2009 Nobel Prize in economics, refuted the pessimistic reading of Garrett Hardin’s “tragedy of the commons,” which had supposed that shared resources must inevitably be ruined unless privatized or controlled by the state. The tragedy happens when governance is absent, not when the resource is shared. This is a deeply hopeful finding, and it rhymes with everything I’ve said about constraint: the rules, far from stifling the community, are what allow the commons to flourish.

And that, I find, loops back once more to the hinge, the grammar, the rules of chess: a good rule is not the opposite of freedom but its scaffolding. There is so much further to go, and I’ll continue next with the physics of music’s close cousin, sound itself, and the odd ways acoustics shapes both architecture and the human voice.

Proceed

Sound is the right next subject, because it is the one physical phenomenon we all carry around inside us. Start with what sound actually is: a pressure wave moving through a medium, a chain of compressions and rarefactions in which no air molecule travels far, though the disturbance crosses a room. In air at room temperature the wave moves at about 343 meters per second, and that number depends on temperature but, perhaps surprisingly, not on pressure. Sound travels faster in water, around 1,480 meters per second, and faster still in steel, because the stiffness of a medium relative to its density sets the speed. The ear is an extraordinary instrument for detecting these waves. At the threshold of hearing, the eardrum moves by less than the diameter of a hydrogen atom, and the range from the faintest audible sound to the loudest tolerable one spans a factor of roughly a trillion in power, which is why we use the logarithmic decibel scale. A conversation sits near sixty decibels, a rock concert near a hundred and ten, and every additional ten decibels is a tenfold increase in power, though only about a doubling in perceived loudness.

The cochlea performs something close to a physical Fourier transform. It is a fluid-filled spiral tube lined with the basilar membrane, which is narrow and stiff at the base and wide and floppy at the apex, so that high frequencies cause maximum vibration near the entrance and low frequencies travel farther in before peaking. Along that membrane sit roughly sixteen thousand hair cells, and each region is effectively tuned to a particular frequency. The result is a place code: your brain learns what pitch it is hearing partly from where along the membrane the vibration was strongest. Georg von Békésy won the 1961 Nobel Prize for mapping this traveling wave in cadaver cochleae. Later work revealed that the picture was incomplete, because dead cochleae are far less sharply tuned than living ones. The outer hair cells actively amplify the vibrations, changing length in response to the signal, in effect pumping energy back into the wave. This cochlear amplifier is so strong that the ear sometimes emits sound on its own, called otoacoustic emissions, which audiologists now use to screen newborns for hearing loss. A healthy ear is both a microphone and, faintly, a loudspeaker.

Pitch perception has a famous oddity: the missing fundamental. If you play a set of tones at 200, 300, and 400 hertz, listeners hear a pitch of 100 hertz, even though no energy exists at that frequency. The brain infers the common periodicity of the harmonic series and reports a fundamental that isn’t there. This is why a small telephone speaker, unable to reproduce low frequencies, can still convey the deep voice of a man, and why organ builders can simulate a very low pipe by sounding two higher ones together. It also shows that pitch is a construction of the auditory system rather than a direct reading of the physical spectrum. Timbre, the quality that distinguishes a violin from a flute playing the same note, comes from the relative strengths of the harmonics and from how they evolve in time. The attack of a note, its first few tens of milliseconds, carries so much identifying information that if you cut it off a recording of a piano and play the rest, many listeners struggle to say which instrument they are hearing.

Now think about what a room does to sound. When a sound is made in an enclosed space, the listener first receives the direct sound, then a series of discrete early reflections from nearby surfaces, then a dense, decaying wash of reflections called reverberation. Wallace Clement Sabine, a young Harvard physicist, founded architectural acoustics in the 1890s when he was asked to fix the dreadful acoustics of the university’s new Fogg Lecture Hall. He and his assistants spent nights moving cushions borrowed from the nearby Sanders Theatre in and out of the room, timing with a stopwatch and an organ pipe how long a tone took to fade to inaudibility. From this he derived the reverberation time formula, which says that decay time is proportional to the room’s volume divided by its total absorption. The rule is simple and has guided hall design ever since. A hall for speech wants a reverberation time of perhaps a second or less, so that syllables don’t smear; a hall for orchestral music wants closer to two seconds, which gives the sound warmth and fullness; a cathedral can reach six seconds or more, which is why plainchant and slow polyphony developed in such spaces, since quick melodies would dissolve into mush. Music and architecture have shaped each other for centuries: composers wrote for the rooms they knew.

What makes a great concert hall is not fully reducible to a formula, which is part of the fascination. Leo Beranek surveyed conductors and critics around the world in the 1960s and correlated their rankings with measured acoustic properties. The consistently top-ranked halls were shoebox shaped: the Musikverein in Vienna, the Concertgebouw in Amsterdam, Symphony Hall in Boston. Their narrow, rectangular form delivers strong early lateral reflections, which arrive at the two ears from the sides at slightly different times and produce a sense of envelopment, of being surrounded by the music. The listener perceives this as spaciousness, and it’s a quality that fan-shaped and wide halls often lack. Some halls have been spectacular failures. Philharmonic Hall in New York, opened in 1962, was notorious for thin bass and a lack of warmth and underwent rebuilding repeatedly, culminating in a complete gutting in 1976. Royal Albert Hall in London was famous for an echo so pronounced that a wag said it was the only place where a British composer could be sure of hearing his work twice, until acoustic diffusers, the “mushrooms” hanging from the ceiling, tamed it in 1969. Modern design uses computer modeling and scale models, and the Elbphilharmonie in Hamburg, with its vineyard-style seating, relies on a surface of around ten thousand uniquely milled gypsum fiber panels whose tiny irregular patterns scatter sound, designed by Yasuhisa Toyota with the help of extensive simulation. And yet, even with all this technology, designers readily admit that some of the success of a hall remains a matter of judgment, sensitivity, and a little luck.

Whispering galleries show sound’s geometry in a more dramatic form. In the dome of St. Paul’s Cathedral in London, a whisper against the wall on one side can be heard clearly on the opposite side, some forty meters away. Lord Rayleigh explained this in the early twentieth century: the sound hugs the curved wall, repeatedly reflecting at grazing incidence, so that it travels around the circumference rather than spreading out and weakening as it would in open air. The waves are confined to a thin layer along the wall and diminish only with the distance traveled, not with the square of it. The ancient Greek theaters, like Epidaurus, achieve something equally impressive through a different mechanism. A person speaking on the stage can be heard clearly in the topmost rows, some fifty-five rows up, and acoustic studies suggest the stepped limestone seating acts as a filter that suppresses low-frequency background noise while reflecting the higher frequencies of the human voice back toward the audience. The 2007 study by Georgia Tech researchers reported this filtering effect, though later work has debated how much of Epidaurus’s legendary clarity is due to the seat geometry and how much to the generally quiet setting and the careful placement of the audience.

The human voice is its own marvel of acoustic engineering. The vocal folds in the larynx vibrate, chopping the stream of air from the lungs into puffs and producing a buzz rich in harmonics. The vocal tract, the throat, mouth, and nasal passages above, acts as a resonant filter that amplifies certain frequency bands, called formants, and suppresses others. The vowel sounds you recognize are defined by the positions of the first two or three formants, which you alter by moving your tongue, jaw, and lips. Say “ee” and then “ah” and feel how the shape of the cavity changes; that shape is shifting the resonances. This is the source-filter model of speech, developed by Gunnar Fant in the 1950s, and it explains why you can whisper vowels, since whispering replaces the vibrating folds with turbulent noise but keeps the same filtering, and why robots in old films sounded the way they did: a crude buzz source run through a simple filter. It also explains the difference between a speaker and a singer. A trained operatic singer can produce a “singer’s formant,” a cluster of energy around three thousand hertz achieved by lowering the larynx and narrowing the pharynx, which lets the voice cut through a full orchestra without amplification, because the orchestra’s energy at those frequencies is relatively low. The singer isn’t louder so much as better placed in the spectrum.

Birdsong poses a stranger version of the same problem. Birds produce sound with the syrinx, an organ at the base of the trachea with two sound sources that can operate independently, so that a songbird can in effect sing two notes at once or produce rapid alternations that sound like a single impossibly fast trill. The sonogram of a hermit thrush shows harmonically pure tones and intricate structure that some listeners find reminiscent of flute music, and indeed some composers, Messiaen foremost among them, transcribed birdsong into their scores with devoted care. Songbirds learn their songs from tutors during a sensitive period, in a process remarkably parallel to human language acquisition: they listen, they babble in a subsong stage, they gradually shape their own output toward a memorized template, relying on auditory feedback. Deaf birds raised in isolation produce abnormal songs. Regional dialects exist, and in some species they drift over generations, a form of cultural evolution in a nonhuman animal. The neural circuitry for song learning, discovered by Fernando Nottebohm in canaries and zebra finches, includes brain regions that grow and shrink with the seasons, and his work contributed to the discovery that adult brains can generate new neurons. A great deal of what we know about neurogenesis in adults, and about vocal learning more generally, traces to the humble study of birds singing.

Whales extend the story into the sea, where sound is the dominant sense because light fades quickly and sound travels far. Humpback whale songs are long, structured sequences of moans, cries, and chirps that can last for twenty minutes and are repeated for hours. They are sung by males, and a population shares a song that changes gradually over the season; occasionally a novel song sweeps across the Pacific from west to east, replacing the previous one within a couple of years, as studies of songs recorded across the South Pacific have documented. This is horizontal cultural transmission on an ocean scale. Underwater, there is a layer called the SOFAR channel, at a depth of around a kilometer, where the temperature and pressure combine to give a minimum of sound speed, and sound waves entering this layer are refracted back toward its axis and can travel for thousands of kilometers. The Navy exploited this in Cold War hydrophone arrays to track submarines, and, as an unexpected side effect, the recordings provided data on blue and fin whale calls, which sit at around twenty hertz, below the range of human hearing. It is plausible, if not certain, that whales once communicated across entire ocean basins and that the growth of shipping noise, which raises ambient noise in the same low frequency band, has dramatically shrunk their acoustic world. A researcher’s phrase for this is the loss of “acoustic space.” The effects of noise on marine life are an active area of study, with concerns about sonar, seismic airgun surveys, and the general rumble of commercial shipping, and the 2020 slowdown in shipping early in the pandemic gave a rare natural experiment: studies of right whales in the Bay of Fundy found measurable drops in stress hormone levels when noise from ships fell.

Bats and dolphins take sound from communication to perception. A bat emits ultrasonic pulses, often above fifty kilohertz, and listens for the echoes, using the delay to judge distance, the Doppler shift to judge relative velocity, and the spectral changes to judge the shape and texture of objects. Donald Griffin, who discovered echolocation in the late 1930s with Robert Galambos while they were students at Harvard, famously took a captive bat to a physics colleague’s lab, where an ultrasonic microphone revealed what no human ear had heard: a bat in flight was shouting constantly in the sky. Some bats adjust their calls to avoid jamming one another, and some moths have evolved ears tuned to bat calls, or even produce their own clicks to jam the bat’s sonar, an acoustic arms race that has run for fifty million years. Toothed whales have a related system; a dolphin generates clicks in a structure in its forehead called the melon, which focuses the sound into a beam, and receives the echoes through its lower jaw. A dolphin can discriminate between objects differing in material, such as aluminum from brass, and can detect a small fish buried in sand. The fact that an animal can perceive the interior structure of another through sound has led to speculation that dolphins may experience something like a sonic X-ray of one another, a notion that is more suggestive than established, but there is some behavioral evidence that they attend closely to such information.

Back in the human world, the invention of recorded sound was one of the great transformations of culture, and it is worth remembering how recent and strange it was. Before Edison’s phonograph in 1877, every sound vanished the moment it was made. The idea that a voice could outlast its speaker was uncanny enough that early listeners were unnerved by it, and Edison himself initially imagined the main use as dictation and recording the last words of the dying. Another pioneer, Édouard-Léon Scott de Martinville, had actually made a recording device in 1857, the phonautograph, which traced sound waves as lines on soot-blackened paper, but with no way to play them back. In 2008, researchers at Lawrence Berkeley National Laboratory used optical scanning and computer processing to convert such a trace from 1860, of someone singing a fragment of “Au Clair de la Lune,” into audible sound, so that a voice from before the American Civil War could be heard for the first time. The Victorian invention of recording changed music itself: performers could now be heard by millions, composers’ works could be preserved in a definitive performance, and the very skills of listening shifted, since the audience was no longer limited to live events. Glenn Gould famously retired from concert performance in 1964 to devote himself to the studio, arguing that the recording was the proper medium of the future, a stance that seemed eccentric then and more prescient now.

The technology of recording also sharpened the physics. Digital audio relies on the sampling theorem, associated with Nyquist and Shannon, which states that a signal containing no frequencies above a given limit can be perfectly reconstructed from samples taken at more than twice that limit. Human hearing tops out around twenty kilohertz, so the compact disc standard of 44,100 samples per second captures the whole audible band with a little margin for the filters. It’s a startling theorem, since a continuous waveform carries infinitely many points between any two samples, yet nothing is lost, provided the signal is band limited. Violations produce aliasing, in which frequencies above the limit fold back down and masquerade as lower ones, the same effect that makes wagon wheels appear to spin backward in films. In practice, audio converters filter out frequencies above the limit before sampling to prevent this. There’s a good deal of passionate argument among audiophiles about whether higher sampling rates or analog media sound better, and controlled listening tests generally find that, for playback, differences between standard resolution and high resolution are very hard to detect, whereas the differences in the recording, mixing, and mastering matter far more. The vinyl revival is probably best explained by factors other than fidelity: the physical ritual, the artwork, the sense of intention in sitting down to listen to an album side.

That brings me to a favorite corner: the psychology of music and why it moves us. Music activates the brain’s reward system. Studies by Robert Zatorre, Valorie Salimpoor, and colleagues found that listening to music one finds intensely pleasurable triggers dopamine release, in the caudate nucleus during anticipation of a favorite passage and in the nucleus accumbens at the peak, the same circuitry involved in food, sex, and drugs, and that the anticipation is as important as the moment of resolution. This fits Leonard Meyer’s theory, from the 1950s, that musical emotion arises from expectation: composers set up patterns, delay or violate them, and fulfill them in satisfying ways. The deceptive cadence, in which a phrase seems headed for the home chord but veers elsewhere, produces a small jolt of surprise followed by a reorienting pleasure. David Huron’s more recent work, in Sweet Anticipation, develops a detailed account of how the brain’s prediction systems, tuned by statistical learning of the musical idiom one grew up with, generate feelings of tension, surprise, and release. Chills in response to music, the shiver down the spine, tend to occur at moments of unexpected harmonic change or sudden dynamic shifts, and are more common in people high on a personality trait called openness to experience. Why this should have evolved is debated: some propose it is a byproduct of other capacities, as Steven Pinker’s quip about “auditory cheesecake” suggests, while others see music as an adaptation for group bonding, coordinating emotion and movement, or for mother-infant communication, since lullabies and infant-directed singing appear in cultures worldwide with similar acoustic features.

The cross-cultural study is instructive. A 2019 analysis of recordings from dozens of societies found that songs of certain kinds, lullabies, dance songs, healing songs, love songs, share features across cultures, and that listeners from different societies, with no knowledge of the original contexts, could guess above chance which songs were lullabies or dance songs. Yet the details vary enormously: scales, rhythmic organization, the use of microtones, the relation of music to speech. The Javanese gamelan uses tuning systems, slendro and pelog, that don’t map onto Western pitches, and each ensemble is tuned to itself, with the instruments’ paired tones deliberately slightly mismatched to produce a shimmering beating that is considered beautiful. Indian classical music organizes melody in ragas, each with a characteristic set of notes, ascending and descending patterns, and associated moods and times of day, and organizes rhythm in cycles called talas, in which the cycle’s first beat, the sam, becomes a point of arrival that performers play with, anticipating it and landing on it with a flourish. West African drum ensembles layer interlocking rhythms of different lengths, creating polyrhythms in which listeners may pick out different patterns depending on where they focus attention, and a cross-rhythm like three against two is a basic ingredient of much African-derived music, including jazz, salsa, and rock. The study of rhythm shows how deeply our bodies participate in music. Humans, unlike most other animals, can spontaneously synchronize movement to a beat, and for a long time it was thought that this was a uniquely human trait, until a cockatoo named Snowball became an internet celebrity in 2008 for dancing to the Backstreet Boys, and subsequent careful experiments confirmed that some parrots do entrain to a beat, suggesting that the capacity is linked to vocal learning, an idea formalized in the vocal learning hypothesis for beat perception.

Noise, in the colloquial sense, has a history too, and I’ll end this part there, but not before noting how the very concept of unwanted sound has shaped cities. Industrial and traffic noise is not merely annoying. Chronic exposure is associated with elevated stress hormones, sleep disturbance, and increased risk of cardiovascular disease; the World Health Organization has estimated that in Western Europe, hundreds of thousands of healthy life years are lost each year to environmental noise. Studies of children in schools near airports show reading impairments that improve when the noise is reduced, and a famous natural experiment came from the closing of the old Munich airport in 1992 and the opening of a new one, which let researchers track the cognitive performance of children at both sites and observe changes that followed the noise. Cities have responded through zoning, quieter pavements, barriers, and, in some places, the cultivation of “quiet areas.” The soundscape as a field of study, begun by R. Murray Schafer in the 1970s with the World Soundscape Project in Vancouver, asks us to treat the acoustic environment as something to be designed with care and not merely suppressed. Schafer distinguished “keynote sounds,” the background that defines a place, like the sea for a coastal town, from “signals,” foregrounded sounds that demand attention, and “soundmarks,” unique community sounds worth preserving, like a distinctive bell. He lamented the rise of the “lo-fi” soundscape, in which the cacophony of modern life swamps the subtle sounds that once let us hear distances and directions and the character of places. It’s a plea to listen, and that seems a fine segue into the next subject, because listening, of all human skills, may be the most underrated, and the study of attention is where I’ll turn next.

Attention is the narrow gate through which the world enters consciousness, and its limits shape nearly everything we do. William James wrote in 1890 that everyone knows what attention is, the taking possession by the mind, in clear and vivid form, of one of several simultaneously possible objects or trains of thought. That definition holds up well. What modern research has added is a detailed account of how limited the gate is and how it operates. The cocktail party problem, named by Colin Cherry in 1953, asks how we follow a single voice in a crowded room. Cherry’s dichotic listening experiments, in which participants heard different messages in each ear and were asked to repeat one, showed that people could follow the attended message easily and afterward remembered almost nothing about the unattended one beyond physical features, such as whether the voice was male or female, though they would often notice their own name in the ignored channel, a finding that suggested the unattended stream is processed at least enough to detect personally significant stimuli. Theorists argued for decades over where the filter lies, early, rejecting stimuli on the basis of physical features, as Broadbent proposed, or late, after meaning is extracted, as Deutsch and Deutsch held. Anne Treisman’s attenuation model offered a compromise: the unattended channel is turned down rather than blocked, so that highly salient or primed items can still break through. Later work by Nilli Lavie argued that the location of selection depends on perceptual load: when a task is demanding, irrelevant stimuli are excluded early, but when it is easy, the spare capacity spills over and distractors get processed. This has an everyday implication, which is that a person absorbed in a hard task is less distractible than someone doing a dull one, a pattern most people recognize from their own experience of being able to concentrate better in a complicated problem than in a tedious one.

Multitasking is largely a myth in the sense that people imagine it. What actually occurs is rapid task switching, and each switch carries a cost, as Rubinstein, Meyer, and Evans measured in the early 2000s: after each change of task, performance is slower and more error prone for a short interval while the mind reconfigures its goals and rules. The psychological refractory period shows that when two tasks both require a central decision stage, the second must wait for the first. Studies of people using phones while driving find impairment comparable in some respects to being over the legal blood alcohol limit, and, importantly, hands-free devices do not eliminate the problem, because the interference lies in the cognitive load of the conversation, not in the handling of the device. A small number of people, “supertaskers,” identified by Watson and Strayer at the University of Utah, appear to be able to drive and perform a demanding auditory task with no cost, but they make up only a couple of percent of the population, and people’s own estimates of their multitasking ability bear little relationship to their measured performance; heavy media multitaskers actually tend to do worse on some measures of filtering out distractions, though the direction of causation is unclear. The lesson for ordinary life is unglamorous: do one thing at a time, and treat the feeling of effortless juggling as unreliable evidence.

There’s an interesting aspect of attention that is often overlooked, which is that it can be directed to places other than where the eyes are pointed. Covert attention allows you to monitor a location in the periphery while fixating elsewhere, as when a driver keeps an eye on a car in the mirror without turning. Michael Posner’s cueing experiments in the 1980s revealed that a valid cue directing attention to a location speeds detection there, while an invalid cue slows it, and that attention can be thought of as a spotlight, or perhaps more like a zoom lens whose focus can be broadened or narrowed. Attention can also be object based, spreading to cover the whole of an object once any part of it is selected, and feature based, enhancing a particular color or direction of motion everywhere in the visual field. Neuroscientifically, attention boosts the firing rate of neurons responding to the attended stimulus and reduces the correlated noise among them, effectively sharpening the signal. There is a network view of attention too: Posner and Petersen described separate systems for alerting, orienting, and executive control, with distinct anatomy and neurochemistry, and disorders of attention such as ADHD can be understood as differences in how these systems are regulated, though the full picture is far from simple.

There is also the subject of mind wandering, which seems to be the default state of a great deal of human thought. In a 2010 study by Matthew Killingsworth and Daniel Gilbert, using a smartphone app that pinged thousands of people at random moments to ask what they were doing and thinking, participants reported that their minds were wandering about forty-seven percent of the time, and that they were generally less happy when mind wandering than when focused on the task at hand, regardless of what the task was. The brain’s default mode network, a set of regions including the medial prefrontal cortex and posterior cingulate, discovered by Marcus Raichle and colleagues to be more active at rest than during demanding tasks, is implicated in self-referential thought, memory retrieval, imagining the future, and thinking about other people’s minds. Mind wandering is not purely a failing. It supports planning, creative incubation, and the integration of experience into a narrative of one’s life. People who are given a demanding task and then a break that allows undemanding mind wandering often solve problems better than those who go on to another demanding task, consistent with the old experience of a solution arriving in the shower. The relationship between focus and wandering is a trade-off to be managed rather than a problem to be eliminated, and contemplative traditions that cultivate sustained attention, like various forms of meditation, can be seen as training the capacity to notice when the mind has wandered and to return, the repeated act of noticing and returning being itself the skill. Whatever the ultimate benefits claimed for mindfulness, which have sometimes been oversold, trials do find modest improvements in attention and in some measures of well-being, with the best evidence in the context of anxiety and depression and relapse prevention, and with effects often comparable to other active treatments rather than dramatically larger.

Let me use the thread of the wandering mind to consider the odd status of boredom, the discomfort of unoccupied attention. In a 2014 experiment by Timothy Wilson and colleagues, participants were left alone in a room for fifteen minutes with nothing to do except think, with the option to give themselves a mild electric shock, which they had previously said they would pay to avoid. A majority of the men, and a quarter of the women, chose to shock themselves at least once. The finding is a bit startling, and the interpretation is debated, since some participants may simply have been curious about the shock, but it is consistent with the idea that an unstructured mind is uncomfortable for many people, and that we go to remarkable lengths to avoid it. The historical record has an earlier version: Pascal wrote in the seventeenth century that all of humanity’s problems stem from man’s inability to sit quietly in a room alone. Boredom, on some accounts, is the signal that the current activity is not worth its cost, pushing us to seek something better, an exploration-exploitation signal, again. It may also be the necessary precondition for certain kinds of creativity, and there’s a widespread concern that constant availability of stimulation from phones eliminates the empty intervals in which attention would otherwise turn inward. The evidence on whether smartphone use harms attention or well-being at a population level is contested and nuanced, with associations often small and causal direction unclear, so firm claims either way should be treated with care. But as a personal practice, deliberately leaving space for unstructured time seems a harmless experiment whose results each person can judge for themselves.

I’ll shift to a subject that has been brewing under many of these, and which I have been circling without naming directly: the idea of a skill, how expertise is acquired, and what deliberate practice means.

Anders Ericsson’s studies of violinists at a Berlin music academy, published in 1993, found that the best students had accumulated, by age twenty, around ten thousand hours of solitary practice, compared with about eight thousand for the next tier and four thousand for those aiming to be music teachers. This was popularized, and oversimplified, as the “ten thousand hour rule” in Malcolm Gladwell’s Outliers. Ericsson objected to the popularization on several grounds. The number was an average, not a threshold, and the best violinists had not all accumulated exactly that much. More importantly, what mattered was not hours of any practice but deliberate practice: focused, effortful activity at the edge of one’s ability, with specific goals, immediate feedback, and repeated correction of weaknesses, usually guided by a teacher who can design appropriate exercises. Merely playing through pieces you already know does little. A meta-analysis by Macnamara, Hambrick, and Oswald in 2014 found that deliberate practice explained a substantial but far from total share of the variance in performance, about a quarter in games, a fifth in music, and much less in education and in professions, indicating that other factors, including starting age, genetics, working memory, and sheer opportunity, also matter. The debate about talent versus practice is perhaps less a contest than a recognition that both contribute, interacting in ways that are not additive: ability influences how much people practice, how they respond to it, and whether they persist.

Chess has been a laboratory for expertise because it offers a clear measure, the Elo rating, and a known structure. Adriaan de Groot, in the 1940s, and later Chase and Simon, found that grandmasters did not search deeper than weaker players; instead, they recognized meaningful patterns. Shown a position from a real game for five seconds, masters could reconstruct nearly all of the pieces, while novices managed a handful. But when the pieces were arranged randomly, the masters’ advantage vanished, indicating that their memory was based on learned chunks, familiar configurations of pieces that carry meaning, estimated at fifty thousand or more. That is the secret of expertise in many domains: perception reorganized by experience, such that the expert literally sees different things. A radiologist glances at an X-ray and perceives an anomaly that a novice cannot find even when told it is there; a firefighter commander, in the studies of Gary Klein, senses that a building is unsafe without being able to articulate why, using recognition-primed decision making, in which the expert matches the situation to a prototype and mentally simulates the first option that comes to mind, rather than comparing many options. Intuition, on this account, is not mystical; it is recognition built through thousands of encounters. But Kahneman and Klein, in a notable collaboration, concluded that such intuition can be trusted only when the environment is sufficiently regular to be learned and the person has had adequate opportunity to learn it with feedback. Chess, firefighting, and anesthesiology, where feedback is swift and the world is relatively stable, support reliable intuitions; stock picking and long-term political forecasting do not, and in those domains confident intuitions deserve suspicion.

The ways in which humans learn motor skills are fascinating in their own right. A baby learning to walk fails thousands of times a day and does not give up, a feat of persistence that adults rarely match. Skill learning proceeds through stages, from a cognitive stage of effortful, error-prone attempts to an autonomous stage in which performance runs smoothly with little conscious attention, as described by Fitts and Posner. At the same time, there’s a well-known paradox of expert performance: overthinking can wreck it. “Choking under pressure” appears to result when a performer directs conscious attention to steps that are normally automatic, disrupting them, or when worry consumes working memory resources. Sian Beilock’s experiments with golfers found that novices putt better when they pay attention to their stroke, while experts putt better when distracted by an unrelated task or when forced to hurry, and worse when told to monitor their technique. The practical advice that follows is odd but consistent: for well-learned skills, focus on an external goal, such as the target, rather than on the movement of the body, a result confirmed in many studies by Gabriele Wulf on the benefits of an external focus of attention.

Sleep returns here as a skill-learning ally. Motor learning consolidates during sleep, particularly in stage two sleep with its bursts called spindles, and studies show that participants who sleep after learning a finger-tapping sequence improve overnight without additional practice, whereas those who stay awake for the same interval do not. The spacing effect, one of the most reliable findings in the psychology of learning, indicates that distributing practice over time produces better long-term retention than massing it into a single session, a pattern first documented by Hermann Ebbinghaus in the 1880s, who taught himself lists of nonsense syllables and measured how quickly he forgot them, deriving the forgetting curve, a steep initial drop followed by a gentler decline. Related is the testing effect: retrieving information from memory strengthens it more than rereading does, which is why self-quizzing and flashcards outperform passive review, a result replicated extensively by Henry Roediger, Jeffrey Karpicke, and others. And there’s the principle of desirable difficulty, articulated by Robert Bjork: conditions that make learning feel harder, such as spacing, interleaving different types of problems, and testing, often produce better retention and transfer, even though they feel less productive in the moment. This gap between how learning feels and how well it works leads students to favor ineffective strategies like highlighting and rereading, because fluency feels like mastery. An illusion of competence is a persistent hazard: when the material is in front of you and looks familiar, it is easy to mistake recognition for the ability to recall or produce it.

That relates to a broader cognitive bias, the Dunning-Kruger effect, in which people with limited competence in a domain tend to overestimate their ability, since the skills needed to perform well are often the same skills needed to evaluate performance. The original 1999 study found that participants scoring in the bottom quartile on tests of humor, grammar, and logic estimated themselves as well above average. The effect has been criticized on statistical grounds, since regression to the mean and a general tendency toward positive self-assessment can produce similar graphs without a special metacognitive deficit, and later analyses suggest the pattern is partly an artifact, though there’s still evidence that low performers have poorer insight into their errors. The popular version, in which fools are supremely confident and experts are racked with doubt, is too neat; what’s well supported is that calibration is hard, that feedback is the best corrective, and that having a sense of one’s own limits is itself a skill acquired through experience. Good forecasters track their own predictions and score them, which turns vague confidence into measurable accuracy, and learn from the discrepancies.

I want to dwell for a moment on the importance of teachers, as practice’s necessary partner. Benjamin Bloom’s 1984 “two sigma problem” reported that students tutored one on one using mastery learning techniques performed two standard deviations better than students in a conventional classroom, meaning the average tutored student outperformed ninety-eight percent of the control group. The challenge Bloom posed was to find methods of group instruction as effective as tutoring. Subsequent research has found that effect sizes from tutoring, while consistently positive and often large, are generally smaller than two sigma, and Bloom’s studies were small and short. But the core insight stands: individualized feedback, adaptive pacing, and ensuring mastery before advancing are powerful, and the best teachers do all three in some form, diagnosing the misconception behind an error rather than just correcting the answer. A teacher’s job, seen this way, is not mainly to transmit information, which books and videos can do, but to see what the student is thinking and to design the next step. Expertise in teaching, like expertise elsewhere, consists largely of pattern recognition, here of the many ways students go wrong. This is why subject mastery alone does not make a good teacher: experts often suffer from the “curse of knowledge,” in which they cannot easily recall what it was like not to know something, and so skip steps that were once hard and are now invisible to them. Elizabeth Newton’s tapping experiment at Stanford in 1990 captured it neatly: people asked to tap out the rhythm of a well-known song and predict whether a listener would identify it guessed that half would, but the actual rate was about two and a half percent, since the tappers heard the melody in their heads and could not imagine hearing only knocks.

The curse of knowledge links back to language and to writing, since clear writing is largely the art of overcoming it. Steven Pinker has argued that the main reason for bad prose is the failure to imagine what the reader doesn’t know, and that the cure is to write for a specific, intelligent reader who is smart but unfamiliar with your jargon, and to give concrete examples before abstractions. George Orwell’s 1946 essay on politics and the English language made a related argument, that vague, inflated, cliché-ridden prose is both a symptom and a cause of muddled thinking, and that slovenly language makes it easier to have foolish thoughts. His rules, such as never using a long word where a short one will do, and cutting any word that can be cut, are still excellent advice, though he himself prefaced them with the observation that one should break any of the rules sooner than say anything outright barbarous. Writing is thinking made visible; many writers report that they do not know what they think until they’ve tried to put it on the page, and a vague idea that seems fine in the head often collapses when forced into sentences. This is part of the reason that writing is a good tool for learning, and why, in the interest of being useful rather than merely long, this meandering stream has tried to land each digression on a concrete claim.

Having said that, I’ll go on, and take up a topic the thread of writing makes natural: the history of the book, and the odd technologies that preceded and followed the printed page.

Proceed

The book as we know it, a stack of bound pages you can flip through, is a surprisingly late and deliberate invention. For most of the ancient world, long texts lived on scrolls, rolled lengths of papyrus or parchment that you unwound with one hand and rewound with the other. A scroll is a linear medium. To find a passage you had to scroll to it, which is why the word survives in our software, and consulting two places at once was nearly impossible. The Iliad needed a scroll for each of its twenty-four books, and a library was a room of pigeonholes full of cylinders with tags dangling from the ends.

The codex, sheets folded and stitched along one edge, appeared in the first centuries CE and spread with striking speed among early Christians. By the fourth century it had largely displaced the scroll for literary use. Several explanations have been offered: a codex is compact, holds more text if both sides of the leaf are used, can be hidden in a cloak during persecution, and permits random access, so that a reader can jump to a verse. That last advantage matters for a community that cites scripture constantly. Roman jurists adopted it as well, because law is a text you consult rather than read straight through. The codex is, in effect, the first random-access memory device, and its triumph over the scroll is a good example of a technology winning by changing what readers could do rather than by looking impressive. Parchment, made from scraped and stretched animal skin, outlasted papyrus in Europe because papyrus, dependent on Egyptian reeds and brittle in damp climates, was costly and fragile. A single large Bible might consume the skins of hundreds of sheep or calves, so books were treasures, chained to desks in some libraries and sometimes valued like estates. Monastic scriptoria produced them by hand, and the scribes left in the margins a trail of complaints that survives like graffiti: “Thank God, it will soon be dark,” “My hand is cold,” “The parchment is hairy.” Another ingenious practice was the palimpsest, in which old writing was scraped off to reuse the skin, leaving ghosts of the earlier text. Modern multispectral imaging has recovered extraordinary things from them. The Archimedes Palimpsest, a tenth-century copy of treatises by Archimedes, including the only surviving Greek text of the Method, was overwritten in the thirteenth century by a monk copying a prayer book. It was sold, damaged by mold, even decorated with forged paintings in the twentieth century, and then painstakingly imaged between 1999 and 2008, revealing Archimedes’ reasoning about infinitesimals and counting arguments that anticipate integral calculus. The scraping that nearly destroyed the work also, by an accident of reuse, preserved it. Paper came from China, traditionally credited to Cai Lun around 105 CE, though archaeological finds show paper existing earlier. Its route west is partly documented: Arab forces defeated a Tang army at the Battle of Talas in 751, and later accounts say captured papermakers taught the craft in Samarkand. Whatever the truth of that story, paper mills spread through Baghdad, Damascus, Cairo, and Moorish Spain, and reached Christian Europe by the twelfth century. Paper was cheaper than parchment, and its abundance made possible the vast bureaucratic and scholarly culture of the Abbasid caliphate, including the great libraries of Baghdad. Woodblock printing, also from East Asia, produced the oldest dated printed book, the Diamond Sutra of 868, and by the eleventh century Bi Sheng had invented movable type from baked clay, followed by metal type in Korea. The Korean Jikji, printed in 1377, is the oldest surviving book printed with movable metal type. So Gutenberg’s innovation in Mainz around 1450 was not the invention of printing so much as a successful system for printing an alphabetic script at scale, combining an oil-based ink that stuck to metal, a hand mold for casting uniform type, a screw press adapted from wine and olive presses, and a business model, financed by loans from Johann Fust, that ended in a lawsuit and Gutenberg’s loss of his equipment. Why did movable type matter so much more in Europe? An alphabet of a few dozen letters is an ideal fit for movable type, whereas Chinese requires thousands of characters, which made woodblocks competitive for centuries. Within fifty years of Gutenberg there were presses in hundreds of European towns, and by 1500 perhaps twenty million volumes had been printed. Elizabeth Eisenstein argued in The Printing Press as an Agent of Change that print standardized texts, enabled the accumulation of corrections, spread scientific tables and diagrams that could be reproduced exactly, and facilitated the Reformation: Luther’s pamphlets sold in the hundreds of thousands, and his German Bible helped fix a standard written German. Not everything about the claim is uncontroversial. Historians note that early printed books resembled manuscripts, that errors were also reproduced en masse, and that print spread rumor and pamphlet quarrels as readily as truth. The witch-hunting manual Malleus Maleficarum, printed in 1486, circulated widely thanks to the press. Every information technology amplifies whatever people want to say, and early modern Europe was no different from our own century in that respect. Print also changed the shape of the page. Early manuscripts ran text together, with few spaces, because scribes saw reading as an oral performance. Word separation became common in the early Middle Ages, notably through Irish scribes learning Latin as a foreign language who found the unfamiliar words hard to parse. Punctuation, page numbers, tables of contents, indexes, chapter headings, and title pages, all the apparatus we take for granted, developed gradually, and Aldus Manutius in Venice contributed the semicolon in its modern form, italic type, and the small octavo format that made books portable, the ancestor of the paperback. Silent reading, which seems the natural way to read, was unusual in the ancient world; Augustine remarked with amazement on seeing Ambrose read without moving his lips or voice, though scholars debate how rare it really was. The shift from reading aloud to reading silently changed what books could be: private, subversive, intimate. The book then proved to be a durable and adaptable form, and also a vulnerable one. Acidic paper made from wood pulp, adopted widely in the nineteenth century, is slowly destroying itself; libraries estimate that a substantial fraction of books from roughly 1850 to 1950 are embrittled and crumble when handled. This “slow fires” crisis spurred deacidification programs and the move to permanent alkaline paper. The medieval parchment manuscript, whose life is measured in a thousand years, is in some respects more durable than the pulp paperback on the shelf. The tradeoff between cost and permanence recurs again and again: the technology that democratizes access often sacrifices longevity. Electronic books have so far supplemented rather than replaced print in most markets, which surprised many forecasters. Sales of e-books grew quickly after the Kindle arrived in 2007, then plateaued, and print has held a steady, even resurgent share, with independent bookstores recovering somewhat in the 2010s. Studies of reading comprehension on paper versus screens find a modest advantage for paper, particularly for long, informational texts and under time pressure, though results vary and the gap may reflect habits of skimming on screens more than anything inherent. The physicality of a book, its heft, the spatial sense of being a third of the way through, may support memory by giving texts a location. The codex has now persisted for roughly two thousand years, and its survival suggests that some designs reach a kind of local optimum: a hand-sized, flip-through object that needs no power and degrades gracefully. Libraries are the institutions around books, and they are stranger and more varied than their quiet image suggests. The House of Wisdom in Baghdad, if it was the grand academy of legend rather than a more modest collection, supported translation of Greek, Persian, and Indian works into Arabic, and the translation movement preserved and extended much of ancient science, including Galen, Ptolemy, and Euclid; al-Khwarizmi’s treatise on solving equations by restoring and balancing gave us the word algebra, and his name gave us algorithm. Timbuktu’s manuscripts, hundreds of thousands in private family collections, survived centuries of desert climate and, in 2012 to 2013, a jihadist occupation, thanks partly to a clandestine effort by librarians and townspeople who smuggled thousands of them out in footlockers to Bamako. Benjamin Franklin and his friends founded the Library Company of Philadelphia in 1731, pooling subscriptions to buy books none of them could afford alone, a model for the subscription library that led eventually to free public libraries. Andrew Carnegie financed more than two thousand library buildings across the English-speaking world, on the condition that the communities committed to maintaining them, an arrangement that reflected his belief in self-improvement through reading. The public library is among the quietly radical institutions of modern life: a place where anyone can borrow knowledge at no charge, funded collectively, whose value is hard to capture in a market price. Cataloguing, the unglamorous craft that makes libraries usable, is itself a chapter in the history of ideas. Callimachus compiled the Pinakes, a catalog of the Library of Alexandria, in the third century BCE. Melvil Dewey devised his decimal classification in 1876, dividing knowledge into ten classes, each subdivided by tens, a scheme of remarkable practical power and obvious cultural bias, with the bulk of the religion class given to Christianity. The Library of Congress system, built for a large national collection, uses letters and numbers and is more flexible. Every classification imposes a worldview, and the fights over where to shelve a topic show how contested our categories are. Borges’s “Library of Babel,” imagining a library containing every possible book of a fixed length with all combinations of letters, is the nightmare opposite: all the knowledge is there, including every truth, but it is drowned in an ocean of gibberish, with no catalog that could be trusted. In a sense, the problem of the library is the problem of selection, and what makes a collection valuable is as much what it excludes as what it holds. That points to the reader. Reading is one of the most remarkable feats of cultural reprogramming the brain performs, given that it is only a few thousand years old and thus too recent for specific evolutionary adaptation. Stanislas Dehaene’s “neuronal recycling” hypothesis proposes that literacy repurposes cortical regions evolved for object and face recognition, in particular an area in the left ventral occipitotemporal cortex nicknamed the visual word form area, which becomes tuned to the letter shapes of the reader’s script. Learning to read changes the brain: literate adults show differences in visual processing, and in phonological awareness, compared with otherwise similar illiterate adults. Writing systems that differ in how transparently spelling maps onto sound, such as Finnish or Italian, which are highly regular, versus English, which is notoriously irregular, produce differences in how quickly children learn to read, with English-speaking children taking measurably longer to reach fluency. Dyslexia, a difficulty with decoding words that affects perhaps five to ten percent of people, is more prevalent and more obvious in irregular orthographies, and brain imaging suggests differences in the networks linking visual and phonological areas. Reading, like music, is a culturally acquired skill built on neural machinery assembled for other purposes, and its fragility, the way it depends on years of instruction, is easy to forget for those who learned it so early. All of this makes the next subject natural, since what we read shapes what we believe, and how we come to believe things deserves its own long look, starting with the odd reliability of testimony. Proceed Most of what you believe, you believe because someone told you. You have never measured the distance to the moon, seen an electron, or checked that Australia exists by going there. You accept the boiling point of water, the date of the French Revolution, the existence of your own grandparents’ childhood homes, all on the word of others. Philosophers call this the problem of testimony, and it is an embarrassment for any theory of knowledge that insists on personal verification. Hume, who was suspicious of most things, argued in his essay on miracles that we should proportion belief to evidence and that testimony is only as good as our experience of its reliability. But he also recognized that we have no choice but to rely on it, since most of the experience on which our judgments rest was handed to us secondhand. The alternative of trusting only what you personally verified would leave you knowing about as much as a feral child. The interesting question is not whether to trust but how to calibrate trust, and here developmental psychology has something surprising to report. Children are credulous in a structured way. Experiments by Paul Harris, Melissa Koenig, and others show that even preschoolers track who has been right before, prefer information from confident speakers, and discount informants who have made errors in naming familiar objects. A four-year-old who watches an adult mislabel a ball as a shoe becomes less willing to accept that adult’s name for a novel gadget. Children also attend to whether a speaker is in a position to know: they trust someone who saw the hidden object over someone who had their eyes covered. This epistemic vigilance, as Dan Sperber and colleagues call it, suggests that human communication evolved alongside mechanisms for filtering it. If we believed everything we were told, liars would exploit us without limit; if we believed nothing, language would be useless. Equilibrium requires listeners to be selectively gullible and speakers to pay a cost for being caught out. One of the most reliable biases in that filtering is the truth default. Timothy Levine’s research on deception finds that people, absent specific suspicion, assume others are honest, and that this is usually a sound policy because most communication most of the time is truthful. The downside is that people are poor lie detectors. Meta-analyses of hundreds of studies, notably by Bond and DePaulo, put average accuracy at about fifty-four percent, barely above a coin flip, and professionals such as police officers and customs agents do little better, though they are often more confident. The folk cues, gaze aversion and fidgeting, turn out to be weak or irrelevant. What does help is content: checking statements against known facts, asking for unexpected details, and noticing inconsistency over time. The lesson is that the reliability of testimony rests less on our ability to read faces than on institutions and structures that make lying costly and checkable: records, witnesses, reputations, cross-examination. Science itself is a formalization of that insight. A scientific paper is testimony, but testimony of a particular kind, accompanied by methods, data, and the expectation of replication, so that the reader can in principle check. The motto of the Royal Society, founded in 1660, is Nullius in verba, take nobody’s word for it, though in practice the society’s members, like all scientists, took an enormous amount on trust. Steven Shapin’s A Social History of Truth argues that in seventeenth-century England, credibility attached to gentlemen because their independence was supposed to guarantee honesty; Robert Boyle’s air-pump experiments were witnessed by respectable observers whose signatures vouched for the results, a practice Shapin and Schaffer call virtual witnessing, since Boyle’s detailed descriptions were meant to make readers feel they had been present. Today the modern equivalents are peer review, data sharing, and preregistration, and when they fail, as in famous fraud cases, such as Diederik Stapel’s fabricated social psychology studies or the retracted 1998 paper falsely linking vaccines to autism, the damage spreads precisely because everyone downstream was relying on the testimony. The spread of falsehood has its own dynamics. A large study of Twitter by Vosoughi, Roy, and Aral, published in 2018, found that false news stories were about seventy percent more likely to be retweeted than true ones and reached people several times faster, and that the effect was driven by humans, not bots, with false stories scoring higher on novelty and eliciting more surprise and disgust. Novelty draws attention and sharing signals that one has inside information. Yet some of the popular fear about misinformation outruns the evidence: most people’s news diets are not dominated by fake news, exposure is concentrated among a small minority of heavy consumers, and misinformation often acts less by changing minds than by reinforcing what people already want to believe. Hugo Mercier’s argument in Not Born Yesterday is that humans are not especially gullible, that people are far more resistant to persuasion than commonly imagined, and that the major problem is not credulity but a failure to accept true information from sources whose interests are opposed to one’s own. Rumors that spread successfully tend to be those that people already find plausible and useful, such as those confirming a prior suspicion about a rival group. On this view the question to ask about a bogus claim is less “why did they believe it?” than “what did the belief do for them?” The long history of hoaxes supports a certain humility. In 1835, the New York Sun published a series of articles claiming that the astronomer John Herschel had discovered life on the moon, complete with bat-winged humanoids and unicorns, observed through a giant telescope. It sold enormously. Orson Welles’s 1938 radio dramatization of The War of the Worlds is often said to have caused mass panic, but historians such as Jefferson Pooley and Michael Socolow have argued that the panic was largely a newspaper exaggeration, since the newspapers, threatened by radio, had an interest in portraying the new medium as dangerous. The legend of the panic is itself a case study of a false story that spread because it was satisfying and served interests. The Piltdown Man, a supposed missing link unearthed in England in 1912 and exposed as a forgery in 1953, composed of a medieval human skull and an orangutan jaw stained to look ancient, fooled experts for forty years, partly because it flattered British hopes that humanity had originated in England and partly because it matched what experts expected to see. Frauds succeed by fitting the shape of what people already wish to find. What protects us, when anything does, is a division of epistemic labor with checks. Consider Wikipedia, a project that experts initially expected to be a disaster. It operates on rules requiring citation to reliable sources, on transparent edit histories, on talk-page deliberation, and on thousands of watchful volunteers, and studies comparing its accuracy on scientific topics with traditional encyclopedias found comparable error rates, though coverage is uneven and contested political topics are battlegrounds. The encyclopedia’s reliability is the product of structure: the norms and tools that make it cheap to correct errors and expensive to sustain them. Likewise, markets for information such as prediction markets use financial stakes to coax honest probabilities, and their forecasts often beat polls and pundits, since traders who are wrong lose money. All of these are ways to make testimony accountable. This leads to the strange case of expertise, and the question of when to defer to experts. The philosopher Alvin Goldman posed the “novice-expert problem”: how can a layperson judge which of two conflicting experts to trust, when the person lacks the expertise to evaluate their arguments? Goldman suggested indirect cues: track records, agreement among other experts, evidence of bias or conflicts of interest, and the ability to articulate arguments that survive rebuttal. For a layperson the best single heuristic is usually the consensus of the relevant scientific community, since a consensus built through many independent checks is much harder to fake than a single voice. This is imperfect, since consensus has sometimes been wrong, as with stomach ulcers, which Barry Marshall and Robin Warren showed in the 1980s to be caused by the bacterium Helicobacter pylori, upending a belief in stress and acid, and which won them a Nobel Prize. But the cases of lone dissenters being vindicated are vastly outnumbered by those of lone dissenters being mistaken, and the vindicated ones typically succeeded by producing evidence that persuaded the field rather than by simply insisting on being persecuted. Galileo is invoked by every crank, but, as the saying goes, they laughed at Galileo, and they also laughed at Bozo the Clown. Such is the delicate machinery of knowledge: partial, social, and fallible, yet capable of correcting itself over time. A related puzzle sits underneath it, which is how any group of fallible people can arrive at beliefs better than any member holds. Condorcet’s jury theorem, from 1785, offers a clean mathematical statement: if each voter is more likely than not to be right on a yes-or-no question, and votes independently, then the probability that the majority is right grows toward certainty as the group grows. Flip the assumption, so that each voter is more likely wrong, and the majority becomes reliably wrong. The theorem puts into formal terms what I said earlier about independence and diversity, and it shows how fragile collective wisdom is to shared bias: if everyone’s errors are correlated because they all read the same sources, the large number of voters buys little. That is why the structure of information flow matters as much as the number of participants, and why the best institutions deliberately build in dissent, independent review, and adversarial testing, such as the devil’s advocate, the red team, the opposing counsel, and the second referee. The legal system is perhaps the most developed machinery we have for testing testimony. The adversarial trial pits two sides against each other, allows cross-examination of witnesses, imposes rules of evidence that exclude hearsay and unreliable sources, and requires a burden of proof, higher in criminal than civil cases. William Blackstone’s maxim that it is better that ten guilty persons escape than that one innocent suffer expresses a deliberate asymmetry in how errors are weighed. Yet the system has been jolted by findings about its own reliability. Eyewitness misidentification has been a factor in about seventy percent of the convictions later overturned by DNA evidence in the United States, according to the Innocence Project, and the work of Elizabeth Loftus and others on malleable memory has led to reforms in how lineups are conducted, such as double-blind administration and recording the witness’s confidence at the time of the first identification. Forensic techniques once presented as infallible, including bite-mark analysis and certain kinds of hair comparison, were criticized in a 2009 National Academy of Sciences report as lacking scientific validation. The law, in short, learned about its own error rates only when an independent test, DNA, supplied a standard against which to check it, which is a vivid illustration of why we need ground truths wherever we can find them. Which brings me to measurement itself as an antidote to testimony, and I’d like to spend the next stretch on the history of statistics, which is the discipline devoted to learning from imperfect evidence. It begins with an unlikely pair of founders: John Graunt, a London haberdasher, who in 1662 analyzed the weekly Bills of Mortality and noticed regularities, such as the steady excess of male births and the stable proportions of deaths from various causes, and Edmond Halley, of comet fame, who used Breslau’s mortality records to construct one of the first life tables, which made life annuities and insurance calculable. The mathematics of chance had grown out of gambling problems posed to Pascal by the Chevalier de Méré in 1654, whose correspondence with Fermat laid the foundations of probability theory. The first problem, how to split the stakes of an interrupted game fairly, is a lovely one: the answer depends not on what has happened but on what could still happen, on the probabilities of the possible futures, and that shift from history to prospects is the conceptual leap on which all risk calculation rests. Over the following two centuries the subject accumulated its tools. Jacob Bernoulli proved the law of large numbers in a work published in 1713, showing that as the number of trials grows, the observed frequency of an event converges on its probability. Abraham de Moivre and then Laplace and Gauss developed the normal distribution, the bell curve, which arises whenever many small independent influences add together, as the central limit theorem would later state in general form. Gauss used least squares to predict the orbit of the dwarf planet Ceres after it was lost in the sun’s glare in 1801, a stunning demonstration that noisy observations could be combined to pin down a trajectory. Adolphe Quetelet, a Belgian astronomer turned social scientist, applied the bell curve to human traits and conceived the “average man,” an idea that seeded both sociology and a long debate about whether averages describe anyone. Francis Galton, Darwin’s cousin, discovered regression to the mean while studying heights of parents and children: very tall parents tend to have children who are tall but less extreme, and very short parents have children closer to average. This is not a force pulling individuals toward the middle but a statistical consequence of imperfect correlation, and failure to grasp it produces endless false causal stories, such as the observation that pilots praised after an excellent landing tend to do worse next time, while those scolded after a poor one improve, leading instructors to conclude that criticism works and praise backfires, when performance was simply drifting back toward each pilot’s typical level. Galton, regrettably, also founded eugenics, and the early history of statistics is entangled with it. Karl Pearson and Ronald Fisher, giants who developed correlation, chi-squared tests, analysis of variance, and the design of experiments, were both committed eugenicists, and their statistical innovations were in part motivated by the wish to measure heredity. The fact deserves to be stated plainly and not airbrushed: powerful tools were forged in the service of ideas now repudiated, and the tools survived while the ideology was discredited, which is both a warning about how motivated reasoning can ride on mathematics and a reminder that a method can be sound even when its creators’ purposes were not. Fisher’s experimental design, with randomization, replication, and blocking, which he developed at the Rothamsted agricultural station in the 1920s, remains the basis of modern trials. The famous story of the lady tasting tea, in which a woman claimed to tell whether milk or tea was poured first into the cup and Fisher worked out how to test her claim, is told in his book The Design of Experiments: with eight cups, four of each kind, randomly ordered, the chance of identifying all correctly by luck is one in seventy. The design determines what the data can tell you before a single cup is poured. The p-value, much maligned, is a descendant of that style of reasoning. It answers a specific question: assuming there’s no real effect, how likely is a result at least as extreme as the one observed? A small p-value means the data would be surprising under the null hypothesis, not that the null hypothesis has a small probability, a common and consequential confusion. The conventional threshold of 0.05 is arbitrary, owing a good deal to a passing remark by Fisher that it was convenient, and treating it as a cliff, with results just under 0.05 declared real and those just over declared null, has distorted the literature. In 2016 the American Statistical Association issued a statement warning against such use, and some researchers have urged lowering the threshold or abandoning significance language in favor of estimates and intervals. The rival Bayesian tradition, descended from Thomas Bayes’s posthumously published 1763 essay and developed by Laplace, treats probability as a degree of belief and updates it with evidence according to Bayes’ theorem: the posterior is proportional to the prior times the likelihood. Bayesian methods answer the question people actually want answered, how probable is this hypothesis given the data, at the cost of requiring a prior, which critics call subjective and defenders call honest. For about a century frequentist methods dominated, but computing advances in the 1990s, especially Markov chain Monte Carlo, made Bayesian analysis practical for complex models, and it’s now widely used from epidemiology to machine learning. The two camps have mostly settled into coexistence, with many working statisticians using whichever tool fits the problem, and the sharpest disagreements often concern what counts as a legitimate prior or a fair test. Bayes’ theorem has a pleasing everyday form, which I’ll use to reconnect with testimony. Suppose a friend tells you a surprising claim. How much should you believe it? Your posterior depends on the prior plausibility of the claim and on the likelihood that your friend would say it if it were true compared with if it were false. If the claim is extraordinary, say, that a coin landed on its edge, then even a reliable friend’s report may leave you doubtful, because the prior is tiny and the chance that the friend is mistaken or joking, though small, still exceeds it. That is the logic behind Laplace’s dictum, which Carl Sagan popularized as “extraordinary claims require extraordinary evidence.” It also explains why a single dramatic study should move us less than a replicated body of evidence, and why a claim consistent with well-established theory needs less support than one that overturns it. Hume’s argument about miracles has this structure: the testimony must be more improbable to be false than the event to be true. Whether or not one accepts his conclusion for miracles, the form of the reasoning is just Bayesian, and a person who updates in proportion to evidence, neither dismissing the surprising out of hand nor swallowing it whole, is doing about as well as a finite mind can. I’d like to carry this into a practical domain where the stakes of calibrated belief are literal: weather forecasting, one of the success stories of quantitative prediction. A modern five-day forecast is as accurate as a one-day forecast was in 1980, a gain of about a day per decade, thanks to better satellites, denser observations, faster computers, and above all data assimilation, the mathematics of blending a model’s prediction with fresh measurements. The atmosphere is chaotic, a fact established by Edward Lorenz in 1961 when he reran a simulation with inputs rounded to three decimal places instead of six and found that the resulting weather diverged completely, the discovery that gave us the butterfly effect, named after the title of his later talk asking whether a butterfly’s flap in Brazil could set off a tornado in Texas. Chaos limits deterministic forecasts to roughly two weeks no matter how good the models become, because tiny errors in the initial state grow exponentially. Forecasters respond with ensembles, running a model dozens of times from slightly perturbed starting points and reading the spread as a measure of uncertainty. When you hear a seventy percent chance of rain, this is well-calibrated: across many days with that forecast, it should rain on about seventy percent of them, and the best forecasting services do hit this mark closely. Nate Silver, in The Signal and the Noise, held weather forecasting up as a model of a field that took probabilistic honesty seriously and improved through feedback and competition, in contrast with fields like earthquake prediction, where decades of effort have produced no reliable short-term forecasts. Earthquakes, in fact, show the limits starkly. Seismologists can say that a given fault segment has some probability of rupturing over thirty years, and they have excellent knowledge of the long-term rate of quakes, which follows the Gutenberg-Richter law: each decrease of one unit in magnitude brings roughly ten times as many events, a power-law relationship that implies no characteristic scale. But predicting when a specific quake will strike, within days, has defeated every attempt. The 1975 evacuation of Haicheng in China, based on foreshocks, saved lives when a magnitude 7.3 quake struck, but the following year the Tangshan quake killed a quarter of a million people with no warning. In 2009, six Italian scientists and officials were convicted of manslaughter after the L’Aquila earthquake, for allegedly giving falsely reassuring information before the disaster, a verdict that alarmed researchers worldwide, though the scientists were later acquitted on appeal, and the case is more accurately about faulty risk communication than punishment for failing to predict. Early warning systems, which detect the fast but weak P waves and issue alerts seconds before the damaging S waves arrive, now operate in Japan, Mexico, and on the US West Coast, and even a few seconds can allow trains to slow and surgeons to pause. That kind of modest, honest capability is perhaps what a science should offer when the grander promise is out of reach.

Each of these examples shows the same underlying discipline: say what you know, say how well you know it, and track whether you were right. It is a simple rule and an enormously difficult practice. It governs how a good doctor communicates risk, how a good scientist reports a result, and how a good institution makes decisions under uncertainty. It is also, I think, the best single answer to the problem with which I began, the problem of trusting what we cannot check: trust sources that are accountable, that state their uncertainty, that welcome checking, and that have been right before. And with that, I’ll turn to the next subject, which has been waiting in the wings: the history and strangeness of money, the most successful shared fiction in human life, and the trust on which it entirely depends.

Money is a shared fiction that works because nearly everyone acts as though it is real, and the fiction is older and stranger than the coins in your pocket suggest. The textbook story goes: barter was clumsy because it required a “double coincidence of wants,” so people gradually settled on a commodity everyone would accept, and money emerged. It is a tidy story, and anthropologists have been unable to find the society where it happened. David Graeber, in Debt: The First 5,000 Years, marshals the evidence that credit came first. In small communities, people kept informal accounts of who owed what, and the exchange of goods between strangers happened mostly at the margins, often between people who were potentially hostile. Barter, on this view, is what happens when people who lack mutual trust or a shared system of credit need to trade, and it shows up chiefly after the collapse of a monetary system, as in Russia in the 1990s or among prisoners of war, whose cigarette economies were famously documented by R. A. Radford in a 1945 account of a German camp.

The earliest records support a credit-first reading. The Mesopotamian temples kept ledgers in silver and grain, and the shekel was originally a unit of weight, about eight grams of barley’s equivalent, and later of silver, long before there were coins. Prices were fixed administratively, a bushel of barley equal to a shekel of silver, and debts were recorded on clay tablets, with the interest rate customarily one-sixth per year on silver loans and a third on grain. Because debts could pile up until farmers lost their land or sold their children into bondage, Mesopotamian kings periodically proclaimed clean-slate edicts, called misharum or andurarum, cancelling consumer debts and freeing debt slaves. The tradition survives in the biblical jubilee, in which land returns to its original families every fiftieth year. These were acts of political prudence: a society in which debts ratchet upward without relief ends up with a population of landless dependents and a ruler whose army has no free soldiers. That the oldest known legal and economic documents already grapple with debt crisis suggests how basic the problem is.

Coined money arrives later, around 600 BCE in the kingdom of Lydia in western Anatolia, in the form of small lumps of electrum, a natural alloy of gold and silver, stamped with a lion’s head. Coins spread rapidly through the Greek world, and Graeber and others argue that the real driver was war: soldiers needed to be paid and fed, and a ruler who minted coins and demanded taxes payable only in them created a market where none had existed, since the soldiers’ coins would be accepted by local people who needed them to pay the tax. Imposing a tax payable in your own currency is a clever way to make the currency valuable, which is the core of the chartalist theory of money, associated with Georg Friedrich Knapp and, in its modern form, with Modern Monetary Theory: money’s value rests on the state’s willingness to accept it for obligations to the state. Critics point out that this isn’t the whole story, since money circulated widely in places with weak states and since the value of any currency also depends on expectations about its future supply.

The debasement of coinage is the original inflation. Roman emperors reduced the silver content of the denarius from nearly pure in the first century to a few percent by the late third, paying armies with ever thinner coins, and the resulting price rises contributed to the crisis of that century. Diocletian’s price edict of 301 tried to fix prices by decree and failed. Medieval monarchs clipped and recoined, and Henry VIII’s “Great Debasement” of the 1540s cut the silver in English coins by more than half, earning him the nickname “Old Coppernose” since the silver wore off the high points of the king’s portrait, notably the nose. Out of this tradition came Gresham’s law, the observation that bad money drives out good: if two coins have the same legal value but different metal content, people hoard the heavier and spend the lighter, so the circulating coins deteriorate. The law fits any case where a fixed exchange rate undervalues one form of money, and it was a nuisance of every metallic standard.

Paper money came first in China, where merchants in Sichuan issued receipts for deposited coins around the tenth century, and the Song government took over the practice, issuing official notes. Marco Polo marveled at Kublai Khan’s paper currency, which was backed by the state’s authority and, notably, the Yuan dynasty’s strictures against refusing it. The Chinese experience also supplied the first lessons in hyperinflation: when governments printed notes to cover deficits, as the Yuan and later the Ming did, the notes lost value, and the Ming eventually abandoned paper for silver. In Europe, banking developed from the activities of goldsmiths, who issued receipts for stored gold that began to circulate as money, and who realized that, because depositors rarely all came for their gold at once, they could lend out more than they held. This is fractional reserve banking, the central magic and central hazard of finance. It creates money by lending, and it creates the possibility of a bank run, in which fear that the bank cannot pay causes the withdrawals that make it unable to pay. Diamond and Dybvig formalized this in 1983: a bank run is a self-fulfilling prophecy, with two equilibria, one in which everyone stays calm and the bank is sound, and one in which everyone panics and it is not. Deposit insurance, introduced in the United States in 1933, removed the incentive to run by guaranteeing deposits, and it worked so well that bank runs of the old kind became rare until the digital run on Silicon Valley Bank in March 2023, when depositors withdrew some forty-two billion dollars in a single day through their phones, a reminder that technology changes the speed of the old dynamics but not their nature.

Central banks are the institutions evolved to manage that fragility. The Bank of England, founded in 1694 to lend money to the Crown, gradually took on the role of lender of last resort, formalized in Walter Bagehot’s 1873 dictum that in a panic a central bank should lend freely, to solvent institutions, against good collateral, at a penalty rate. The Federal Reserve was created in 1913 after the Panic of 1907, which J. P. Morgan had personally ended by locking bankers in his library until they agreed to a rescue, a solution that demonstrated both the need for a systematic backstop and the dangers of relying on a single private individual. During the Great Depression the Fed failed in the very task Bagehot described, allowing thousands of banks to fail and the money supply to shrink by about a third, a mistake that Milton Friedman and Anna Schwartz documented in A Monetary History of the United States and that Ben Bernanke, a scholar of the Depression, vowed not to repeat when he led the Fed through the 2008 financial crisis. The gold standard, which tied currencies to the metal, constrained governments from printing at will but also transmitted deflation and forced countries into recession to defend their parity; Barry Eichengreen showed that the countries that left gold earliest in the 1930s recovered earliest. The Bretton Woods system, devised in 1944, tied currencies to the dollar and the dollar to gold at thirty-five dollars an ounce, until Nixon closed the gold window in 1971. Since then the world’s currencies have been fiat money: valuable because governments say so, because taxes and contracts are denominated in them, and because people expect others to accept them.

Inflation targeting is the modern discipline replacing the gold standard. Many central banks aim for roughly two percent inflation, adjusting interest rates to cool or stimulate the economy. The 1970s, when inflation in the United States and Britain reached double digits, taught that expectations matter: if people expect rising prices, they demand higher wages and raise prices, making the expectation self-fulfilling, and breaking it required Paul Volcker’s savage interest rate increases of 1979 to 1982, which pushed the federal funds rate near twenty percent and unemployment above ten percent. Extreme cases show what happens when confidence is lost. Weimar Germany’s hyperinflation of 1923 saw prices double every few days, with workers paid twice daily and rushing to spend their wages before they lost value; Hungary in 1946 set the record, with prices doubling every fifteen hours; Zimbabwe in 2008 issued a hundred-trillion-dollar note. These collapses typically follow a government’s inability to fund itself by any means other than printing money, and they illustrate that the ultimate foundation of a currency is not gold or decree but a credible expectation that it will hold its value. The return of inflation in 2021 to 2023, reaching around nine percent in the United States and higher in Europe and elsewhere, after decades of quiet, revived both the old arguments about supply shocks versus demand and the old fear of expectations becoming unanchored; central banks raised rates sharply, and inflation fell without the deep recession many predicted, a result economists are still examining.

The newest chapter is cryptocurrency, which began in 2008 with the pseudonymous Satoshi Nakamoto’s Bitcoin white paper. Its technical achievement was a solution to the double-spending problem without a trusted intermediary: a distributed ledger, the blockchain, maintained by competing participants who expend computational work to add blocks, with the longest chain accepted as truth, so that rewriting history would require outspending the honest majority. Whatever one thinks of it as money, and it has proved volatile and used chiefly for speculation, the design is a clever application of incentives, a version of the repeated-game logic from earlier. Critics point to its energy consumption, comparable to a mid-sized country’s under proof of work, to the prevalence of fraud, collapses like FTX in 2022, and to the paradox that a currency valued at wildly fluctuating prices serves poorly as a medium of exchange or a store of value. Supporters see a hedge against government debasement and a platform for programmable finance. Ethereum’s 2022 shift to proof of stake cut its energy use by over ninety-nine percent, showing the design space is wider than the original. Stablecoins, tokens pegged to the dollar, have found real use for cross-border transfers, and central banks are exploring digital currencies of their own. What the episode demonstrates, whichever way it ultimately goes, is how money has always been a technology of trust, with its forms depending on whom people are willing to trust and for what.

That insight places money alongside language, law, and time zones, as one more convention that works because we all believe it does. Yuval Noah Harari calls such constructs intersubjective realities, things that exist in the shared beliefs of many people: nations, corporations, human rights, and money. They are not illusions, since they have real power, but they are not physical either; a corporation can be dissolved by a change of mind, as can a currency. Anthropologists have documented extreme examples, such as the stone money of Yap, enormous limestone discs, some twelve feet across, quarried on distant Palau and used for large transactions. Ownership of a stone could be transferred by agreement without moving it, and the story goes that one stone lost at sea in transit was still treated as valuable, since everyone agreed it existed. Milton Friedman wrote about Yap in 1991 as a model of a purely ledger-based money. The details of the Yapese case are disputed, but the point lands: money’s substance is the ledger of mutual recognition, and the stone is a token.

And with that, I’ll pick up a thread that money and credit make unavoidable, the economics of risk and insurance, and the odd ways humans handle the possibility of loss.

Proceed

Insurance is one of the oldest ways humans have found to turn a private catastrophe into a manageable cost, and its basic move is so simple it’s easy to miss: many people each pay a small, certain amount so that the few who suffer a large loss can be made whole. Nothing is created. Risk is not eliminated; it is spread, and the spreading works because of the law of large numbers. No one can predict whether a given house will burn this year, but an insurer who covers ten thousand houses can predict, with surprising accuracy, how many will, and can set premiums accordingly.

The ancestors of the practice are old. Merchants in the ancient Mediterranean used bottomry loans, in which a lender advanced money against a ship’s voyage and was repaid, with steep interest, only if the ship arrived. The borrower’s debt vanished with the vessel, so the loan doubled as insurance, and the high rate was the premium. Modern marine insurance grew in the coffee houses of seventeenth-century London, especially the one run by Edward Lloyd near the Tower, where shipowners seeking cover circulated a slip of paper and individual backers wrote their names beneath the risk they were willing to carry, which is why we still call them underwriters. Lloyd’s of London grew from that room into a market of syndicates. Fire insurance arrived after the Great Fire of 1666 destroyed most of the old city, when Nicholas Barbon opened an office to insure houses, and insured buildings were marked with metal plaques so that company fire brigades would know which blaze to fight. Brigades reportedly sometimes let uninsured buildings burn, a vivid early illustration of how a market in protection can sort people into the covered and the exposed.

Life insurance needed mortality statistics, which is where Graunt and Halley, mentioned earlier, come back in. Early schemes charged everyone the same premium regardless of age, which meant the young subsidized the old and then stopped buying, leaving the insurer with the aging and unhealthy, a failure that anticipated a whole theory. The Equitable Society, founded in London in 1762 on the actuarial work of James Dodson, set premiums by age, and that put life insurance on a sound mathematical footing. The old tontines took a different route: investors paid into a pool and received annuities that grew as fellow members died, with the last survivors collecting the lot, a design that rewards longevity and, as critics noted, creates an unpleasant incentive for the members to wish their fellows ill. The French crown ran them in the seventeenth and eighteenth centuries to raise money. A distant relative survives in the French viager, in which a buyer pays an elderly owner a monthly sum until death, whereupon the property passes to the buyer. The most famous viager was a bet that went wrong for the buyer: a lawyer named André-François Raffray bought the Arles apartment of Jeanne Calment in 1965, when she was ninety, and he died in 1995, having paid far more than the apartment was worth, while Calment lived on until 1997 at the reported age of 122. The claim that she was that old has itself been disputed in recent years, but the story survives as a parable about longevity risk.

The deeper theory of insurance began with a question about decisions. Daniel Bernoulli, in 1738, took up the St. Petersburg paradox, a game in which a coin is flipped until it lands heads and the payout doubles with each tail, giving an infinite expected value and yet a game no sensible person would pay much to enter. His resolution was that the value of money to a person is not proportional to its amount: an extra thousand ducats matters far more to a pauper than to a rich man. This diminishing marginal utility explains insurance. A person whose house is worth most of their wealth faces a catastrophic fall in well-being if it burns, so they will rationally pay slightly more than the expected loss to avoid that risk, while an insurer with many customers, for whom each loss is small relative to its capital, is nearly indifferent to the gamble. Both sides gain from the trade, and the surplus is the value of risk spreading. When the logic is turned around, the same concavity predicts that people should not buy lottery tickets, and yet they do, one of the many things that classical expected utility could not accommodate.

Here Kahneman and Tversky’s prospect theory, published in 1979, enters. They found that people evaluate outcomes as gains and losses relative to a reference point, not as final wealth, that losses loom larger than equivalent gains by a factor of roughly two, and that small probabilities are overweighted while moderate and high ones are underweighted. The result is a characteristic fourfold pattern: risk aversion for likely gains, risk seeking for likely losses, and the reverse for unlikely events, which explains both why people buy lottery tickets and why they buy extended warranties on toasters. Maurice Allais had earlier, in 1953, constructed a pair of choices in which most people violate the axioms of expected utility, preferring a sure million over a gamble with a chance at five million but then, in a differently framed pair, preferring the riskier option, a discrepancy that exposed the special pull of certainty. Loss aversion helps explain why people cling to losing investments, why they refuse favorable small bets, and why a company’s customers react more strongly to a price rise than to an equivalent discount.

Bernoulli’s insight has a modern sequel in the question of what to maximize when you play repeatedly. John Kelly, a Bell Labs researcher working with Shannon’s ideas in 1956, showed that a gambler who wants to maximize long-run growth should bet a fraction of their bankroll proportional to their edge, neither more nor less, since overbetting leads to ruin even when each bet has positive expected value. Edward Thorp used the Kelly criterion in blackjack and then in markets, and with Shannon built a wearable computer to beat roulette in 1961. The idea has been generalized by Ole Peters and colleagues into what they call ergodicity economics, which stresses that the average across many people differs from the average over one person’s trajectory through time when growth is multiplicative. Take a bet that adds half your wealth on heads and takes forty percent on tails. The expected multiplier per flip is 1.05, so the ensemble average rises, but the typical multiplier over time is the geometric mean, about 0.95, so almost every individual who keeps playing sinks toward nothing while a few lucky ones become enormously rich and drag the average up. This fits an intuition that risk-averse behavior is not a mere bias: if you only get one life, avoiding ruin is rational even when the average looks attractive.

Insurance markets also break down in distinctive ways. George Akerlof’s 1970 paper on the market for lemons, which won him a share of the 2001 Nobel Prize, shows how asymmetric information can unravel a market. If sellers of used cars know quality and buyers do not, buyers will offer only the price for an average car, and sellers of good cars will withdraw, leaving a pool of worse cars, which lowers the price further, in a spiral. Health insurance has the same structure, called adverse selection: the people most eager to buy cover are those who expect to need it, so premiums must rise, which drives out the healthy, which raises premiums again. This is the reason many systems mandate participation or subsidize it. The companion problem is moral hazard, a phrase from the insurance trade itself, in which being protected from a loss changes behavior and so raises its likelihood. Kenneth Arrow’s 1963 paper on medical care laid out both. Insurers respond with deductibles, copayments, and exclusions, which keep policyholders with a stake in the outcome, but these shade into the dilemma that coverage meant to protect people from catastrophic expense also deters them from seeking care. The best-known claim about behavioral offsets is Sam Peltzman’s 1975 argument that seat belt rules were partly canceled by riskier driving, a result disputed by later studies, which generally find that safety rules do save lives on net even if some risk compensation occurs. The general point stands: protection alters incentives, and good design anticipates how.

Some risks are poor candidates for private insurance because they strike many people at once. Pooling works when losses are roughly independent; a flood, hurricane, or pandemic hits the whole pool simultaneously. Hurricane Andrew in 1992 destroyed so much of south Florida that about a dozen insurers failed, and it prompted the growth of catastrophe bonds, in which investors buy securities that lose principal if a disaster of a defined size occurs, transferring the risk to capital markets, which are far larger than any insurer’s balance sheet. Reinsurance does the same inside the industry, as insurers insure themselves. Flood cover in the United States is provided largely by a federal program, begun in 1968, because private insurers declined to offer it, and it has been criticized for subsidizing building in hazardous places and for repeatedly paying to rebuild the same flooded houses. Climate change is pressing on the whole model. As losses from wildfires, floods, and storms grow and become harder to price, insurers in some regions have raised premiums sharply or withdrawn, and state-backed insurers of last resort have become unexpectedly large. When the risk can no longer be priced affordably, the market sends a signal, though it may be an unwelcome one.

Insurance also has a moral history that is not always flattering. In 1781 the captain of the slave ship Zong, short of water, ordered some hundred and thirty enslaved Africans thrown overboard, and the owners claimed their value from insurers, who refused to pay. The ensuing case, Gregson v. Gilbert, was argued as a dispute about cargo, the judge, Lord Mansfield, observing that the case was the same as if horses had been thrown overboard. Abolitionists, notably Granville Sharp, publicized it, and it became a rallying point of the campaign mentioned earlier. The episode shows how the machinery of risk, with its neutral language of cargo, loss, and indemnity, can launder an atrocity, and it should make us wary of any system that translates human beings into line items.

The same pooling idea, applied at the scale of a nation, is social insurance. Bismarck’s Germany introduced sickness insurance in 1883, accident insurance in 1884, and old age and disability pensions in 1889, partly to blunt the appeal of socialism. The American Social Security Act followed in 1935. These programs pool risks that private markets handle poorly, such as living too long, becoming disabled, or losing a job in a downturn, and they avoid adverse selection by covering everyone. John Rawls offered a philosophical frame in 1971: imagine choosing the rules of society behind a veil of ignorance, not knowing your own place in it, and you would, he argued, choose arrangements that protect the worst off, much as you would buy insurance against the possibility of landing there. The thought experiment treats justice as the insurance policy that rational people would buy before knowing how the lottery of birth turns out. Critics reply that people differ in their appetite for risk and that a different bettor might prefer a society with greater rewards for the lucky, but the picture of a welfare state as mutual insurance remains one of the most influential in political thought.

Some of the hardest risks are the ones models handle worst. Standard financial risk measures assume that returns follow something like a bell curve, in which extreme moves are vanishingly rare, but real markets have fat tails, with large moves occurring far more often than the normal distribution allows. In 1998 the hedge fund Long-Term Capital Management, staffed by Nobel laureates and sophisticated traders, lost billions in weeks after Russia defaulted, and the Federal Reserve Bank of New York organized a private rescue to prevent a wider crash. In the run-up to 2008, rating agencies and banks leaned on the Gaussian copula, a formula popularized by David Li in 2000, to estimate how mortgage defaults would correlate, and it understated the chance that house prices would fall everywhere together, an assumption encouraged by the fact that they hadn’t in the historical data the models used. Nassim Taleb’s The Black Swan made the broader philosophical case: the events that matter most are the rare, high-impact ones that lie outside the record, and a model fitted to the past can offer false comfort precisely because it has never seen the thing that kills it. The practical counsel that follows is not to predict the unpredictable but to build systems that survive being wrong, with redundancy, modest leverage, and exposure that has a floor.

That counsel comes from engineering as much as from finance. Aviation is the best-known success in managing catastrophic risk. In 1935 the Boeing Model 299, the prototype of the B-17, crashed on a test flight, killing the pilot, and the investigation concluded that the aircraft was not too dangerous to fly but too complicated for memory. The response was the pilot’s checklist, which has since become a cornerstone of safety in aviation and, as Atul Gawande argued, in surgery. James Reason’s “Swiss cheese” model describes accidents as the alignment of holes in successive layers of defense, each imperfect, so that safety comes from having many layers and from closing holes before they line up, and it shifts blame from the last person to touch the controls toward the whole system. Sociologist Diane Vaughan’s study of the 1986 Challenger disaster described “normalization of deviance”: engineers had seen erosion of the booster’s O-ring seals on earlier flights, and because each flight returned safely, the anomaly gradually came to be treated as acceptable, until the cold morning when it was not. The commercial aviation record, in which fatal accidents have become extraordinarily rare, is built on investigation without blame, mandatory reporting, and the open sharing of near misses, a culture that treats every failure as information.

How we judge risk, as opposed to how we manage it, remains full of quirks. Paul Slovic’s studies of risk perception found that people fear hazards that are dreaded, uncontrollable, catastrophic, and unfamiliar, such as nuclear power, far more than hazards that are familiar and spread out in time, such as car accidents or cigarettes, regardless of the numbers. After the attacks of September 11, 2001, many Americans avoided flying and drove instead, and Gerd Gigerenzer estimated that the shift caused roughly fifteen hundred additional road deaths in the following year, more than the number of passengers killed on the hijacked planes. Fear of a vivid risk can thus cause deaths through the substitution of a less vivid but larger one. Regulators try to impose some consistency through cost-benefit analysis, using a “value of a statistical life,” which in the United States is on the order of ten million dollars. That phrase sounds callous but represents the amount people collectively are willing to pay to reduce risks that will, in expectation, save one life, not the worth of a particular person; without some such figure, society would spend lavishly to avert a vivid danger while neglecting cheaper ways to save more lives. The opposing view, the precautionary principle, holds that where an action threatens serious or irreversible harm, lack of full scientific certainty should not delay protective measures. Each approach has a failure mode: cost-benefit can underweight deep uncertainty, and precaution can paralyze action or merely shift risk elsewhere, as when a ban on a chemical leads to a worse substitute.

The deepest insurance problem is the one we cannot buy a policy against: the rare event that would end the pool itself. No insurer can pay out after a catastrophe that destroys the insurer, and the moments in history when humanity has come closest to such a threshold deserve a careful look, so the next subject I’ll take up is the strange, sobering story of nuclear close calls and the logic of deterrence, a domain where the stakes of getting probabilities wrong have been measured in the survival of cities.

Proceed

The nuclear age began with a calculation that might have gone wrong. Before the Trinity test in July 1945, Edward Teller raised the worry that a fission explosion might ignite the nitrogen in the atmosphere, setting off a runaway fusion reaction that would burn the planet. Hans Bethe and others worked through the numbers and concluded it was essentially impossible, and a report was written, LA-602, that put the odds at effectively zero. Enrico Fermi, characteristically, took bets among the staff on whether the test would ignite the atmosphere, partly to relieve tension. The episode set a template: a small group of people, working under time pressure, assessing a risk of ultimate magnitude with the tools of the day, and then proceeding. Whatever one thinks of the decision, the physics held, and the same calculation done today would be more rigorous. But the habit of accepting a small probability of an unbounded loss because the argument for safety seems sound has recurred in later technologies, and it is worth remembering that the arguments themselves can contain errors. Before the 1954 Castle Bravo test, physicists miscalculated the yield of a hydrogen bomb by a factor of nearly three, because they overlooked that lithium-7 in the fuel would also contribute. It produced fifteen megatons instead of the expected five and spread fallout over inhabited atolls and the Japanese fishing boat Lucky Dragon, killing one crewman and helping to launch the modern antinuclear movement.

The logic of deterrence grew from the fact that, after the Soviet Union acquired the bomb in 1949, no country could defend itself against a nuclear strike. Bernard Brodie wrote in 1946 that the chief purpose of the military establishment had shifted from winning wars to averting them. Thomas Schelling, in The Strategy of Conflict and Arms and Influence, developed this into a theory of bargaining under threat. A threat is only effective if it is credible, and credibility is hard when carrying it out would be suicidal. Schelling’s answers included commitment devices, ways of tying one’s own hands so that retaliation becomes automatic, and “the threat that leaves something to chance,” in which one deliberately creates a risk of catastrophe that neither side fully controls. The paradox is that rationality can be a handicap: the leader who is known to be willing to act irrationally gains leverage. Herman Kahn, whose On Thermonuclear War shocked readers with its cold discussion of casualty figures, and who was one of the models for Dr. Strangelove, argued that a credible strategy had to contemplate the unthinkable. Kubrick’s film took the commitment idea to its satirical limit with the Doomsday Machine, an automatic retaliatory device, whose flaw, as Dr. Strangelove laments, was keeping it secret, since a deterrent no one knows about deters no one.

Mutual assured destruction, a name coined with some irony, described the stable condition in which each side could absorb a first strike and still destroy the other. Stability depended on survivable forces: missiles in hardened silos, bombers on alert, and above all submarines hidden in the ocean, which could not be found and destroyed in a first strike. Ironically, defenses could be destabilizing, since a state that believed it could shield itself might be tempted to strike first, which is why the 1972 Anti-Ballistic Missile Treaty limited them, and why multiple warhead missiles, which tempted a first strike by making a single enemy missile capable of destroying several of one’s own, were considered dangerous. The theory has real intellectual force, and no nuclear weapon has been used in anger since 1945, an outcome that deterrence’s defenders cite as vindication. The difficulty is that a single absence of catastrophe in some eighty years is weak evidence about the underlying odds, much as surviving a game of Russian roulette tells you little about the number of bullets in the gun. The historical record gives plenty of reason for unease, because many of the closest calls came not from deliberate decisions but from error, misunderstanding, and malfunction.

The Cuban Missile Crisis of October 1962 is the best-known. When American reconnaissance found Soviet missiles under construction in Cuba, the Kennedy administration weighed an invasion, an airstrike, and a naval quarantine, and chose the last. The standoff lasted thirteen days. Only decades later did historians learn how close it came to going wrong on levels beyond the leaders’ control. Soviet forces in Cuba held tactical nuclear weapons and, as it turned out, local commanders had some authority to use them if invaded. On October 27, an American destroyer group dropped practice depth charges on the Soviet submarine B-59 to force it to surface. Cut off from Moscow, deep underwater, with batteries low and the air foul, the captain, Valentin Savitsky, believed war might have begun and wanted to launch a nuclear torpedo. Launch required the consent of three senior officers aboard, and the flotilla chief of staff, Vasili Arkhipov, refused. Whether the story is exactly as reported has been debated, but it is documented enough to be taken seriously, and it suggests that a single person’s judgment may have separated the world from nuclear war. The same day, an American U-2 was shot down over Cuba and its pilot killed, and another had strayed into Soviet airspace over Siberia. Kennedy and Khrushchev both stepped back, aided by a secret deal to withdraw American missiles from Turkey, which was concealed from the public for years. Robert Kennedy’s memoir and the transcripts of the White House tapes show leaders who were frightened and who deliberately worked to leave their opponent a way out, which Schelling had advised: never corner an adversary so completely that the only choices are humiliation or war.

Another incident came in September 1983, when the Soviet early-warning satellite system reported that the United States had launched five intercontinental missiles. The officer on duty at the command center near Moscow, Lieutenant Colonel Stanislav Petrov, judged it to be a false alarm. His reasons were that a real American first strike would likely involve hundreds of missiles, not five, that the ground radar had detected nothing, and that the system was new and, in his view, not fully reliable. He reported it as a malfunction. It was: the satellites had mistaken sunlight glinting off high-altitude clouds for missile flares. Petrov was neither punished nor rewarded at the time, and his role became known only after the Cold War. Weeks later, the NATO exercise Able Archer 83 simulated a nuclear release procedure so realistically that some Soviet leaders, already primed by a period of intense mistrust, feared it might be cover for a real attack and raised the alert status of some of their forces, though how close it came to disaster is argued among historians. In 1979 and 1980, American warning systems gave false indications of massive attack because of a test tape inserted into a live system and a failed computer chip; both were identified before response was launched. In 1995, a Norwegian scientific rocket triggered an alert in Russia, and Boris Yeltsin reportedly opened the nuclear briefcase and began the procedure before the rocket was recognized as harmless. A 1961 accident over Goldsboro, North Carolina, in which a B-52 broke apart and dropped two hydrogen bombs, left one of them with three of its four safety mechanisms triggered, the last remaining switch preventing detonation. Eric Schlosser’s Command and Control gathered such cases and argued that the weapons’ safety record owed as much to luck as to design.

Such stories underline Charles Perrow’s thesis in Normal Accidents: in systems that are both tightly coupled, where a failure in one part quickly propagates, and complex, where interactions are unexpected, accidents are inevitable and can’t be engineered out, however many safeguards are added, since the safeguards themselves add complexity. Nuclear command systems are the extreme case, and they face a dilemma: they must be able to launch very fast, to deter a first strike, and also must never launch by mistake. Maximizing one weakens the other. Launch on warning postures, in which missiles are fired upon detecting an attack and before the enemy warheads land, leave leaders only minutes to decide, a time too short for the most careful deliberation. The alternative of delegating authority to commanders in the field, to preserve retaliation if leaders are killed, creates the risk of unauthorized use. Scott Sagan has argued that organizations are unlikely to behave as flawless rational actors, and that the historical record shows near misses arising from precisely the kinds of routine organizational failures one would predict.

Arms control is the attempt to reduce those risks by agreement. The 1963 Partial Test Ban Treaty moved tests underground, partly in response to evidence that strontium-90 from fallout was accumulating in children’s teeth, as documented by the St. Louis Baby Tooth Survey. The Nuclear Non-Proliferation Treaty of 1968 bargained non-nuclear states’ promises not to acquire weapons against the nuclear states’ pledges to disarm eventually. It is widely considered one of the most successful treaties in history: the predictions of the 1960s that dozens of states would soon have nuclear weapons proved wrong, and today nine states hold them, though critics argue that the disarmament bargain has not been honored. SALT, START, and the INF Treaty cut and limited arsenals, which peaked at about seventy thousand warheads in the mid-1980s and have since fallen to roughly twelve thousand, still more than enough to devastate the planet. Verification was crucial: Reagan’s maxim, borrowed from a Russian proverb, was “trust, but verify,” and inspections and national technical means, meaning satellites, made agreements possible between rivals who did not trust each other. In recent years the framework has eroded; the United States withdrew from the INF Treaty in 2019 after accusing Russia of violating it, and the New START treaty, the last major limit on deployed strategic arsenals, was due to expire in February 2026. I can’t say with confidence how that has played out in the months since, and it is the sort of development one would want to check against current reporting.

The consequences of a large nuclear exchange extend beyond blast and radiation. In the early 1980s, a group including Carl Sagan published the TTAPS study proposing “nuclear winter”: smoke from burning cities, lofted into the stratosphere, would block sunlight and cool the planet enough to wreck agriculture worldwide. The initial estimates were criticized as overstated, but modern climate models, including work by Alan Robock, Brian Toon, and colleagues, find that even a limited regional war, say a hundred Hiroshima-sized weapons between India and Pakistan, could inject enough soot to cause multiyear cooling and substantial crop failures, with famine potentially affecting hundreds of millions. Such findings suggest that the harm of nuclear use would not stay with the combatants, that is, that the risk is global in a literal sense. Another dimension is the moral one. Hiroshima and Nagasaki killed on the order of a hundred and forty thousand and seventy thousand people respectively by the end of 1945, and the debate over whether the bombings were necessary to end the war, as defenders held, or whether Soviet entry into the war was the decisive factor, as revisionists argue, remains one of the most contested questions in modern history. The “nuclear taboo,” a norm against use that Nina Tannenwald has traced, has held through wars in which nuclear states faced defeat, such as the American war in Vietnam and the Soviet war in Afghanistan, and may be one of the most important and least appreciated normative achievements of the century.

The people who spent careers on this have left reflections worth hearing. Robert McNamara, defense secretary during the Cuban crisis, concluded late in life that the world had survived by luck, saying that the indefinite combination of human fallibility and nuclear weapons carries a very high risk of nuclear destruction. Daniel Ellsberg, famous for the Pentagon Papers, worked early in his career on nuclear command and control, and his book The Doomsday Machine described the system’s hair triggers and the extent of delegated authority. Others, like Kenneth Waltz, argued that nuclear weapons make war less likely, since they raise its costs so high, and that proliferation might therefore be stabilizing, a view most scholars reject because new nuclear states may have weaker controls and more fragile command structures. The debate cannot be settled by evidence, as the sample of nuclear history is small and the counterfactuals are invisible.

There is a coda that points toward hope. The one disease that humanity has deliberately eradicated, smallpox, was eliminated through a collaboration between the United States and the Soviet Union at the height of the Cold War. The Soviet Union proposed the global eradication campaign in 1958, the World Health Organization led it, and the last natural case occurred in Somalia in 1977. Rivals cooperated on a shared threat because both saw it as in their interest, and the achievement saved an estimated hundreds of millions of lives over the following decades. It is a reminder that adversaries can build trust through narrow, verifiable, mutually beneficial tasks, and that some of the most important diplomatic achievements are ones that go unnoticed because they work.

That thought leads me toward another kind of cooperation across divides, the one that makes the physical world’s most ambitious science possible. I’ll turn next to the large collaborations of modern physics and astronomy, from CERN’s accelerators to the gravitational wave observatories, and how thousands of people manage to build one instrument and agree on what it has found.

Proceed

Big science begins with a problem of scale: the questions physicists most want to answer sit at energies, distances, or sensitivities that no single laboratory can reach. The Large Hadron Collider at CERN is the extreme case, a ring twenty-seven kilometers around, buried beneath the French-Swiss border, in which superconducting magnets cooled to about 1.9 kelvin, colder than outer space, steer two beams of protons in opposite directions until they collide at nearly the speed of light. At full operation the beams cross about forty million times a second, and each crossing produces dozens of collisions. No detector could record them all, so the experiments use layered triggers: hardware circuits that decide within microseconds which events look interesting, followed by software filters, reducing the flood to a few thousand events per second saved for analysis, still a torrent. The data are processed on the Worldwide LHC Computing Grid, a network of computing centers in dozens of countries, because no single institution owns enough machines to hold them.

The human organization is stranger than the hardware. ATLAS and CMS, the two general-purpose detectors, each involve roughly three thousand physicists from well over a hundred institutions. No one is in charge in the sense a corporation has a boss. Collaborations run on elected spokespeople, committees, internal review, and consensus, and since the participants are employed by universities and national labs that cannot be commanded, the whole thing works through persuasion and shared commitment. Papers carry the names of everyone, which is why some papers have author lists longer than their text; the Higgs mass measurement paper of 2015 by ATLAS and CMS together listed over five thousand authors. Internal procedures are elaborate precisely because there’s no way to take any one member’s word: an analysis is typically developed by a small team, scrutinized by independent reviewers, presented to the collaboration, and subjected to blinding, in which the analysts fix their method before looking at the region of data where a signal would appear, to avoid unconsciously tuning cuts until a bump emerges. This is the institutional version of what I said earlier about preregistration, adopted by particle physics decades ago because the field has been burned by false alarms.

Those false alarms are instructive. Physicists use a demanding threshold for claiming a discovery: five sigma, meaning that if there were no new particle, the chance of a fluctuation at least that large would be about one in 3.5 million. The standard is stricter than in most sciences because experimenters examine many places in the data, the “look-elsewhere effect,” so a modest bump somewhere is nearly guaranteed by chance. In December 2015, both ATLAS and CMS saw a small excess of photon pairs at a mass around 750 GeV, at roughly three sigma. Hundreds of theory papers appeared within months explaining what the new particle might be. With more data in the summer of 2016, the bump vanished. The episode is remembered as a cautionary tale and also as the system working, since the threshold did its job and the retraction was swift and unembarrassed. Likewise the 2011 faster-than-light neutrino result from OPERA, mentioned earlier, was announced by a team that said openly it expected to find an error and asked the community for help, which it did.

The Higgs discovery, announced on July 4, 2012, followed nearly half a century after Peter Higgs, François Englert, Robert Brout and others proposed the mechanism in 1964. The particle was the last missing piece of the Standard Model, and it was found at about 125 GeV, consistent with the range indirect measurements had favored. What it showed was less a surprise than a confirmation, and since then the LHC has found no clear sign of anything beyond the Standard Model, which is both a triumph and a frustration. Theorists had expected new particles near the electroweak scale, such as the supersymmetric partners that would stabilize the Higgs mass against quantum corrections, the “hierarchy problem,” and their absence so far has undermined a favored line of thinking. The situation resembles the late nineteenth century in reverse: instead of a few anomalies hinting at a revolution, the theory works embarrassingly well while leaving the large questions, dark matter, the matter-antimatter asymmetry, the masses of neutrinos, and gravity, untouched. Several results hint at cracks, such as measurements of the muon’s magnetic moment at Fermilab that differed from the prediction at about four to five sigma, though improved theoretical calculations using lattice methods have narrowed the discrepancy, and the status of that anomaly has shifted as the theoretical input has been refined.

The cost-benefit question for such projects is real and unresolved. The LHC cost roughly nine billion dollars to build, and a proposed successor, the Future Circular Collider, would be a ring of about ninety kilometers at a cost of tens of billions, which prompts critics such as Sabine Hossenfelder to argue that money would be better spent on other science, since there’s no guaranteed discovery. Defenders note that basic research has a history of unplanned payoffs: the World Wide Web was invented at CERN by Tim Berners-Lee in 1989 to help physicists share documents, and accelerator technology feeds medical applications, including proton therapy for cancer and the ubiquitous PET scanner. But justifying a collider by spin-offs is a weak argument, since the same money spent directly on those goals would likely do more; the honest case is that humanity wants to know what the universe is made of, and that this is worth paying for in the way cathedrals were. The cancelled American Superconducting Super Collider, which Congress ended in 1993 after about two billion dollars had been spent and a tunnel dug in Texas, is the counterexample reminding everyone that such projects depend on sustained political will.

Gravitational wave astronomy offers a different model, and a spectacular payoff. Einstein predicted gravitational waves in 1916, ripples in spacetime produced by accelerating masses, and the effect is almost absurdly small. When two black holes merge, the waves passing Earth stretch and squeeze space by about a thousandth of the width of a proton across the four-kilometer arms of the LIGO detectors. Measuring that requires laser interferometry of extraordinary refinement: light bounces back and forth between mirrors suspended as pendulums, with the mirrors isolated from seismic noise by layered stages, the vacuum system among the largest in the world, and the laser power and mirror coatings tuned until quantum noise becomes the limit. Squeezed light, a quantum-optical trick that redistributes uncertainty between phase and amplitude, is now used to push sensitivity past the standard quantum limit, so a detector in Louisiana and another in Washington state are among the most precise measuring devices ever built. Rainer Weiss conceived the design in the 1970s, Kip Thorne and Ronald Drever helped to found the project, and Barry Barish rescued its management in the 1990s, turning a troubled endeavor into a working collaboration. The first detection, GW150914, arrived on September 14, 2015, just as the upgraded instruments were being tested: a chirp from two black holes of about thirty-six and twenty-nine solar masses that merged 1.3 billion light-years away, radiating about three solar masses of energy in a fraction of a second, more power than all the stars in the observable universe combined. The Nobel Prize followed in 2017.

The detection raised the question of how to be sure, and the way the team handled it is a model of internal skepticism. Because the signal came so early and so clearly, some members suspected a “blind injection,” a fake signal secretly inserted to test the pipeline, as had been done in earlier runs, so the collaboration spent months in careful checks before announcing in February 2016. In 2017, the detection of a neutron star merger, GW170817, by LIGO and the Virgo detector in Italy, was followed within seconds by a gamma-ray burst seen by space telescopes, and then by observations from nearly every major observatory, a milestone of multimessenger astronomy. That event settled several questions at once: it confirmed that mergers of neutron stars produce short gamma-ray bursts, showed that gravitational waves travel at the speed of light to within one part in a quadrillion, and revealed through the afterglow’s spectrum that such mergers forge heavy elements like gold and platinum, giving observational support to the idea that much of the universe’s gold was made in cosmic collisions. The ring on a finger holds atoms formed in an event like that, which seems a good reason to find astrophysics moving. Hundreds of mergers have now been logged, building a census of black holes whose masses and spins challenge theories of stellar evolution, including some in the supposed “mass gap” where stars should leave no black hole behind.

Astronomy at large scale involves the same sociology. The Event Horizon Telescope, which produced the first image of a black hole’s shadow, in the galaxy M87, announced in April 2019, is not a single telescope but a network of radio dishes spread from Hawaii to Chile to the South Pole, combined by very-long-baseline interferometry into an Earth-sized virtual dish. Each station recorded data on hard drives, petabytes in total, with timing from hydrogen maser atomic clocks, and since the South Pole drives could not be shipped until the austral summer, the data waited months. Katie Bouman, then a young researcher, became briefly famous for her role in developing imaging algorithms, and the team’s insistence on using several independent imaging pipelines, with groups working in isolation to avoid contaminating each other’s results, was another example of blinding. The ring of light surrounding a dark center matched the predictions of general relativity for a black hole of about six and a half billion solar masses, and in 2022 the team imaged Sagittarius A*, the black hole at the center of our own galaxy, which is far smaller and flickers on a timescale of minutes, making it harder to capture.

The James Webb Space Telescope shows the same pattern with the added challenge that it could not be repaired. Its 6.5-meter mirror is made of eighteen gold-coated beryllium segments that had to unfold in space, and the five-layer sunshield, the size of a tennis court, had to deploy flawlessly, with over three hundred potential single points of failure, a fact that kept engineers awake for years. It launched on Christmas Day 2021, after cost overruns that took the price to about ten billion dollars and a delay of over a decade, and unfolded without a hitch, a demonstration of what engineering patience and redundancy can do. It sits at the Sun-Earth L2 point, a gravitationally balanced location a million and a half kilometers from Earth, where the instrument can stay cold enough to observe infrared light, the kind that cosmic expansion has stretched from the earliest galaxies. Its observations of exoplanet atmospheres, detecting water vapor, carbon dioxide, and in some cases sulfur dioxide produced by photochemistry, have opened the study of other worlds’ chemistry.

Exoplanets are themselves a story of a field that went from speculation to a catalog in a generation. The first planet around a Sun-like star, 51 Pegasi b, was found by Michel Mayor and Didier Queloz in 1995, a gas giant orbiting in just over four days, which defied expectations since no one had imagined a Jupiter-sized planet so close to its star. The Kepler mission, launched in 2009, stared at one patch of sky and watched for the tiny dimming as planets crossed their stars, and showed that planets are common: most stars have them, and small rocky planets in the habitable zone, where liquid water could exist on the surface, are not rare. More than five thousand are confirmed. The question of whether any harbor life is now a research program rather than a fantasy, centered on searching for biosignatures in atmospheres, such as oxygen alongside methane, a combination hard to sustain without biology. The claim in 2023 of dimethyl sulfide in the atmosphere of K2-18 b stirred excitement, and further analysis has weakened it, again illustrating the care needed and the value of the five-sigma culture. Frank Drake’s equation of 1961, which multiplies factors from star formation rates to the lifetime of civilizations, captures our ignorance: some terms are now constrained, but the ones about the emergence of life and intelligence remain wild guesses, and the silence of the sky, the Fermi paradox, stays unexplained. Proposed answers range from the rarity of the evolution of complex life, the Great Filter, to the possibility that civilizations are short lived or simply uninterested in announcing themselves.

What strikes me about all these enterprises is the combination of ambition and procedural modesty. The collaborations succeed not because their members are saints but because they have built structures that make error visible: independent analysis, blinding, open data eventually, formal review, and cultures in which a retraction is a normal event rather than a disgrace. Peter Galison, in Image and Logic, described how particle physics evolved two traditions, one that valued pictures, such as bubble chamber photographs of individual events, and another that valued statistics, aggregates from electronic detectors, and how the field came to reconcile them. Whatever the apparatus, a claim has to survive the scrutiny of colleagues with every motive to find the flaw. It is a cooperative arrangement built on organized distrust, which is a curious thing to say about humanity’s most trusted form of knowledge, and I think it is exactly right.

That brings me to the subject of how disagreement is made productive, and I want to turn to a very different arena where organized distrust has been refined over centuries, the common law tradition of precedent and reasoning by analogy, and how a body of law grows by accretion from individual cases.

Proceed

The common law is a strange machine: a body of rules that no one designed, grown case by case over nearly a thousand years, and still doing its work in courtrooms from London to Lagos to Sydney. Its origin story begins after 1066, when the Norman kings, and especially Henry II in the 1160s and 1170s, sent royal justices out on circuit to hear disputes in the shires. The judges returned to Westminster and compared notes, and the customs they found in different places were gradually blended into a law “common” to the whole realm. No legislature wrote it. It was assembled from decisions, which is why its basic unit is the case rather than the statute, and why its characteristic virtue is a kind of disciplined improvisation: judges must resolve the dispute in front of them, yet must do so in a way that fits what earlier judges decided.

That constraint is the doctrine of precedent, stare decisis, “to stand by things decided.” Its justification is partly fairness, since like cases should be treated alike, and partly practical: if people can predict how courts will rule, they can plan, contract, and settle without litigating. Reasoning by analogy is the working method. A lawyer arguing that the present case resembles a prior one must say in what respects, and the opposing lawyer must argue that the differences matter more. The ratio decidendi, the principle necessary to the earlier decision, binds later courts, while obiter dicta, remarks made in passing, merely persuade. Deciding which part of an old opinion was essential is itself an act of interpretation, and so the doctrine gives judges both a leash and some slack. Karl Llewellyn, a legal realist, observed that the leash is often longer than formal theory admits, since precedents can be read broadly or narrowly depending on where the court wants to go.

The classic illustration is the evolution of negligence. For centuries, English law offered remedies for particular recognized wrongs, but no general duty to take care. In Donoghue v Stevenson, decided by the House of Lords in 1932, Mrs. Donoghue drank ginger beer in a Paisley café, a friend having bought it for her, and allegedly found the remains of a decomposed snail when the bottle was emptied. She had no contract with the manufacturer, and under the old privity rule she should have had no claim. Lord Atkin, however, reasoned from the Biblical injunction to love one’s neighbor to a legal principle: you must take reasonable care to avoid acts or omissions you can reasonably foresee would injure your neighbor, meaning persons so closely affected that you ought to have them in contemplation. From a bottle of ginger beer grew the modern law of negligence, extended over the following decades to doctors, architects, accountants, and drivers. The law expanded, and then, as courts worried about unlimited liability, contracted again with limiting doctrines such as proximity and policy. This is the pattern: a principle announced in a concrete setting, extended by analogy, and then trimmed by later judges who see the consequences.

The system also confronts the problem of what to do when precedent produces injustice. Courts sometimes distinguish a case, finding a difference that lets them avoid the old rule, and sometimes overrule it outright. In Brown v. Board of Education in 1954, the US Supreme Court rejected the “separate but equal” doctrine of Plessy v. Ferguson from 1896, relying in part on social-scientific evidence about the harms of segregation, a move critics at the time thought extralegal. Lord Denning, the famously inventive English judge, spent a career pushing the law toward what he saw as justice, as when he developed the doctrine of promissory estoppel to enforce promises on which people had relied, and he was just as famously rebuked by colleagues for flexibility that threatened predictability. The tension is permanent: too much fidelity to the past entrenches old errors; too little makes the law a matter of the judge’s mood. Oliver Wendell Holmes wrote in 1881 that the life of the law has not been logic but experience, and that its prophecies of what courts will do are what he meant by the law. Ronald Dworkin offered a rival picture, in which judges are like authors of a chain novel, each adding a chapter that must fit the story so far while making it the best story it can be, so that law has an integrity beyond mere prediction.

Equity grew up beside the common law to patch its rigidities. Because the common courts could only award damages and insisted on rigid forms of action, litigants petitioned the king, and then the Lord Chancellor, “keeper of the king’s conscience,” for relief. Chancery developed remedies such as injunctions and specific performance, and recognized the trust, an arrangement in which one person holds property for the benefit of another, which common law courts would not have countenanced. The trust turned out to be one of the most flexible legal inventions ever made. It let crusaders leave their lands in the care of friends, let families protect property from creditors and the crown, and now structures pension funds, charities, and mutual funds, a thousand-year-old device managing trillions of dollars. For centuries the two systems ran in parallel, famously illustrated in Dickens’s Bleak House, where the Jarndyce case drags on until the estate is consumed by costs, and they were merged administratively in England by the Judicature Acts of 1873 and 1875.

Alongside the common law tradition stands the civil law, descending from Roman law and codified in the Napoleonic Code of 1804, in which the primary source is a comprehensive written code and the judge’s role is, in theory, to apply it rather than make law. Reality is blurrier than the contrast suggests. Civil law judges interpret broad provisions, and their decisions accrue authority, while common law jurisdictions have statutes and codes aplenty. But the two traditions differ in temperament: the common lawyer reasons upward from cases, the civilian downward from principles. They also differ in procedure. The common law trial is adversarial, driven by the parties, with a relatively passive judge and, in some cases, a jury, while civil law systems are more inquisitorial, with judges actively investigating. Each has distinctive strengths and failings, and comparative lawyers enjoy pointing out that both produce a rough kind of justice with entirely different machinery. Other traditions have their own logics: Islamic jurisprudence, fiqh, draws on the Quran, the Sunnah, consensus, and analogical reasoning, qiyas, a method strikingly like common law analogy, developed by jurists in the eighth and ninth centuries, and its schools preserve centuries of case-based reasoning.

The jury deserves its own note, as an institution that hands legal fact-finding to twelve ordinary people. Its roots lie in medieval inquests, where neighbors were summoned to say what they knew, and its modern role is to apply the law to the facts as they find them. Magna Carta in 1215 is often credited with enshrining judgment by one’s peers, though its clause meant something more limited at the time. Juries have the power of nullification, to acquit despite the evidence, as when colonial juries acquitted John Peter Zenger in 1735 of seditious libel for printing criticisms of the governor of New York, helping to establish the principle that truth is a defense, and as when Northern juries sometimes refused to convict under the Fugitive Slave Act. The same power was exercised in the American South to acquit white defendants who killed Black victims, a reminder that the conscience of the community can be a vice as well as a virtue. Psychological research shows that jurors are influenced by the order of evidence, by narrative coherence, and by pretrial publicity, and the Kalven and Zeisel study of the 1950s and 60s found that juries agreed with the judge in roughly four cases out of five, suggesting that the system is less capricious than critics fear, even if far from perfect.

The adversarial system embodies the organized distrust I described in science. Each side is assigned to marshal the best case, and the theory, going back to John Stuart Mill’s argument in On Liberty about the value of hearing the opposite case, is that truth will emerge from the clash. Cross-examination is, in Wigmore’s phrase, the greatest legal engine ever invented for the discovery of truth, though anyone who has watched it can appreciate it also as a rhetorical weapon used to unsettle truthful witnesses as readily as to expose liars. The professional ethics governing lawyers are a fascinating balance: a lawyer owes a duty of confidentiality to the client, must not knowingly present false evidence, and is expected to represent even unpopular defendants vigorously. John Adams defended the British soldiers charged in the Boston Massacre of 1770, winning acquittals for most, and later regarded it as one of the best services he ever rendered his country, since a society that denies a defense to the hated has abandoned its own principles.

A further feature of the system is the way it treats facts that cannot be proved with certainty. Standards of proof are a kind of formalized probability: “preponderance of the evidence” in civil cases, meaning more likely than not, “clear and convincing” for certain weightier matters, and “beyond reasonable doubt” in criminal cases, which courts decline to quantify, though when researchers ask, judges and jurors give numbers ranging from about seventy to ninety-five percent. The asymmetry reflects Blackstone’s ratio again. Statistical evidence poses puzzles: suppose a hundred people attended a rodeo and only one paid; a court cannot find any particular attendee liable on the ground that there is a ninety-nine percent chance he is a gatecrasher. Intuitively, such “naked statistical evidence” is not enough, though a much weaker eyewitness account might be. Legal philosophers have debated why, suggesting that proof must be causally connected to the fact, or that it must be sensitive to the truth in the way individual evidence is, a puzzle with echoes in the earlier medical test example. The question became concrete in the case of Sally Clark, a British solicitor convicted in 1999 of murdering her two infant sons, where an expert pediatrician testified that the chance of two cot deaths in one family was one in 73 million, a number reached by squaring the single-event probability on the false assumption of independence and then compounded by the prosecutor’s fallacy of treating it as the probability of innocence. The Royal Statistical Society protested, and her conviction was overturned in 2003, but she never recovered and died in 2007. Courts have since become more careful about statistical testimony, though the problem recurs wherever numbers meet lawyers.

Laws also have to deal with the problem of interpretation, which is as old as writing. A statute says “no vehicles in the park.” Does it cover a bicycle, an ambulance, or a war memorial mounted on a tank? H. L. A. Hart used the example to argue that rules have a core of clear meaning and a penumbra of uncertainty, where judges must exercise discretion, and Lon Fuller replied that purpose, not just words, guides interpretation, since a rule about vehicles in the park exists to protect quiet and safety. The schools of thought are still alive: textualists such as Antonin Scalia held that judges should follow the ordinary meaning of the enacted words, because that is what the legislature voted on, while purposivists look to the aim of the law and its history. In constitutional law the divide shows as originalism versus living constitutionalism, with each side claiming to constrain judges better and accusing the other of letting politics in. The interpretive canon is, in effect, a set of tie-breaking conventions, such as reading ambiguous criminal laws narrowly under the rule of lenity, and each is defensible and none is a complete algorithm.

All this suggests why the common law matters beyond lawyers’ circles. It’s an evolved system for generating rules out of conflict, with errors corrected by later cases, and Friedrich Hayek admired it as an example of spontaneous order, the kind of social knowledge that no central planner could have written. Some economists, following Paul Rubin, have argued that litigation tends toward efficient rules because inefficient ones are challenged more often, while others find the evidence for that tendency thin. The system’s weaknesses are real: cost and delay make justice inaccessible to many, outcomes depend on resources, and the doctrine carries historical prejudices, such as the rule of coverture, under which a married woman’s legal identity merged with her husband’s, which persisted well into the nineteenth century. Reform through legislation and through changing judicial attitudes has addressed some of these, and at its best the system displays the capacity to absorb criticism and change course.

One of the most interesting frontier questions is how law should cope with technologies it did not foresee. The common law has stretched to cover steam locomotives, automobiles, telegraphs, and now software and networks, and its method of analogy is both a strength and a handicap: courts asked about data held on a phone in the 2014 case Riley v. California had to decide whether it was like a wallet found on an arrested person, and concluded it was not, since a phone holds vastly more than any pocket ever could. The Court’s judgment turned on a recognition that a difference in degree can become a difference in kind. When the analogies run out, legislatures step in, and they are often slower and more captured by interest groups than courts, which sets up a recurring question about who should make the rules for the new world.

That question, who gets to make the rules and how the people governed by them are involved, is the heart of political theory, and I’ll follow it into a subject that has been in the background throughout: the different ways humans have designed systems of collective decision, from Athenian sortition to modern voting rules, and the surprising mathematics that shows no scheme can satisfy everything we want.

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Athens is the natural place to start, because the Athenians did something we have mostly forgotten how to do: they used lotteries as a core democratic device. In the fifth and fourth centuries BCE, the Assembly, open to all adult male citizens, set policy by direct vote, but the day-to-day business of government was handled by a Council of Five Hundred, chosen by lot from the citizens, with each of the ten tribes supplying fifty members. Most magistracies were filled the same way, and juries of several hundred, again chosen by lot, decided cases. Offices were typically held for a year, and one could serve on the Council only twice in a lifetime. Elections were reserved for roles where expertise was thought to matter, notably the ten generals, who were chosen by vote and could be re-elected, as Pericles was, repeatedly. The Athenians understood a point that modern theorists have rediscovered: election selects for ambition, visibility, and the resources to campaign, whereas a lottery gives every citizen an equal chance and produces a body that statistically resembles the population. Aristotle wrote in the Politics that the appointment of magistrates by lot is democratic, while election is oligarchic, a claim that sounds bizarre to anyone raised on the equation of democracy with voting. To prevent abuse, officials faced an audit at the end of their term, the euthynai, and the Assembly could vote each year on whether to hold an ostracism, exiling for ten years a citizen deemed too powerful, by writing his name on a potsherd. Archaeologists have found thousands of these ostraka, including a cache of nearly two hundred bearing the name of Themistocles, many apparently written in a handful of hands, suggesting that someone was distributing pre-inscribed shards, an early instance of organized campaigning.

The idea is far from dead. Citizens’ assemblies, bodies of randomly selected residents who deliberate with expert briefings and then make recommendations, have been used in Ireland, where a 2016 to 2018 assembly helped break a decades-long deadlock on abortion and led to the 2018 referendum, and in France, where a Citizens’ Convention on Climate met in 2019 and 2020. The Canadian provinces of British Columbia and Ontario tried assemblies on electoral reform in the 2000s, and Belgium’s German-speaking community has established a permanent one. The attraction is that a random sample, given time and information, can reach considered judgments free of the incentives that distort party politics. James Fishkin’s deliberative polling has repeatedly shown that people’s views shift, often toward more moderate or better-informed positions, after structured discussion. Skeptics worry about legitimacy, since nobody elected these people, and about how much influence a few hundred participants can have on a large society, and in practice outcomes depend heavily on whether governments commit in advance to act on the results.

Now the mathematics of elections. Suppose three candidates, and voters who rank them. The simple plurality rule, used in the United States and Britain for most legislative seats, counts only first choices, and it leads to a well-known pathology: with a split between two similar candidates, the vote of their shared constituency divides, and a candidate whom most voters dislike can win. Duverger’s law is the observation that plurality systems tend toward two parties, because voters abandon candidates who cannot win rather than waste their votes, and because the fear of splitting pushes similar factions to merge. Alternatives abound. Runoff systems, as in France, hold a second round between the top two. Instant-runoff voting, or ranked-choice voting, used in Australia for its lower house and in Maine and Alaska, has voters rank candidates, eliminating the last-placed candidate round by round and transferring those ballots. Approval voting lets voters say yes or no to each candidate. The Borda count gives points according to rank. Condorcet’s method, from the eighteenth century, declares winner the candidate who would beat every other in head-to-head contests, if there is one.

The trouble is that sometimes there isn’t one. Condorcet noticed that majorities can be cyclic: in a population where a third prefer A over B over C, a third prefer B over C over A, and a third prefer C over A over B, a majority prefers A to B, a majority prefers B to C, and a majority prefers C to A. Collective preferences can be intransitive even when every individual’s are perfectly consistent. This is the Condorcet paradox, and it implies that the idea of “the will of the people” can be ill-defined when there are more than two options. The Marquis de Condorcet, a mathematician and a champion of the Revolution, died in prison in 1794 in circumstances that are not fully clear, a poignant end for the man who first showed that majority rule is subtle.

Kenneth Arrow, in his 1951 doctoral work, turned this into a general theorem. He asked: is there any way of aggregating individual rankings into a social ranking that satisfies a few reasonable conditions? Unrestricted domain, meaning it works for any set of individual preferences; the Pareto principle, meaning that if everyone prefers X to Y then society does; independence of irrelevant alternatives, meaning the social ranking of X and Y depends only on how individuals rank X and Y; and non-dictatorship, meaning no single individual’s preferences always prevail. Arrow proved that with three or more options, no rule satisfies them all. The result won him a Nobel Prize in 1972 and launched social choice theory. It doesn’t say democracy is impossible; it says that no voting method is perfect, so every scheme must give up something, and choosing a method is choosing which flaws to tolerate. Later, Allan Gibbard and Mark Satterthwaite showed that any reasonable voting rule with three or more options is open to strategic manipulation: there will be situations where a voter does better by misreporting their true preferences. Honest voting cannot be guaranteed by design alone.

Real examples show these abstractions biting. In the 2000 US presidential election, Ralph Nader received about ninety-seven thousand votes in Florida, where the margin between Bush and Gore was a few hundred, and many assume most Nader voters would have preferred Gore to Bush, so the plurality rule let a minor candidate swing the outcome. In the 2009 mayoral election in Burlington, Vermont, which used instant-runoff voting, the Progressive candidate, Bob Kiss, won, although the Republican led in first preferences and the Democrat, Andy Montroll, would have beaten either in head-to-head contests, a Condorcet winner who was eliminated first and thereby exposed a weakness in the method. Vermont’s city abandoned the system soon after. Such cases dramatize that each rule has scenarios in which its result looks perverse. Social choice theorists like Donald Saari and Eric Maskin have argued for methods like the Borda count or majority judgment, a system proposed by Michel Balinski and Rida Laraki, in which voters grade candidates and the winner is the one with the highest median grade, but no method has attracted a consensus, and the political choice of rules is always entangled with who expects to benefit.

Another layer is representation. Even when one agrees on how to count votes within a district, how to draw districts becomes a battleground. The word gerrymander dates from 1812, when Massachusetts governor Elbridge Gerry signed a map with a salamander-shaped district favoring his party. Modern software allows astonishingly precise manipulation: by “packing” opponents into a few districts they win overwhelmingly and “cracking” the remainder across many where they narrowly lose, a party with a minority of votes can secure a majority of seats. Social scientists have developed measures such as the efficiency gap, which compares the two parties’ wasted votes, and computer simulations that generate thousands of random maps to see whether a given one is an outlier. In Rucho v. Common Cause in 2019, the US Supreme Court held that partisan gerrymandering claims present political questions beyond the reach of federal courts, leaving reform to states, some of which have turned to independent commissions. Proportional representation systems, common in continental Europe, avoid the problem by allocating seats according to party vote shares, but they bring their own tradeoffs: more parties, coalition bargaining, and sometimes small parties with leverage far beyond their size. Political scientists such as Arend Lijphart have argued that consensus democracies, with proportional representation and power sharing, tend to perform at least as well as majoritarian ones on economic management and better on inclusiveness, while defenders of majoritarian systems stress clear accountability, since voters can throw out a government in a single stroke.

Beyond the counting of votes lies the deeper question of why people vote at all. A single vote has a vanishing chance of deciding a large election, perhaps one in tens of millions in a US presidential contest, though much higher in a swing state, so by narrow calculation of cost and benefit, voting is irrational, the “paradox of voting” noted by Anthony Downs in 1957. Yet tens of millions vote. Explanations include duty, expressive satisfaction, social pressure, and the sheer pleasure of participating, and field experiments by Alan Gerber and Donald Green found that mailing people notices saying their turnout would be publicly known raised participation substantially, evidence of how much voting is a social act. Compulsory voting, as in Australia, where nonvoters pay a small fine, produces turnout above ninety percent. The same logic of collective action applies to public goods generally. Mancur Olson argued in 1965 that large groups with diffuse interests find it hard to organize, since each member benefits from the others’ efforts whether or not they contribute, while small, concentrated groups, such as industries seeking tariffs, organize easily, a tilt that explains why policies with concentrated benefits and diffuse costs persist: sugar subsidies, agricultural supports, and occupational licensing.

There’s also the question of information and the voter’s role. Voters are often poorly informed about policy, a fact documented since the survey research of the 1950s, but Samuel Popkin and others argued that they use cues, such as party labels, endorsements, and the performance of the economy, as shortcuts and are thereby “low-information rational.” Bryan Caplan has argued the opposite, that voters are systematically biased on economics in ways that matter, and Achen and Bartels’s Democracy for Realists found that retrospective voting is crude, with voters punishing incumbents for events like droughts and shark attacks beyond their control. Their provocative result about shark attacks off the New Jersey coast in 1916 has itself been questioned on methodological grounds. In any case, the picture of an informed electorate weighing policy platforms gives way to one of group identity and loyalty, where parties function as social identities and policy positions follow from them. Defenders of democracy respond that its value lies less in the quality of each decision than in its capacity for peaceful turnover and error correction: Karl Popper held that the key question is not who should rule but how we can get rid of bad rulers without bloodshed, and elections answer that. Amartya Sen noted that no functioning democracy has ever suffered a famine, because leaders who must face voters and a free press cannot ignore starvation, a claim that fits the evidence remarkably well.

All of this concerns formal institutions, but the informal ones often matter more, and in the next stretch I want to turn from how collective decisions are made to the conditions that let a society hold together at all: trust, social capital, and the quiet networks that Robert Putnam found in Italian regions and American bowling leagues.

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Putnam’s story starts in Italy, because Italy ran a natural experiment. In 1970 the national government created regional governments with identical formal powers and structures, all at once, across a country whose regions differed enormously. Putnam and his colleagues, Robert Leonardi and Raffaella Nanetti, spent about twenty years tracking how these new institutions performed, and the results, published in 1993 as Making Democracy Work, were stark. Governments in the north, in Emilia-Romagna and Tuscany, were responsive, efficient, and innovative, while those in much of the south, in Calabria and Campania, were sluggish and corrupt. Economic differences didn’t explain it well; the best predictor was the density of civic associations: choral societies, football clubs, cooperatives, and mutual aid groups. Regions with a rich associational life had better governments, and the pattern, Putnam argued, traced back nearly a thousand years to the medieval city-republics of the north, where horizontal networks of guilds and confraternities flourished, versus the vertical, hierarchical Norman monarchy of the south. That is a bold claim about institutional persistence, and critics have questioned the causal arrows and the long historical reach, though later work by Guido Tabellini and others found that medieval political institutions do still correlate with present-day trust in Italian cities, which lends the thesis more support than skeptics expected.

Social capital, Putnam’s term, comes in two forms. Bonding capital ties together similar people, family, close friends, ethnic communities, and is good for mutual support but can become insular. Bridging capital links across divides and is better for spreading information and cooperation among groups. Mark Granovetter’s 1973 paper, “The Strength of Weak Ties,” found that people are more likely to get jobs through acquaintances than through close friends, because close friends share your information, while acquaintances move in different circles and know of different openings. The result generalizes: weak ties are bridges across which novelty flows, and a society with many of them is better at spreading innovation, though also rumor. Ronald Burt’s work on structural holes sharpened the picture, showing that individuals who span gaps between otherwise disconnected clusters tend to have good ideas and get promoted, since they see what no one inside either cluster can see. A network is not just a set of relationships but a distribution of opportunity.

In Bowling Alone, published in 2000, Putnam turned to the United States and documented a long decline in civic participation since the 1960s: fewer people attending club meetings, joining unions, going to church, hosting dinner parties, or voting, with league bowling declining even as the number of bowlers rose. He blamed generational change, television, suburban sprawl and long commutes, and the pressures of two-career families, with generational replacement carrying the largest share. The thesis was contested; Claude Fischer and others noted that new forms of association emerged and that the decline was not uniform, and some argued that measures of trust and informal socializing held steadier than Putnam suggested. But the book struck a chord because it named a worry many people felt. Subsequent work, including Putnam’s own Our Kids, documented how class divides in family structure, schooling, and neighborhood have widened, so that children from different backgrounds increasingly grow up in separate worlds, with weaker bridging ties between them.

Trust itself has been measured for decades with a single survey question: generally speaking, would you say that most people can be trusted, or that you can’t be too careful? Answers vary enormously across countries, with around sixty to seventy percent in Norway, Sweden, and Denmark saying most people can be trusted, versus under ten percent in some countries in Latin America, Africa, and Southeast Asia. Within the US, the figure has fallen from about half in the 1960s to around a third or less. Generalized trust correlates with lower corruption, better institutions, and higher growth, and Francis Fukuyama argued in Trust that high-trust societies could build large, impersonal organizations, while low-trust ones depended on family firms. Stephen Knack and Philip Keefer found cross-country correlations between trust and economic performance. Yet the direction of causation is murky, since good institutions may produce trust rather than the reverse, and a wallet-drop experiment run by Reader’s Digest, in which reporters dropped wallets in cities worldwide and counted how many were returned, found the highest return rates in Helsinki and lower rates elsewhere, though a later and larger academic study across forty countries by Cohn and colleagues found, surprisingly, that wallets with more money in them were more likely to be returned, suggesting that people’s concern for their own self-image as honest operates beyond mere self-interest.

Where does trust come from? Part of the answer is that it is learned early and is sticky. Eric Uslaner argues that generalized trust is a moral value, instilled in childhood and relatively stable, and that it is optimism about people more than a calculation of risk. Another part is that it responds to institutions: when courts enforce contracts and police are not predatory, strangers can be relied on, and when the state is corrupt, the rational response is to trust only kin. Immigration and diversity have been tested as threats to trust. Putnam’s own 2007 study reported that in more ethnically diverse American communities, people trusted neighbors less and withdrew from civic life, though he emphasized that the effect might fade as societies adapt. Subsequent studies are mixed: contact between groups, when sustained and on equal terms, tends to reduce prejudice, as Gordon Allport hypothesized in 1954 and a large meta-analysis by Pettigrew and Tropp later supported, while sheer proximity without interaction may not help. Context matters, and the effect of diversity depends on segregation, economic conditions, and how political leaders frame group differences.

The economics of trust is vivid in the history of long-distance trade. The Maghribi traders I mentioned earlier relied on a coalition enforced by reputation, while their Genoese contemporaries, according to Avner Greif, relied on formal legal institutions and the city’s courts, a contrast that, he argued, shaped the divergent institutional paths of the Islamic and European worlds. In many modern contexts, informal enforcement still does the work that law cannot. Lisa Bernstein’s study of the New York diamond industry found that dealers, who hand over gems worth enormous sums on a handshake and a Hebrew phrase, rely on a private system of arbitration with the threat of expulsion from the community, and the cotton and grain trade developed similar private rules. Robert Ellickson’s Order Without Law examined cattle ranchers in Shasta County, California, who settled straying-cow disputes with neighborly norms, often ignoring the legal rules, which favored ranchers in some cases and farmers in others. Informal norms of reciprocity and gossip handled most conflicts at lower cost than litigation, suggesting that law is a backstop to social order and not its foundation, a point echoing Ostrom’s findings about commons.

The flip side is the dark face of dense networks. Tight-knit communities can enforce conformity, punish deviants, and exclude outsiders. Edward Banfield’s 1958 study of a southern Italian village, The Moral Basis of a Backward Society, described “amoral familism”: the rule of maximizing the material, short-run advantage of the nuclear family while assuming everyone else does the same, which impedes collective action. Mafia organizations are themselves an extreme form of social capital, selling protection in places where the state fails to provide it, as Diego Gambetta documented in The Sicilian Mafia: the mafioso sells trust, and the demand for his product springs from distrust. Corruption is likewise a coordination equilibrium: if everyone else pays bribes, refusing means losing out, so honest individuals cannot reform the system alone. Bo Rothstein has argued that building trust requires a credible shift in expectations, which has occurred in a few cases, such as Singapore’s anticorruption drive under Lee Kuan Yew, which combined high pay for officials with an independent investigative bureau, and Georgia’s rapid reduction of petty corruption after 2004, when the government fired the entire traffic police force and rebuilt it, though the durability of such reforms varies.

Social capital’s relevance to everyday health is among the best documented findings in epidemiology. The Roseto study in the 1960s found that this Pennsylvania town of Italian immigrants had remarkably low rates of heart disease despite the residents’ diets and smoking habits, and the researchers, Stewart Wolf and John Bruhn, attributed it to close family ties and communal solidarity; as the community Americanized and the old patterns eroded, heart disease rose to match the national average. A larger body of research shows that social isolation predicts mortality comparable to smoking, as in Julianne Holt-Lunstad’s meta-analysis of over three hundred thousand people, which found that strong social relationships were associated with about a fifty percent greater likelihood of survival over the study period. The Harvard Study of Adult Development, which has followed the lives of several hundred men and later their families for over eighty years, concluded that the quality of close relationships at midlife was the strongest predictor of health and happiness in old age, more than cholesterol or wealth. Loneliness has been called an epidemic, and the US Surgeon General issued an advisory on it in 2023, though some researchers caution that the data on whether loneliness has increased over time are less clear than the headlines imply, while the health consequences of chronic isolation are not in doubt.

The coda I find most hopeful is how readily trust can be rebuilt through small, repeated acts of cooperation. Experimental economics has a standard tool, the trust game: one player may send money to another, the amount triples in transit, and the recipient may send some back. Pure self-interest predicts that nothing is sent, yet in experiments around the world participants typically send about half and receive back roughly the amount sent, with significant variation across societies that correlates with the strength of market integration and of community norms, as found in the cross-cultural studies by Joseph Henrich and colleagues. The pattern is encouraging. People extend trust as an act of faith, are reciprocated more often than a cynic would expect, and learn from the experience. Societies might thus be seen as large trust games with institutions shaping the payoff structure, and policy as an effort to shift the equilibrium from suspicion toward confidence.

Which leads me, by way of the networks that carry both trust and rumor, to the mathematics of networks themselves, a subject that has transformed epidemiology, sociology, and the study of the internet, beginning with the odd fact that you are probably only a few handshakes away from almost anyone on Earth.

The “six degrees of separation” idea has a murkier origin than its fame suggests. In 1967 the psychologist Stanley Milgram sent packets to a few hundred people in Nebraska and Kansas, asking them to forward the packet to a stockbroker in Boston by passing it only to someone they knew on a first-name basis. Of the chains that reached the target, the median length was about six. The result was always shakier than the legend: most chains never finished, with completion rates in some runs around a fifth, and the starting groups were not random samples. Later work, including a 2003 email experiment by Duncan Watts and colleagues involving some sixty thousand participants and eighteen targets worldwide, found chains of similar length among those that completed, while stressing how many broke off, and when Facebook measured its own graph of billions of users in 2011 the average distance came out around 4.7 steps. The core fact holds up: in a world of billions, the typical separation between two people is tiny.

The puzzle is why. Most of our friends know each other, so social networks are highly clustered, and clustered networks would seem to have long paths, since your friends’ friends are mostly your own friends. A random network has short paths but no clustering. In 1998 Watts and Steven Strogatz showed that you can have both. Start with a ring lattice, where each node connects only to its nearest neighbors, giving high clustering and long paths. Rewire just a small fraction of the links to random distant nodes, and the average path length collapses while clustering barely changes. A handful of long-range shortcuts is enough. These “small-world” networks turned out to describe the neural wiring of the worm C. elegans, the power grid of the western United States, and the collaboration network of film actors, which gave rise to the game of Six Degrees of Kevin Bacon, and the mathematicians’ cousin, the Erdős number, which counts collaboration steps to the prolific Paul Erdős. This is Granovetter’s weak ties in mathematical form: the acquaintances you rarely see are the shortcuts.

A second discovery concerned the distribution of connections. In a random network, most nodes have roughly the average number of links, with a bell-shaped spread. But when Albert-László Barabási and Réka Albert mapped the World Wide Web in 1999, they found something else: a few sites with enormous numbers of links and a vast majority with very few, a heavy-tailed pattern called a power law. They proposed an explanation, preferential attachment: new nodes tend to link to nodes that already have many links, so the rich get richer. The same logic had appeared earlier in Herbert Simon’s work, in Derek de Solla Price’s studies of citations, and in Robert Merton’s “Matthew effect” in science, named for the line about those who have being given more. Whether real networks are as universally scale-free as claimed has been disputed; a 2019 statistical analysis by Anna Broido and Aaron Clauset of nearly a thousand networks found that strictly scale-free structure is rare, and that log-normal distributions often fit as well. But heavy tails of some kind are everywhere, and they matter because they change how networks behave.

They matter for robustness. A network with hubs is resilient against random failure, since most nodes are minor and losing them hardly matters, but it is fragile against targeted attack, since removing the hubs shatters it. Airlines illustrate the point: closing a few major hubs would disrupt travel far more than closing hundreds of small airports. The same structure explains why the internet survives random router outages yet has weak points, and why supply chains with a few critical suppliers are vulnerable, as the semiconductor shortage after 2020 showed. Cascading failures are the other danger. The 2003 northeastern North American blackout began with a few transmission lines in Ohio sagging into trees; an alarm system failed, load shifted to other lines, they tripped in turn, and within minutes about fifty million people lost power. Power grids, financial systems, and ecosystems share this feature: components are coupled so that stress redistributed from a failed node can overload its neighbors. Studies of the 2008 crisis by Andrew Haldane and Robert May argued that the interbank lending network, designed to spread risk, had become a channel for contagion, and that diversification which helps individual banks can make the system as a whole more fragile when everyone holds similar assets.

Epidemics are the most consequential application. In the simplest model, the SIR model of Kermack and McKendrick from 1927, a population is divided into susceptible, infected, and recovered, and an outbreak grows if each case infects more than one other on average, the basic reproduction number R0 exceeding one. The fraction that must be immune to stop spread is one minus one over R0, so measles, with an R0 around twelve to eighteen, needs about ninety-five percent immunity, which is why small drops in vaccination bring outbreaks. But the simple model assumes everyone mixes equally, and real contact networks are not like that. Heavy-tailed contact patterns mean some people, “superspreaders,” infect many others while most infect none; for SARS-CoV-2, studies suggested that roughly ten to twenty percent of cases accounted for most onward transmission, with clusters in crowded indoor settings such as choir rehearsals and bars. The value of R0 alone hides this: a disease with a modest average and high variability behaves differently, with more explosive local clusters and also a greater chance that a seeded outbreak simply fizzles. Interventions that target hubs, closing the highest-risk settings or vaccinating the most connected, outperform uniform ones. Contact tracing works best when it looks backward as well as forward, finding the event that infected a cluster, since the source is more likely to be a superspreading occasion than the individual case.

Information and behavior spread over networks too, though not always like viruses. Simple contagion, such as the transmission of a germ or a piece of news, needs only one exposure. Complex contagion, as Damon Centola has shown, applies to behaviors that carry cost or risk, such as joining a protest or adopting a new health practice, where people need reinforcement from several sources before acting. His experiments with online health communities found that clustered networks, where a person’s contacts reinforce one another, spread behaviors better than random long-range networks, the opposite of what works for simple information. So weak ties carry news but strong, overlapping ties carry commitment, a distinction relevant to the study of social movements and to why a rumor can circle the globe while a norm changes slowly, one community at a time. Nicholas Christakis and James Fowler reported that obesity, smoking cessation, and happiness seem to spread through social ties in the Framingham study data, out to three degrees of separation, but statisticians, including Russell Lyons, criticized the methods, since homophily, the tendency of similar people to connect, can mimic contagion in observational data. Disentangling influence from selection remains among the hard problems of social science.

Homophily itself generates striking results. Thomas Schelling’s 1971 segregation model placed two kinds of agents on a grid, each content so long as at least a third of its neighbors were of its own kind, and moved discontented agents to vacant spots at random. Even with such mild preferences, with no one demanding a majority, the grid evolved into sharply segregated blocks. The lesson was that large-scale patterns need not reflect large-scale intentions: mild individual preferences can aggregate into outcomes that nobody wanted, a theme I touched on with flocks and Life. It does not show that real segregation arises that way, since housing discrimination, lending policy, and zoning played enormous roles, but it demonstrates why the absence of overt racism in individuals cannot guarantee integrated outcomes, and it is a model of how local rules produce global structure.

Another network wrinkle is the friendship paradox, discovered by the sociologist Scott Feld in 1991: on average, your friends have more friends than you do. It sounds like an insult but is simple arithmetic: popular people appear on many friend lists, so when you sample a random friend, you are more likely to land on a well-connected person. The same bias applies to disease, which suggested a clever tool, used by Christakis and Fowler in a 2010 study of a flu outbreak at Harvard: monitor a random sample of students’ friends rather than the students themselves, since the friends are more central and get infected earlier, producing an early-warning signal about two weeks ahead of the general outbreak. The principle also underlies a tactic in public health for vaccinating without mapping the whole network: ask random people to name a friend and vaccinate the friend, thereby reaching hubs without knowing who they are, an elegant way of using a statistical illusion.

Networks reach into biology at every scale. Within a cell, proteins interact in networks whose hubs tend to be essential, so that deleting a highly connected protein is more likely to be lethal. Food webs, mapped by ecologists since Charles Elton, show that species are linked by feeding relationships in structures that are more stable when they have modular organization and many weak interactions, as Robert May argued in 1972 that large random ecosystems should be unstable, a mathematical finding that appeared to contradict the field’s intuition that diversity breeds stability and which prompted decades of work on what structures make complexity sustainable. The brain, with its eighty-six billion neurons, is organized as a network with small-world features, dense local clusters, and hub regions, and the study of the “connectome,” the full wiring diagram, has been completed for the nematode worm’s 302 neurons, for the fruit fly larva, and in 2023 for the fruit fly brain of about a hundred and thirty thousand neurons, with a mouse cubic millimeter of cortex mapped as well. A worm’s complete wiring diagram has been known since the 1980s, and it has not yet yielded a full understanding of how it behaves, a sobering reminder that structure does not directly give function.

Networks of ideas follow the same mathematics. Scientific citation networks reveal how fields cluster and when a paper bridges disciplines, and studies by Brian Uzzi and colleagues found that the most influential papers tend to combine a conventional base of well-trodden references with a small number of unusual pairings, a blend of familiarity and novelty. Language itself can be viewed as a network, with words as nodes and associations as links, and the early work of Collins and Loftus on spreading activation in semantic memory modeled how thinking of “doctor” primes “nurse.” Wikipedia’s link structure lets one reach most articles in a few clicks, and the amusing regularity that following the first link of an article tends to lead eventually to Philosophy for a large share of pages is an artifact of how articles are conventionally written, defining a thing by a more general category. The deeper theme is that connection patterns, not just components, determine what systems can do, and that is a satisfying place to land, since I began with a hinge, an object defined by its constraint on how two things are connected.

Having traced connection through societies and cells, I’ll turn next to its opposite number, the question of what happens when networks fail to connect: the science of isolation and the strange stories of populations cut off from each other, including islands, where evolution runs its most revealing experiments.

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Islands are natural laboratories because they strip a problem down. A patch of land surrounded by water has a countable number of species, a known age, and a measurable distance from the nearest source of colonists, which makes it possible to ask questions about biodiversity that are nearly unanswerable on continents. Robert MacArthur and E. O. Wilson, in their 1967 book The Theory of Island Biogeography, proposed that the number of species on an island reflects a balance between two rates: immigration of new species, which falls as the island fills up, and extinction of existing ones, which rises as it fills. Where the curves cross sits an equilibrium richness, with species continually turning over even as the total stays roughly constant. Big islands have lower extinction rates, since populations are larger, and near islands have higher immigration rates, so the model predicts that species number rises with area and falls with isolation. It is among the most quantitatively successful ideas in ecology, and the empirical species-area relationship, in which richness scales roughly as area raised to a power between about 0.15 and 0.35, means that a tenfold reduction in habitat typically costs about half the species.

The most dramatic test was run by Wilson and his student Daniel Simberloff in the Florida Keys in 1966 and 1967. They surveyed the arthropods on tiny mangrove islets, then hired an exterminator to tent them and fumigate, leaving them sterile. Within a year, the islands had been recolonized, and the number of species had returned to roughly the original level, though the particular species differed somewhat. The experiment confirmed the idea of dynamic equilibrium and was neat enough to be taught for decades. Its modern importance lies in conservation: a forest reduced to a fragment is a kind of island in a sea of farmland, and the theory predicts that it will gradually shed species, a process called relaxation. The long-running Biological Dynamics of Forest Fragments Project in the Brazilian Amazon, begun in 1979 by Thomas Lovejoy, isolated plots of one, ten, and a hundred hectares and has documented exactly this: the small fragments lost birds, primates, and many insects, and trees near the edges died faster, as wind and drying penetrated from the clearings. The debate over whether it is better to have a single large reserve or several small ones, the SLOSS debate, never resolved cleanly, but the lesson that fragments leak diversity is well established, and the case for corridors linking them rests on the same reasoning.

Islands also produce odd evolution. Species that colonize them often change in size in predictable ways, a pattern called the island rule: large animals shrink, and small ones grow. Pygmy elephants and hippos once lived on Mediterranean islands, and dwarf mammoths survived on Wrangel Island off Siberia until about four thousand years ago, long after their mainland relatives were gone. In 2003, excavations on the Indonesian island of Flores revealed the remains of Homo floresiensis, a small-bodied hominin about a meter tall, nicknamed the hobbit, living there until perhaps fifty thousand years ago alongside dwarf stegodons and giant rats. Whether it was an island-dwarfed descendant of Homo erectus or arose from an even earlier lineage remains argued. On the other side, rodents and reptiles balloon: the Komodo dragon, the giant tortoises of the Galápagos and Aldabra, the extinct giant rats and flightless birds of New Zealand, including the moa, up to three meters tall, hunted to extinction within a couple of centuries of human arrival around 1300 CE. Flightlessness is itself an island specialty. Without predators, wings are an expensive luxury, and the dodo of Mauritius, the kakapo of New Zealand, and the flightless rails of dozens of Pacific islands all lost the ability to fly. The same naivete made them defenseless against rats, cats, and humans. Birds on islands account for a large fraction of recorded bird extinctions, and archaeologists estimate that the settlement of Pacific islands by Polynesians wiped out perhaps a thousand species of flightless rails alone.

The great evolutionary radiations occur when a colonist arrives on empty islands with many unfilled niches. Darwin’s finches, a group of about fifteen species descended from one ancestral flock that reached the Galápagos, diversified in beak shape to exploit different seeds. Peter and Rosemary Grant spent more than forty years on the small island of Daphne Major measuring beaks and survival, and showed evolution happening in real time: in the drought of 1977, when small soft seeds ran out and only large hard ones remained, the average beak depth of the medium ground finch increased measurably in a single generation, because birds with bigger beaks survived and bred. Hawaii is the champion, where a single ancestral fruit fly arrival or a few of them gave rise to roughly a thousand species of Drosophila, nearly a third of the world’s total, and a lineage of finches became the honeycreepers, with beaks ranging from the thick seed-cracker to the long curved nectar probe. Many have gone extinct since human arrival, victims of introduced mosquitoes carrying avian malaria, rats, and habitat loss. African cichlids in the great rift lakes are the aquatic analog: Lake Victoria alone has harbored hundreds of species that evolved within roughly fifteen thousand years, as the lake dried out and refilled, a rate of speciation that astonishes biologists, followed by the catastrophic introduction of Nile perch in the 1950s, which eliminated a large share of them.

Isolation matters for human populations too, and some of the cleanest evidence about cultural and genetic history comes from them. Genetic drift, random fluctuation in gene frequencies, is strong in small populations, so a founder effect can leave a population with unusual frequencies of particular variants. The Amish of Pennsylvania descend from a few hundred founders, and carry elevated rates of certain recessive conditions, such as a form of dwarfism with polydactyly described in the Lancaster County community. Tristan da Cunha, a speck in the South Atlantic and the most remote inhabited island on Earth, was settled by a small group of families in the early nineteenth century, and its residents have a high frequency of a gene associated with asthma, which made them a valuable research population. The Pingelap atoll in Micronesia has an unusually high frequency of a hereditary colorblindness, achromatopsia, traced to a typhoon around 1775 that cut the population to about twenty survivors, one of whom carried the variant. Oliver Sacks wrote about it in The Island of the Colorblind. These examples illustrate that the pool of variation in any group is partly an accident of history.

Isolation also tests how much knowledge a society can sustain, which returns me to the Tasmanian case I raised earlier. On Easter Island, or Rapa Nui, the story told by Jared Diamond in Collapse is of a society that deforested its own island, built too many giant statues, and fell into warfare and cannibalism. Archaeologists such as Terry Hunt and Carl Lipo have challenged it, arguing that the main damage to the palm forest came from Polynesian rats that ate the seeds, that the population was never as large as claimed, and that the real catastrophe came after European contact in the eighteenth century, with introduced diseases and the Peruvian slave raids of the 1860s that took away much of the population, including the keepers of the rongorongo script. A more recent analysis of the ancient DNA from Rapa Nui skeletons found no evidence of a prehistoric population crash, and also revealed contact with the Americas before European arrival, since some of the islanders carried Native American ancestry. The picture of self-inflicted ecocide may thus be partly a modern parable projected onto the evidence. Whatever the truth, such debates show how a small isolated society becomes a screen for our anxieties about the global one, since the Earth, too, is an island of sorts, with no neighboring continent to colonize if we exhaust it.

The opposite of isolation, the sudden breaking of it, has its own history, and some of the most consequential biology of the past five centuries followed from it. When Europeans reached the Americas, they unleashed the Columbian Exchange: crops, animals, and pathogens moved between hemispheres that had been separated for fifteen thousand years or more. Native American populations, with no prior exposure to smallpox, measles, and other Old World diseases, suffered catastrophic mortality, estimated by most historians at fifty to ninety percent over a century or so, so large that, in one hypothesis advanced by Alexander Koch and colleagues, the regrowth of forests on abandoned farmland drew down enough atmospheric carbon dioxide to contribute to the cooling of the Little Ice Age, a claim that remains debated. In the other direction went potatoes, maize, tomatoes, and cassava, which transformed Old World agriculture: the potato supported population growth in northern Europe, and its dependence on a few genetically uniform varieties set the stage for the Irish famine of the 1840s, when blight destroyed the crop and about a million people died, with another million or more emigrating. Horses, reintroduced to the Americas, remade Plains Indian societies within a few generations. Alfred Crosby, who coined the term, wrote that the exchange created a more homogeneous world, with the same crops and weeds and pests spreading everywhere.

Biological invasions are the continuing consequence. A species freed from its predators and parasites can explode in numbers in a new range: rabbits in Australia, introduced in 1859 by a landowner who wanted something to hunt, spread across the continent at rates of over a hundred kilometers a year, and the deliberate release of the myxoma virus in 1950 killed some ninety-nine percent initially, followed by rapid evolution of resistance in the rabbits and attenuation in the virus, a textbook case of coevolution in real time. Cane toads, brought to Queensland in 1935 to control beetles, have spread west and are evolving longer legs at the invasion front. Zebra mussels, brought in ballast water to the Great Lakes in the 1980s, now clog intake pipes at a cost of hundreds of millions of dollars. Burmese pythons released in Florida have devastated mammal populations in the Everglades. The brown tree snake nearly eliminated Guam’s native forest birds after World War II. Most introduced species fail to establish, and most of those that do cause no major harm, a rule of thumb known as the tens rule, in which roughly ten percent of arrivals establish and ten percent of those become pests, but the few that do have an outsized effect. Islands, with their naive faunas, are especially vulnerable, which is why strict biosecurity at the borders of New Zealand, where arriving travelers may have their boots inspected and cleaned, is more than bureaucratic fussiness. Predator Free 2050, the country’s ambitious plan to eradicate rats, stoats, and possums, treats the entire nation as one conservation island, and eradication campaigns on smaller islands have already been successful: South Georgia declared itself rat-free in 2018 after years of baiting with helicopters, and bird populations have rebounded visibly.

The recurring lesson from isolation is that diversity is made by separation and unmade by contact, and that both forces matter. Without isolation there would be no finches of different beaks, no distinct languages, no regional cuisines. Linguists estimate that about seven thousand languages are spoken today, and that around half may disappear within this century, since small, isolated communities are being absorbed into larger networks; Papua New Guinea alone has over eight hundred languages, many with only a few thousand speakers, a density achieved through generations of valley-by-valley separation. The preservation of such diversity is a different problem from preserving species, since languages can’t be banked in a zoo, and have to live in the mouths of people who judge the cost and benefit of speaking them. Revival efforts offer cautious hope: Hebrew was revived as a spoken language in the nineteenth and twentieth centuries from largely liturgical use, Welsh has stabilized through schooling and public support, and Māori language nests, the kōhanga reo, have helped bring back a language that had been dwindling, though the degree of recovery varies.

I’ll let this thread run into the deepest isolation we know, the oceans’ abyss and the polar ice, where life persists under conditions we once thought sterile, and where the study of extremophiles is reshaping what we think is possible for living things.

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Extremophiles overturned an assumption so basic that nobody had thought to state it: that life needs a temperate, watery, oxygen-friendly world like the one we happen to live in. The first crack came in 1966, when Thomas Brock, a microbiologist, took a look at the hot springs of Yellowstone, where the textbooks said nothing could live above about 73 degrees Celsius. He found pink and yellow mats of bacteria in water well above that. One of them, Thermus aquaticus, turned out to carry an enzyme that stays functional at near-boiling temperatures, and in the 1980s Kary Mullis used that enzyme, Taq polymerase, to automate the polymerase chain reaction. Each cycle of PCR heats DNA to separate its strands, which would destroy an ordinary enzyme, and the heat-stable one from a Yellowstone spring survives it. So the technology behind forensic DNA, genetic testing, and the rapid COVID tests rests on curiosity about a pool of hot water in Wyoming, which is as clean an example as any of unplanned payoff from basic research.

The record holders keep moving. Methanopyrus kandleri, isolated from a hydrothermal vent, grows at about 122 degrees Celsius, and the pressure at depth keeps the water liquid. Pyrolobus fumarii, from a Mid-Atlantic Ridge vent, was the first known to grow at 113. At the other end, microbes live in Antarctic ice and in brine channels within sea ice, active at temperatures well below freezing. Some organisms thrive in acid with a pH near zero, like the archaeon Picrophilus, and others in alkaline soda lakes, as in the Rift Valley. Halophiles bloom in saturated brine, coloring salt-evaporation ponds pink with their pigments. Deinococcus radiodurans, nicknamed Conan the Bacterium, survives radiation doses thousands of times what would kill a human, because it can reassemble its shattered genome from fragments, probably an adaptation to desiccation, which damages DNA in similar ways, with radiation resistance a byproduct. And tardigrades, the eight-legged water bears a fraction of a millimeter long, can dry out into a state called a tun, survive vacuum, radiation, and temperatures near absolute zero, and revive when moistened. Experiments in 2007 exposed them to open space on a European satellite, and some survived. They do it with special proteins, some unique to them, that appear to vitrify their cell contents into a glassy state, an echo of the glass puzzle I mentioned earlier.

Life has also been found in the deep subsurface, and the scale of it is startling. Drilling into ocean sediments and continental rock has turned up microbes kilometers down, and estimates by the Deep Carbon Observatory, a decade-long international program that reported in 2018, suggest that the deep biosphere may hold something like fifteen to twenty-three billion tonnes of carbon, with the total number of cells enormous, though estimates are rough and have been revised. Many of these organisms live extremely slowly, dividing perhaps once in centuries, using energy at rates so low that conventional definitions of growth hardly apply. Some feed on hydrogen produced when radioactive decay splits water molecules in rock, a process called radiolysis, so a community can run on energy that has nothing to do with the sun. In a South African gold mine, kilometers deep, researchers found a bacterium, Desulforudis audaxviator, that appears to live alone, a one-species ecosystem, supplying its own carbon, nitrogen, and energy from the surrounding rock and water. The finding suggested that a self-sufficient microbial world could exist inside a planet, with no need of a surface.

Under Antarctica, the ice has hidden lakes. Lake Vostok, buried beneath roughly four kilometers of ice and sealed off for perhaps fifteen million years, was reached by a Russian drilling team in 2012, and the question of whether its water holds life has been contested, with contamination from drilling fluid making the results hard to read. A cleaner test came in 2013, when American researchers drilled into Lake Whillans, a subglacial lake closer to the surface, and found a diverse microbial community feeding on chemicals from ground-up rock. In 2018, a sample from beneath the ice of the Mercer lake system revealed more, and in 2015 and later years scientists found that Antarctic subglacial water systems are interconnected, flowing and draining on timescales of years, rather than being static, sealed time capsules. The ice itself is a record of Earth’s history, and meltwater beneath it is a thermostat for ice flow, which is why such lakes matter for sea-level projections as well as microbiology.

All this reshaped astrobiology. If life thrives in boiling vents, frozen brine, and rock, then the habitable zone around a star is too narrow a notion. Europa, a moon of Jupiter, has a global ocean beneath an icy shell, kept liquid by tidal flexing as Jupiter squeezes and releases it, and the ocean may touch a rocky seafloor, where vents could occur. Enceladus, a small moon of Saturn, sprays plumes of water vapor and ice grains from fractures near its south pole, and the Cassini spacecraft flew through them in 2008 and later, detecting salts, organic molecules, silica grains that imply hydrothermal activity, and molecular hydrogen, a possible food source for methane-producing microbes. In 2023 phosphorus was reported in the plume ice, completing the set of key elements. None of this shows life exists there; it shows that conditions known to support life on Earth may be present. NASA’s Europa Clipper, launched in October 2024, is meant to reach the Jupiter system around 2030 and fly by Europa dozens of times, and ESA’s JUICE is also headed there. Titan, Saturn’s largest moon, has lakes of liquid methane and ethane, and some researchers have speculated about exotic chemistry using them as solvents, though no organism is known to work that way, and the NASA Dragonfly rotorcraft, planned to launch in 2028, will explore its surface.

Mars is the nearer test. Today its surface is cold, dry, and bathed in radiation, but billions of years ago it had rivers, lakes, and possibly an ocean, and rovers have found minerals formed in water, including clays and sulfates. Perseverance, which landed in Jezero Crater in February 2021, is collecting rock samples from an ancient river delta and caching them for a possible return to Earth, and in 2024 it found a rock with features, spots and bands, that on Earth are associated with microbial activity, nicknamed “Cheyava Falls,” which the team cautiously described as a potential biosignature, one that could also have formed abiotically, and which only laboratory analysis could settle. The sample return program has faced cost problems and redesigns, and I don’t know where it stands now, so that’s another thing to check. The broader philosophical lesson from Mars is how hard it is to demonstrate life. The 1976 Viking landers carried experiments that gave an initially positive signal for metabolism in soil, which most scientists later attributed to reactive chemistry, notably perchlorates discovered by the Phoenix lander in 2008. And in 1996, the announcement that the Martian meteorite ALH84001 contained structures resembling fossil microbes stirred worldwide excitement, and the claim was eventually undermined by alternative explanations for each piece of evidence. Carl Sagan’s dictum that extraordinary claims require extraordinary evidence is the standard here, and it is a demanding one, since the same ambiguity attaches to the earliest traces of life on Earth, where the oldest putative microfossils, about 3.5 billion years old in Western Australia, have been argued over for decades, with some researchers holding they are mineral artifacts.

The question that sits behind all of this is the origin of life, and there the field is both lively and unsettled. Stanley Miller’s 1952 experiment, in which he passed sparks through a flask of water, methane, ammonia, and hydrogen, produced amino acids, and showed that the building blocks of life could form from simple ingredients, though the early atmosphere he assumed is now thought to have been less reducing than he supposed. Competing pictures place the origin in warm ponds, where wet-dry cycles could concentrate and link molecules, as Darwin speculated in a letter, or in alkaline hydrothermal vents, where natural gradients across thin mineral walls resemble the proton gradients that all cells use to make energy, the hypothesis developed by Michael Russell and Nick Lane. The “RNA world” hypothesis proposes that RNA, which can both carry information and catalyze reactions, preceded DNA and proteins, supported by the discovery of ribozymes and by the fact that the ribosome’s catalytic core is itself RNA. John Sutherland’s group has shown plausible routes to make nucleotides and other precursors from simple chemicals under conditions plausible for early Earth, using ultraviolet light and cyanide chemistry, which addressed an old difficulty about how the pieces could be assembled. Yet no one has made a living cell from scratch, and the gap between a soup of molecules and a self-reproducing, evolving system remains wide. Jack Szostak’s lab has been building protocells, fatty-acid vesicles that grow, divide, and can enclose replicating RNA, narrowing it.

The tree of life itself has been redrawn by this work. In 1977, Carl Woese used ribosomal RNA sequences to show that the microbes called archaea, which looked like bacteria, form a domain of their own, as distinct from bacteria as either is from us, which restructured biology’s top-level classification into three domains. Many archaea are extremophiles, though others live in ordinary soil and oceans. In 2015 a group found Lokiarchaeota, in sediments near a hydrothermal field called Loki’s Castle, with genes previously thought exclusive to eukaryotes, and later Asgard archaea provided the best evidence yet that the complex cells of animals, plants, and fungi arose from a merger between an archaeon and a bacterium, the mitochondrial event I described earlier. Following the logic back, the last universal common ancestor, LUCA, was perhaps a hot-loving, hydrogen-using organism living near vents, according to a 2024 genomic reconstruction that put it surprisingly early, around 4.2 billion years ago, though such reconstructions rest on many assumptions and have been contested.

That adds up to a humbling pattern. Each time scientists defined the limits of life, some organism went past them, and the limits turned out to be set more by the availability of liquid water and an energy source than by temperature or pressure alone. Water is the common thread: where it is absent, as in the hyperarid core of the Atacama Desert, one of the driest places on Earth, microbial life becomes sparse and sometimes undetectable, which is why that desert serves as a Mars analog. Energy, in the form of chemical disequilibrium, is the other requirement, and life can be understood as a way of exploiting gradients, a point Erwin Schrödinger made in What Is Life? in 1944, when he said that organisms feed on negative entropy. This brings us back to Landauer, Boltzmann, and the thermodynamic bookkeeping I discussed before: a living thing maintains its order by exporting disorder, and it needs a constant flow of usable energy to do so.

I’ll turn from the energy that powers living things to the mind’s own energy budget, and to the strange economics of the brain, an organ that is two percent of body weight and consumes about a fifth of the body’s energy, whose design reflects trade-offs as tight as any in the deep sea.

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The brain is an expensive organ, and its expense shapes almost everything about it. At rest it burns roughly twenty watts, about the power of a dim light bulb, drawing on glucose and oxygen delivered by a vascular network so dense that no neuron sits more than a few cell widths from a capillary. In a newborn the share is far higher: close to half of the body’s energy goes to the brain, which is one reason human infants are so helpless and so fat, since the fat serves as a reserve for a brain that cannot be allowed to run short. Leslie Aiello and Peter Wheeler proposed in 1995 the “expensive tissue hypothesis”: since a large brain costs so much, hominin evolution must have paid for it somewhere, and they suggested the gut shrank as diet quality improved. Richard Wrangham added a more specific candidate, cooking, which predigests food and raises the energy yield, so that the control of fire made our big brains affordable. The evidence on the timing is disputed, with archaeological traces of controlled fire sparse before about a million years ago, and later comparative studies of mammals found that brain size does not trade off cleanly against gut size, but the underlying point about energy as a binding constraint stands.

Where the energy goes is itself interesting. Most of it is spent not on thinking in any deliberate sense but on the upkeep of neurons’ electrical readiness, chiefly the sodium-potassium pumps that restore ion gradients after each signal. The firing of a neuron is a brief collapse of those gradients, and the cell then has to pay to rebuild them. Simon Laughlin, Peter Sterling, and others have argued that neural design is dominated by this energy accounting: the brain keeps most neurons mostly silent, with average firing rates of only a few spikes per second, because sparse coding is cheap, and wiring is arranged to minimize the length and thickness of axons, since long, thick cables cost more to build and run. Myelin, the fatty sheath wrapped around axons by glial cells, speeds conduction by letting the signal jump between gaps, called nodes of Ranvier, and also saves energy, since far fewer ions need to be pumped back. The loss of myelin in multiple sclerosis shows how much depends on it. The brain’s cost-minimizing logic helps explain the folding of the cortex, which packs a large surface into a skull of limited size and keeps connected regions close together.

Compare it with a computer. A modern data center training a large AI model can draw megawatts, and even a single high-end processor uses hundreds of watts, whereas the brain, doing things that machines still find hard, such as navigating cluttered rooms, understanding speech in noise, and learning from a handful of examples, runs on twenty. Part of the difference is architecture. A conventional computer separates memory from processing and shuttles data between them, at heavy cost, while in the brain, memory and computation are intertwined in the same synapses. Neurons are also analog and noisy, and communicate with sparse spikes rather than continuous clocked values. Neuromorphic engineering, pioneered by Carver Mead in the 1980s, tries to copy these principles in silicon, with chips like IBM’s TrueNorth and Intel’s Loihi that compute with spikes, and they achieve large energy savings on some tasks, though they have not displaced conventional hardware, partly because we lack learning algorithms that exploit them as well as the brain does. How the brain learns remains open: the backpropagation algorithm that powers modern deep learning requires precise error signals sent backward through the network, which has no obvious biological counterpart, and a good deal of theoretical work, by Timothy Lillicrap, Blake Richards, and others, has explored how neurons might approximate it with local mechanisms.

The synapse, where one neuron talks to another, is the site of most learning. Donald Hebb proposed in 1949 that when one cell repeatedly helps fire another, the connection between them strengthens, a rule summarized as cells that fire together wire together. The phenomenon was found in the 1970s by Tim Bliss and Terje Lømo in rabbit hippocampus, where brief high-frequency stimulation produced a lasting increase in synaptic strength, called long-term potentiation, and its counterpart, long-term depression, weakens connections. At the molecular level, a receptor named NMDA acts as a coincidence detector, opening only when the sending neuron releases its chemical messenger and the receiving neuron is already depolarized, which implements Hebb’s rule almost literally. Memories are then thought to be stored in patterns of altered synaptic strength distributed across populations of neurons, which Richard Semon had called engrams in 1904, and modern techniques make them tangible. Susumu Tonegawa’s group identified neurons active during a mouse’s learning of a fearful experience, then used light to reactivate just those cells later, which was enough to make the mouse freeze, and they even implanted false memories by activating a place’s engram while delivering a shock elsewhere. The synaptic story has complications: memories can persist while the underlying synapses turn over, so the code must be more subtle than a fixed set of weights, and some researchers argue that stability emerges from network dynamics.

The brain also rewires itself, to a degree that defied the older doctrine that the adult brain is fixed. Michael Merzenich’s experiments on monkeys in the 1980s showed that cortical maps of the hand reorganize after a finger is lost or after intensive training. London taxi drivers, who memorize the city’s maze of streets for the licensing exam known as the Knowledge, were found by Eleanor Maguire to have a larger posterior hippocampus than controls, growing with years of experience. Blind people recruit visual cortex for touch and language, and in Braille readers the occipital cortex is functionally engaged in reading. After a stroke, remaining regions can take over lost functions, and constraint-induced therapy, which forces use of the weak limb, harnesses that capacity. Plasticity is not unlimited, and it is greatest in critical periods early in life: Hubel and Wiesel showed in kittens that closing one eye during a window in early development leaves the visual cortex permanently wired to favor the open eye. A child born with cataracts that are not corrected in time may never develop normal vision, even after surgery. Yet some plasticity persists through life, and drugs and training that reopen critical-period-like states are an active research area.

Hemispheres and the notion of left-brain and right-brain types deserve some debunking. The two halves are specialized in some ways, language most often depends on the left and spatial attention more on the right, and the work of Roger Sperry and Michael Gazzaniga on “split-brain” patients, whose corpus callosum was cut to control severe epilepsy, produced vivid demonstrations, such as the right hand being unable to name an object shown only to the left visual field. In these patients the left hemisphere readily invented explanations for actions that the right had initiated, which Gazzaniga called the interpreter, and which suggests that our sense of a unified self with reasons for what it does is partly a narrative constructed after the fact. But popular claims that people are “left-brained” analytical or “right-brained” creative have no support. In a large neuroimaging study from the University of Utah, no evidence emerged that individuals consistently favor one side’s networks. The two hemispheres work together constantly, and nearly every task involves both.

The chemistry of the brain is a further story with a recurring moral about oversimplification. Dopamine is called the pleasure chemical, but the experiments of Wolfram Schultz showed that dopamine neurons in monkeys respond to reward prediction errors: they fire when a reward is better than expected, stay flat when it’s as expected, and dip when it’s worse, a teaching signal that matches the temporal difference algorithms of reinforcement learning, a fruitful bridge between neuroscience and machine learning. It is better described as a signal of “wanting” or motivation than of enjoyment, as Kent Berridge’s work separates the wanting and the liking systems, which helps explain addiction, in which craving persists after pleasure fades. Serotonin, linked to mood and the target of common antidepressants, has a more tangled picture. The idea that depression is caused simply by low serotonin has not held up well in a 2022 umbrella review, though the drugs help many patients, and how they work remains uncertain; the mechanisms may involve slower changes in plasticity and network function. Similar caution applies to oxytocin, called the love hormone in popular accounts, where early reports of effects on trust have not replicated robustly. Single-molecule stories are attractive but the brain deals in systems, with each chemical playing multiple roles depending on the receptor, region, and context.

Alongside neurons, the brain’s other cells, the glia, have been undervalued. They were long regarded as mere glue, the name coming from the Greek for it, but astrocytes regulate blood flow, clear neurotransmitters, and take part in synaptic signaling, microglia prune synapses during development and act as immune cells, and oligodendrocytes make myelin. The old claim that glia outnumber neurons by ten to one was corrected by Suzana Herculano-Houzel, who counted cells by dissolving brains into suspensions of nuclei, and found about eighty-six billion neurons and a similar number of non-neuronal cells in the human brain. Her work also showed that the human brain is, in terms of neuron count, a scaled-up primate brain rather than anything exotic, with the cost of cooked food paying for it, which she emphasized, and that elephants, despite a much larger brain by mass, have fewer cortical neurons than we do.

The glymphatic system, described by Maiken Nedergaard in 2012, is a network of channels around blood vessels through which cerebrospinal fluid flows into brain tissue and flushes out waste, and it appears to be most active during sleep, which ties back to the proposal that sleep serves a clearance function. Whether the clearance of molecules such as amyloid beta, implicated in Alzheimer’s disease, is disrupted by poor sleep is under investigation, and the idea has drawn both enthusiasm and critical challenge about how the flow works. Alzheimer’s disease itself is a human tragedy and a scientific puzzle: the amyloid hypothesis, that accumulation of amyloid plaques drives the disease, guided decades of drug development that failed in trial after trial, until antibodies such as lecanemab, approved in the United States in 2023, showed a modest slowing of decline in early-stage patients, which supports the hypothesis while also showing how limited the benefit is and how expensive and risky the treatment. Prevention may matter more: the Lancet Commission has estimated that about forty-five percent of dementia cases might be delayable by addressing modifiable risk factors such as hearing loss, hypertension, physical inactivity, smoking, and social isolation, though such estimates carry uncertainty.

The deepest feature of the energy story may be the constraint it puts on thinking itself. Daniel Kahneman’s distinction between fast intuitive processes and slow deliberate ones can be read partly as an economy of attention: deliberate reasoning is costly, and so the mind falls back on habits and heuristics unless provoked. Studies measuring pupil dilation show that effortful tasks produce measurable arousal, and the popular claim that willpower depletes like a muscle drawing on glucose has failed in large replication efforts, so the cost of mental effort is probably better understood as a matter of opportunity cost and motivation than of fuel running out. Still, the intuition behind it is sound: a system with a fixed power budget and a vast number of possible computations must choose what to compute. The brain’s ordinary solution is prediction, spending effort only on surprises, which links back to the predictive processing story and to Shannon’s insight that information is surprise.

This brings me to a subject the brain’s economy makes unavoidable, the question of what emotions are for, since feelings are, on one account, the brain’s compact summaries of bodily state and situational value, and the science of them has been overturned in the last two decades.

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The folk theory of emotion is that each one is a distinct natural kind with its own circuit, a fingerprint in the face, and an ancient evolutionary pedigree. Fear lives in the amygdala, disgust in the insula, anger is a hot flush with a scowl. Darwin set the template in The Expression of the Emotions in Man and Animals in 1872, and Paul Ekman gave it empirical muscle in the 1960s and 70s by showing photographs of faces to people in many cultures, including the Fore of Papua New Guinea, who had little contact with outsiders, and reporting that they matched six faces to emotion stories at rates well above chance: happiness, sadness, fear, anger, surprise, and disgust. For decades this “basic emotions” view was the textbook consensus, and it underwrote everything from airport screening programs to the television drama Lie to Me.

The challenge has been sustained and, I think, substantial. Lisa Feldman Barrett, James Russell, and others point out that the original experiments forced participants to choose from a short list of labels, which inflates agreement, and that when people are allowed to describe faces freely, or when studies sample more isolated communities, such as the Trobriand Islanders, the matching drops sharply. In a 2019 review of a thousand-odd studies, Barrett and colleagues concluded that people do not reliably make the “expected” facial configuration when feeling an emotion: scowls occur in a minority of angry moments, and people scowl when concentrating or when they have gas. The face is better thought of as a context-dependent signal, more like a word whose meaning depends on the sentence than a readout. A well-known demonstration by Hillel Aviezer showed that, when a face is seen alone, a victorious tennis player’s scream of joy reads as agony, and the body posture and situation carry the real information. Ekman and his defenders reply that some universality survives and that real expressions are often brief and suppressed, so the dispute is partly about what threshold of reliability counts as universal.

Neuroscience has complicated the localization story too. The amygdala is indeed crucial in fear conditioning, as Joseph LeDoux’s work on rats showed, and patients with damage to it, such as S.M., a woman studied by Antonio Damasio’s group, who had bilateral amygdala lesions from a rare disease, showed little fear in situations that would terrify most people, including a haunted house and handling snakes. But LeDoux himself has argued that the circuit is better described as a system for detecting and responding to threat, and that the conscious feeling of fear is constructed elsewhere, in cortical networks that interpret what the body and brain are doing. Meta-analyses of neuroimaging data find that no brain region is selectively active for a single emotion category; the amygdala responds to novelty, ambiguity, and positive arousal as well as threat, and the insula participates in nearly everything. The pattern looks less like a set of emotion organs than like distributed networks used flexibly.

Barrett’s own alternative is the theory of constructed emotion, which builds on the predictive processing picture. On this account, the brain’s primary job is to regulate the body, a process called allostasis, in which it predicts the body’s needs and acts before they arise, as a thermostat anticipating a cold night rather than reacting to it. Sensations from the body, such as a racing heart, a tight stomach, or a flush of warmth, are ambiguous, and the brain interprets them using concepts learned from culture and experience, so that the same bodily state might become fear before a performance and excitement before a roller coaster. The underlying dimensions, which are often called core affect, are valence, from unpleasant to pleasant, and arousal, from calm to activated, and emotion categories are the brain’s way of making sense of them in context. The classic evidence for something like this comes from Stanley Schachter and Jerome Singer’s 1962 experiment, where participants injected with adrenaline reported different emotions depending on whether a confederate acted euphoric or angry, though the experiment has been criticized and has proved hard to replicate cleanly. A more reliable finding is that people who have finer-grained emotional vocabulary, what Barrett calls emotional granularity, tend to regulate their feelings better and are less prone to certain mood problems, consistent with the idea that having more precise concepts gives the brain more precise predictions.

The theory has critics, who argue that it underplays evolved, specialized circuits, evidence from animal research for dedicated defensive systems, and cross-cultural regularities in things like the sound of laughter and crying. Jaak Panksepp argued from experiments in rats and other mammals that there are at least seven primary-process emotional systems, which he capitalized as SEEKING, RAGE, FEAR, LUST, CARE, PANIC/GRIEF, and PLAY, each tied to subcortical circuits that can be triggered by electrical stimulation. Rats emit ultrasonic vocalizations during play and when tickled, around fifty kilohertz, and will chase the experimenter’s hand for more, which Panksepp took as evidence of a rudimentary joy. The disagreement between these camps often turns on what is meant by “emotion”: whether it refers to behavioral and physiological programs shared across mammals, or to the subjective feeling that humans label with words. If the latter, the constructionists have a point that the feeling is not an automatic output of a dedicated circuit; if the former, the animal work stands. A sensible reading is that both are partly right: evolution supplied some preset response programs, and human experience layers learned categories on top.

The body is more involved than the older cognitive picture allowed. William James proposed in 1884 that we do not run because we are afraid but are afraid because we run: the feeling is the perception of bodily changes. Carl Lange had a similar theory, and later experiments tempered the strong version, since people with spinal cord injuries retain emotions, though some report reduced intensity. Damasio’s somatic marker hypothesis proposed that bodily signals guide decision-making, supported by patients with ventromedial prefrontal damage, like the famous Phineas Gage, a railroad foreman who survived an iron rod through his frontal lobe in 1848 and, by contemporary accounts, changed in personality, though the accounts are patchy and probably exaggerated. Elliot, one of Damasio’s patients, scored normally on intelligence tests after tumor surgery but became unable to make decisions, agonizing over trivial choices and making ruinous financial ones, which suggested that emotion is not the enemy of reason but a component of it. Interoception, the sense of the body’s interior, varies among people, and those who are better at detecting their own heartbeat tend to report more intense emotions, while differences in interoception are being explored in anxiety, depression, and autism, though the measures remain contested.

Emotion regulation is where the science gets practical. James Gross distinguishes strategies by when they act: situation selection and modification, attention deployment, cognitive reappraisal, and response suppression. Reappraisal, reinterpreting a situation so that it has a different emotional meaning, such as viewing a job interview as a challenge rather than a threat, generally reduces negative emotion and doesn’t carry the costs of suppression, which tends to leave the physiological arousal intact while impairing memory and making one’s partner in conversation feel less rapport. Experiments by Jeremy Jamieson found that students told that the racing heart before an exam is the body preparing for performance did better than controls, and showed more adaptive cardiovascular responses. Labeling feelings, as Matthew Lieberman’s work suggests, reduces amygdala activity, an effect called affect labeling, which offers a neural rationale for the old advice to name what you feel. Cognitive behavioral therapy rests on versions of these ideas, as does the broader premise that our emotional lives are partly products of the interpretations we make, which are open to revision. None of this implies that feelings are simply choices; chronic stress, loss, illness, and circumstances produce real suffering that is not dissolved by reframing, and sound versions of these techniques are a supplement to, not a substitute for, changing difficult situations.

Emotion and morality meet in the work of Jonathan Haidt, whose social intuitionist model argues that moral judgments are usually driven by fast gut reactions, with reasoning serving mostly to justify them after the fact, his “emotional dog and its rational tail.” In his famous scenarios, such as consensual sibling incest with contraception, people felt strongly that it was wrong, and when their stated reasons were dismissed, they were left “morally dumbfounded,” still convinced. Disgust seems to play a role in moral judgment, since experiments in which people are exposed to a bad smell or sit at a dirty desk sometimes make them harsher, though several of those studies have failed to replicate. Haidt proposed that moral intuitions rest on a set of foundations, care, fairness, loyalty, authority, sanctity, and later liberty, which differ in how much weight various groups give them, with conservatives drawing on all of them and liberals relying mostly on care and fairness. The theory has been criticized for its empirical basis and for how foundations are defined, but it has shaped how researchers think about moral disagreement as a clash of intuitions as well as of arguments. Against it, Joshua Greene’s dual-process account of the trolley problem finds that the personal, emotional engagement of pushing a man off a bridge triggers a more deontological response, while flipping a switch evokes a utilitarian calculus, and reasoning and emotion pull in different directions. The debate about whether reason can ever do more than rationalize is an old one, going back to Hume’s claim that reason is, and ought only to be, the slave of the passions, and to Kant’s opposing hope; the present empirical picture suggests something in between, with reasoning able to revise intuitions especially when people discuss things together and encounter views that conflict with their own.

Laughter belongs in any honest account, since it is among the most puzzling and universal of emotional expressions. Robert Provine, who studied it by eavesdropping in shopping malls and on city streets, found that most laughter does not follow jokes at all: it follows banal remarks like “I’ll see you guys later” and serves mainly a social function, signaling affiliation, smoothing interactions, and punctuating speech. People are about thirty times more likely to laugh in company than alone. Rats and chimpanzees show play vocalizations, and it is plausible that human laughter descends from the panting of ape play. Theories of humor point to incongruity, a setup that primes one interpretation and a punchline that overturns it, to superiority, to relief of tension, and, in one recent account by Peter McGraw, “benign violation,” in which something is threatening but also safe, which would explain why tickling works only when it’s done by someone we trust and why jokes about tragedy become funny with enough distance, an old observation of Mark Twain’s that humor is tragedy plus time. The perception of humor involves the brain’s prediction machinery, the pleasure of resolving a surprise, and so loops back, once again, to the theme of expectation and its violation, which seems to follow me from music to jokes.

Another overlooked emotion, awe, is a good candidate for a topic on which the research is young and promising. Dacher Keltner and Jonathan Haidt defined it as a response to vast things that challenge our existing frameworks of understanding, and experiments find that experiencing awe, by looking up at towering eucalyptus trees, for example, makes people more generous, more humble, and less self-focused, and lowers markers of inflammation. Astronauts report the “overview effect,” a shift in perspective on seeing Earth as a fragile sphere, a theme described by Frank White in 1987. Nature, music, collective ceremony, and great ideas provoke it, and some researchers suggest it may help bind people into groups larger than themselves, working against the isolation described earlier. That a feeling of smallness can enlarge our sense of connection is not paradoxical so much as characteristic of how emotions work: they are not simply private states but social instruments, tuned over evolutionary and cultural time to coordinate individuals.

The broader point I take from this literature is that emotions are neither the irrational intruders of the old Stoic picture nor simple hardwired reflexes, but a form of the brain’s modeling of what matters, shaped by biology, learning, and culture. That is a useful transition into the question of how culture shapes the mind more generally, because the research on emotion has been one front in a larger revolution: the discovery that much of what psychologists took as universal human nature was drawn from a peculiar slice of humanity, and the next subject is that revision, the WEIRD problem and the new cross-cultural psychology.

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The acronym comes from a 2010 paper by Joseph Henrich, Steven Heine, and Ara Norenzayan, who looked at the published literature in the behavioral sciences and noticed an embarrassing fact: the overwhelming majority of research participants came from societies that were Western, Educated, Industrialized, Rich, and Democratic. In a survey of top psychology journals, about ninety-six percent of samples came from Western industrialized countries, home to roughly twelve percent of humanity, and a large share were American undergraduates, often enrolled in introductory psychology courses. Arguably the most studied creature in the history of science was the college sophomore in a lecture hall. That would be harmless if human minds were interchangeable, but the paper’s central argument was that, on a range of measures, WEIRD people are statistical outliers, sometimes at the extreme end of the world distribution, which makes them a poor basis for generalizing about our species.

The best-known demonstration is the Müller-Lyer illusion, in which two lines of equal length look unequal because of arrowheads at their ends. In the 1960s, Marshall Segall, Donald Campbell, and Melville Herskovits tested it across a range of societies and found that susceptibility varied enormously: American adults were strongly fooled, while San foragers of the Kalahari were barely affected at all. The leading explanation is the “carpentered world” hypothesis: people raised among rectangular buildings, straight roads, and right-angled corners learn to interpret such configurations as depth cues, and the illusion is a byproduct of that learning. Even something as basic as how we see turns out to be tuned by environment. The same logic has been applied to many other domains, and the results have been uncomfortable for universal claims.

Take fairness. In the ultimatum game, one player proposes how to split a sum of money and the other can accept or reject; if rejected, both get nothing. Rational self-interest predicts that the responder should accept any positive offer, and the proposer should offer the minimum. In Western samples, people offer around forty to fifty percent and reject offers below about twenty. A large cross-cultural project led by Henrich, with anthropologists playing the game among fifteen small-scale societies, found enormous variation. Among the Machiguenga of Peru, the average offer was about a quarter, and nearly none were rejected, while among the Au and Gnau of Papua New Guinea, proposers sometimes offered more than half and recipients rejected generous offers, apparently because accepting gifts creates obligations to reciprocate. The strongest predictors of fair offers were the degree of market integration and the payoff to cooperation in a society’s daily economic life. Notions of fairness that seemed natural turned out to be partly a product of living among strangers in markets, which echoes the earlier discussion of trust.

Individualism is another dimension on which the WEIRD sample is unusual. Hazel Markus and Shinobu Kitayama argued in 1991 that Western cultures foster an independent self, defined by personal attributes, preferences, and choices, while many East Asian cultures foster an interdependent self, defined by relationships and roles. The differences appear in surprising places. Shown an underwater scene, Americans tend to describe the focal fish, while Japanese participants more often describe the background and relationships among objects, a contrast documented by Richard Nisbett and Takahiko Masuda and framed by Nisbett as analytic versus holistic cognition. In the “framed-line test,” participants must reproduce a line either in absolute length or relative to the surrounding frame, and they differ systematically by culture. East Asians are more likely to attribute behavior to context, while Americans lean toward dispositional explanations, the fundamental attribution error, which turns out to be less fundamental than its name suggests. The origins of these differences are debated: one proposal links them to rice versus wheat farming, since paddy rice requires coordinated irrigation and labor exchange, while wheat does not. Thomas Talhelm found that within China, people from rice-growing southern regions score as more holistic and interdependent than those from wheat-growing northern regions, even among neighbors in the same province, an intriguing result, though the causal story remains contested.

Even the supposed universals of happiness and self-esteem look different. East Asians tend to show less of a self-enhancement bias, the tendency to rate oneself above average, which Westerners display routinely, and some studies find that they are motivated more by self-improvement than self-protection. Choice has a different valence too: Sheena Iyengar and Mark Lepper found that Anglo-American children were more motivated when they chose their own task, while Asian-American children were more motivated when their mothers or peers chose for them. Such results caution against exporting a theory of motivation based on autonomy as though it were a law of human nature. And the standard finding of cognitive dissonance, that people justify their decisions after making them, appears in a form that depends on whose decisions are at stake.

Henrich pushed the argument further in The WEIRDest People in the World, in 2020, proposing a historical explanation for why Western Europeans came to be psychologically distinct. His thesis is that the medieval Western Church, by banning cousin marriage, polygamy, and other practices that sustained extended kin networks, dissolved the clan and tribe structures that dominated most of the world, and that this pushed people toward voluntary associations, impersonal institutions, and an individualistic psychology, which laid the groundwork for markets, universities, and representative government. He and colleagues support this with data showing that the length of exposure to the Western Church correlates with measures of individualism, trust of strangers, and rule-following across European regions, and that the prevalence of cousin marriage predicts psychological differences at a global scale. Critics argue that the thesis is too sweeping, that it neglects other institutional and economic developments, and that the data are correlational. It is an ambitious attempt to link cultural evolution, psychology, and economic history, and whether or not it is right in detail, it takes seriously the idea that minds are shaped by institutions across centuries.

A related worry is how many findings in the behavioral sciences hold across cultures at all. The Many Labs projects and the Psychological Science Accelerator, a network of hundreds of laboratories around the world, have tried to test classic effects in diverse samples, and while some effects replicate widely, others weaken or vanish outside the original context, and effect sizes often vary by site. A 2018 study attempting to replicate twenty-one social science experiments from Nature and Science found that about sixty-two percent replicated, with effect sizes about half the original’s. Overgeneralizing from narrow samples is not the only source of failure, but it is one of the clearest, and it has inspired reforms: reporting of participant characteristics, larger international collaborations, and a stronger norm of acknowledging constraints on generality.

There’s a deeper puzzle about what counts as universal. Some features of human psychology do seem to hold across all known societies. Donald Brown compiled a list of human universals: language, music, kinship terms, the incest taboo, a concept of fairness, gossip, play, the use of fire, and reciprocity. Basic color terms follow a constrained evolutionary sequence, as Brent Berlin and Paul Kay argued in 1969: languages with only two color terms distinguish dark from light, and those with three add red, and so on in a predictable order, though the details have been revised. Infants everywhere seem to share core expectations about objects, number, and agents, such as that objects don’t pass through each other, according to Elizabeth Spelke’s research on core knowledge. Human universals tend to be abstract, with considerable cultural variation in how they are realized. The relationship between the two is like a grammar and its languages: shared architecture, diverse surface forms.

This has methodological implications that extend beyond psychology. Any dataset reflects who was sampled, whose problems were considered worth studying, and whose language the instruments were written in. Medical research has had its own version, where clinical trials historically enrolled mostly men, so that drug dosages and symptom profiles for women, such as heart attack presentation, were poorly characterized, and the Mercator problem returns: a model fitted in one region applied elsewhere with misplaced confidence. Genomics has a comparable skew, since in recent years about eighty percent of participants in genome-wide association studies have been of European ancestry, which limits the accuracy of polygenic risk scores in other populations. Machine learning has the same issue, where systems trained on data from a narrow slice of humanity perform worse on the rest. The cure is not mere inclusion for its own sake but recognizing that variation is itself data: studying how a phenomenon differs across contexts reveals its mechanisms, which is what the Müller-Lyer cross-cultural work did.

The honest lesson of the WEIRD critique is a humbling one for a species that likes to think it understands itself. Much of what we call human nature has been inferred from a thin and peculiar layer, and the full range of human minds is wider than the layer suggests. At the same time, the critique should not collapse into relativism. Cultural differences are real but bounded, built on a shared biological foundation, and part of the work is to map which aspects are fixed, which are flexible, and why. That is a fine place to pivot to the question of how cultures change over time, how ideas, techniques, and norms are transmitted and selected, which brings in the field of cultural evolution and its odd borrowings from genetics.

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The most unsettling finding in the psychology of groups is how little it takes to make one. In the early 1970s, Henri Tajfel and his colleagues at Bristol brought schoolboys into a laboratory and sorted them into groups on trivial grounds, in some versions by whether they preferred paintings by Klee or Kandinsky, and in others, to their astonishment, by the toss of a coin. The boys never met their group-mates, would never interact with them, and gained nothing personally from favoring them. Yet when asked to divide small sums of money between an anonymous member of their own group and an anonymous member of the other, they reliably gave more to their own. More striking, they often preferred a division that maximized the gap between the groups even when it meant that their own group received less in absolute terms. Tajfel called these the minimal group experiments, and they showed that categorization alone, without conflict, history, or material interest, is enough to produce favoritism. His social identity theory, developed with John Turner, proposed that part of our self-esteem comes from the groups we belong to, so that we are motivated to see them as distinctive and superior.

The older view held that prejudice required a reason, usually competition for scarce resources. Muzafer Sherif’s Robbers Cave experiment in 1954 supported this. He took two groups of twelve-year-old boys to a summer camp in Oklahoma, kept them apart at first so that each formed its own identity, naming themselves the Rattlers and the Eagles, and then introduced tournaments with prizes that only one team could win. Hostility escalated quickly into name-calling, raids on each other’s cabins, and fights. The conflict was eased not by friendly contact alone but by superordinate goals that neither group could achieve alone: when the camp’s water supply failed, or a truck carrying their food broke down, both groups had to pull the rope together. The study has been criticized in recent years after the historian Gina Perry examined Sherif’s archives and found that an earlier attempt, at a camp in New York, had failed to produce the hoped-for hostility, because the boys directed their aggression at the experimenters rather than at each other, and that the Oklahoma staff had a hand in stoking the rivalry. So it cannot be taken as a pure demonstration of spontaneous tribalism. It does still fit the broader literature that competition inflames and shared goals reduce conflict.

The biological layer is surprisingly specific. Oxytocin, popularly called the cuddle hormone, turns out in the work of Carsten De Dreu and colleagues to promote trust and generosity toward in-group members while leaving the attitude toward outsiders unchanged or even harsher, so that it appears to tune parochial altruism rather than universal kindness. Brain imaging finds that people show reduced empathic response, in regions that respond to others’ pain, when watching someone from a rival group suffer, and increased activity in reward regions when a rival fails, a pattern called schadenfreude that Mina Cikara has studied in sports fans. Infants appear to prefer people who speak their native language, and the experiments of Kiley Hamlin and Karen Wynn on preverbal babies’ reactions to helpful and hindering puppets suggest that young children prefer those who are nice, but also, in some studies, that they prefer those who are mean to individuals unlike themselves. The interpretation of these baby studies is contested, and several have proven difficult to replicate, so care is needed.

Where does the tendency come from? One influential hypothesis, defended by Samuel Bowles, is that between-group warfare in the Pleistocene favored the evolution of parochial altruism, a package of in-group cooperation and out-group hostility, since groups with more self-sacrificing members would defeat those with fewer. His models suggest this is plausible if intergroup conflict was frequent and genetic differences among groups were large enough, which is a contentious empirical assumption. Anthropologists disagree on how violent prehistoric life was. Skeletal evidence from sites like Jebel Sahaba in Sudan, about thirteen thousand years old, shows a high proportion of violent deaths, and the Kenyan site of Nataruk preserves what looks like a massacre of about ten thousand years ago. But Douglas Fry and others argue that war in the organized sense is largely an invention of settled societies with property and hierarchy, and that foraging bands were mostly peaceable, with high rates of intermarriage and fluid membership across groups. Contemporary hunter-gatherers are no clean guide, since they too have been shaped by contact with states. Richard Wrangham distinguishes reactive aggression, the hot-blooded kind, from proactive, planned aggression, and proposes that humans have unusually low levels of the first, through a process of self-domestication, and unusually high capacity for the second, because we can coordinate coalitions. Chimpanzees conduct lethal raids on neighboring communities, as Jane Goodall documented at Gombe in the 1970s in what came to be called the Gombe Chimpanzee War, but bonobos, their equally close relatives, are far less violent, so the primate evidence points both ways.

Whatever its origin, the boundary of “us” is remarkably flexible, which is the hopeful part. Studies of identity show that people belong to many groups at once, and which one is salient depends on context. In a classic experiment by Jay Van Bavel, participants who had been assigned to a minimal team showed a bias in the brain’s response to faces, but the effect was altered when a different group category was made salient, and Mark Levine’s studies in Manchester found that football fans were more willing to help an injured stranger wearing their club’s shirt than one wearing a rival’s or a neutral shirt, but were equally willing to help the rival fan if he was framed as fellow football fan. Recategorization works: widening the “we” to include the former “they” reduces bias, which was the logic of Sherif’s superordinate goals and of Samuel Gaertner and John Dovidio’s common ingroup identity model. The history of nation-building is, in a sense, a long exercise in such widening. Eugen Weber’s Peasants into Frenchmen described how, in the late nineteenth century, rural people who identified with their village, and often spoke a regional language, were turned into citizens through schools, railroads, conscription, and newspapers, and Benedict Anderson called nations imagined communities, since their members will never meet most of their fellows but picture themselves in communion with them. The Christmas truce of 1914, in which British and German troops on parts of the Western Front climbed out of their trenches to exchange gifts and play football, shows how thin the boundaries could be even in the middle of a war, and how quickly military authorities moved to prevent a repetition.

The most robust evidence for loosening boundaries comes from the contact hypothesis, which I mentioned earlier. Gordon Allport specified in 1954 that contact reduces prejudice under four conditions: equal status within the situation, common goals, cooperation, and the support of authorities or norms. Thomas Pettigrew and Linda Tropp’s meta-analysis of more than five hundred studies and hundreds of thousands of participants found that intergroup contact typically does reduce prejudice, and that the effect holds even when Allport’s conditions aren’t fully met, though it is larger when they are. Friendship is the most powerful form: Stephen Wright and Art Aron’s “extended contact” research shows that merely knowing that a member of your group has a friend in the other group can improve attitudes. Elizabeth Paluck’s field experiments have gone further, finding that a radio soap opera in Rwanda promoting norms of dissent and reconciliation shifted listeners’ perceived social norms and behaviors, though not their private beliefs, which indicates that what changes is often what people think is acceptable to do. A study by Paluck and Donald Green on perspective-taking, in which canvassers asked residents of Miami to recall a time they were judged for being different, produced lasting reductions in prejudice toward transgender people after a ten-minute conversation, a result later reinforced by Joshua Kalla and David Broockman in follow-up experiments. The evidence also contains sobering notes: Robert Putnam’s finding about diversity and trust, which I cited earlier, and Ryan Enos’s study of Boston commuters, in which exposure to Spanish-speaking people on a train platform made riders more exclusionary, suggest that mere proximity, especially when brief and unstructured, may heighten the sense of threat.

Two ancient and modern lessons seem to come out of this literature. The first is that identities are constructed and manipulable, which cuts both ways. Hutu and Tutsi in Rwanda were categories sharpened by Belgian colonial administrators, who issued identity cards in the 1930s and favored the Tutsi minority, and in 1994, radio broadcasts and local authorities mobilized neighbors to kill neighbors in about a hundred days, with a death toll of roughly eight hundred thousand. Studies of that period, and of others such as the Yugoslav wars, show how quickly previously mixed communities can polarize under the pressure of elites who profit from fear. The second lesson is that individuals in such situations often act with extraordinary courage. Samuel and Pearl Oliner’s research on rescuers of Jews in Nazi-occupied Europe found that they were distinguished less by heroic personality than by an upbringing that emphasized inclusive, general rules of care, and by having had prior ties with people outside their own group, with many saying afterward that they had no choice. Such people were rare but not absent, and their existence is as much a fact about human nature as the atrocities.

Present-day politics has its own version, called affective polarization: the growing dislike between partisans, documented by Shanto Iyengar and colleagues in the United States, where the proportion of people who say they would be upset if their child married someone from the other party has risen from a few percent in 1960 to a third or more. It has been measured by thermometer ratings, implicit association tests, and behavior in experiments, in which people discriminate by party more than by race in some tasks. Research by the More in Common project and others shows that people greatly overestimate how extreme and hostile the other side is, a “perception gap” that is widest among the most politically engaged, which suggests that correcting misperceptions can help, and field experiments providing accurate information have reduced partisan animosity modestly, though the effects tend to fade. Whether social media drives polarization is contested, with a set of large studies done in collaboration with Meta around the 2020 election finding that changing the feed algorithm for a few months had little effect on attitudes, even as the results do not rule out longer-term or cumulative effects. Polarization is not a single trend across countries either: it has risen sharply in the United States but declined in some other democracies, which points toward the role of party systems and elite behavior.

The ancient puzzle of how to live with difference has several partial remedies, none sufficient alone: crosscutting memberships, so that a person’s allies on one issue are opponents on another, which James Madison’s argument about factions in Federalist 10 anticipated; institutions that give each group a stake in the whole; shared projects and rituals; and norms that stigmatize dehumanization. The science tends to confirm the old intuition that people are neither hardwired haters nor natural universalists but conditional cooperators and conditional rivals, responsive to the structure they find themselves in.

I’ll move on to a very different kind of boundary, the one between what is inside a cell and what is outside it, and to the architecture of the molecular machines that make the cell’s work possible, a story of engineering at the scale of billionths of a meter.

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A cell is not a bag of chemicals. That older picture, a membrane enclosing a soup of enzymes, has been replaced by something closer to a factory floor: crowded, organized, and full of machines. A typical bacterial cell is a micron or two long, yet it packs several million protein molecules, and a human cell holds on the order of ten billion. The cytoplasm is so dense with macromolecules that it behaves less like water than like a thick gel, and a protein moving through it spends its time bumping into neighbors. Diffusion, which is hopelessly slow over centimeters, is fast over a micron, which is one reason cells are small: a molecule can wander from one side to the other in about a second, whereas a cell the size of a grapefruit would need to rely on transport systems for everything. Much of cell biology is the story of how living things beat the limits of diffusion with directed machinery.

The best-known machine is the ribosome, the structure that reads messenger RNA and strings amino acids into proteins. It is built from several rRNA molecules and around eighty proteins, and the catalytic heart of it, as Venki Ramakrishnan, Thomas Steitz, and Ada Yonath showed in the crystallographic work that earned the 2009 Nobel Prize, is RNA rather than protein, a relic that supports the RNA world hypothesis I described earlier. A bacterial ribosome adds around twenty amino acids per second, with an error rate of roughly one in ten thousand, and a cell can contain tens of thousands of them, so that ribosome production dominates the budget of a fast-growing bacterium. The accuracy is not free. The ribosome uses a proofreading step, spending energy to check that each incoming transfer RNA matches the codon, an application of John Hopfield’s kinetic proofreading idea from 1974: by adding a second, energy-consuming checkpoint, a system can achieve error rates far lower than the equilibrium difference in binding energies would allow. The principle recurs throughout biology, in DNA replication, in immune recognition, and in the discrimination of the right amino acids for each tRNA.

DNA replication is perhaps the most astonishing performance. The polymerase copies a bacterial genome of about four and a half million base pairs in under an hour, with an error rate after proofreading and mismatch repair of about one in a billion to ten billion per base. The replication fork is a moving assembly: a helicase unwinds the double helix at around a thousand bases per second, primase lays down short RNA primers, and the polymerase works continuously on one strand, the leading strand, but in short pieces, Okazaki fragments, on the other, since synthesis only proceeds in one direction. A sliding clamp keeps the polymerase attached, and the whole complex has been compared to a train laying its own track. In humans, replication begins at tens of thousands of origins at once, and the cell must finish before dividing, with checkpoints that halt the cycle if damage is detected. Damage is constant: each human cell suffers tens of thousands of DNA lesions per day from metabolism and radiation, and repair systems, whose study earned Tomas Lindahl, Paul Modrich, and Aziz Sancar the 2015 Nobel Prize, patch most of them. The occasional failure is the seed of cancer, and also of the variation on which evolution works, so the mutation rate is itself an evolved compromise, set at a level that balances accuracy against adaptability.

Cells move cargo with molecular motors that are, quite literally, tiny walkers. Kinesin, discovered in the 1980s, strides along microtubules, the cell’s hollow protein tracks, carrying vesicles and organelles toward the cell periphery. Each step is eight nanometers, taken by alternating two “heads” in a hand-over-hand motion, powered by the hydrolysis of one ATP molecule per step, and videos made by single-molecule microscopy show it plodding along at about eight hundred nanometers per second, dragging a cargo many times its size through the crowd. Its cousin dynein walks the other way, and myosin, which pulls on actin filaments, powers muscle contraction. A muscle is an array of billions of myosin motors pulling in concert. The ATP synthase enzyme, which makes the cell’s energy currency, is a rotary motor: protons flowing through a membrane-embedded ring spin a central shaft, and the rotation forces the catalytic sites in the head to change shape and assemble ATP. In 1997, Hiroyuki Noji and colleagues attached a fluorescent filament to the shaft of a single molecule and watched it turn under a microscope, a direct view of a rotary engine measured in nanometers. Your body makes and consumes roughly its own weight in ATP every day, each molecule recycled hundreds of times.

Bacteria have a rotary motor of another kind, the flagellum, a propeller driven by a ring of stator proteins that turns at hundreds of revolutions per second and can reverse direction, steering the cell by switching between smooth swimming and tumbling. It became a cause célèbre when Michael Behe cited it as an “irreducibly complex” system that could not have evolved, and the scientific response has been that its parts have homologs with other functions: the stator proteins resemble ion channels, and the export apparatus at the base is related to the type III secretion system, which injects toxins. A bacterium swimming in a drop of water lives in a world dominated by viscosity rather than inertia, which Edward Purcell described in his 1977 lecture “Life at Low Reynolds Number”: if you stop pushing, you stop immediately, and a reciprocal motion goes nowhere, which is why a scallop-like flapping would fail and why microbes use corkscrews and rotating tails. For a bacterium, swimming in water is like a human swimming in molasses.

Another dimension of cellular organization has recently overturned the textbook. Besides membrane-bound organelles, cells contain structures with no membrane at all, such as the nucleolus, stress granules, and P bodies. Clifford Brangwynne, Anthony Hyman, and colleagues showed around 2009 that such compartments behave like liquid droplets, formed by liquid-liquid phase separation, in which proteins with flexible, disordered regions condense out of the cytoplasm much as oil separates from water. Their dynamics, fusion, dripping, and rapid exchange of contents, were observed in living cells. The idea has been enormously productive: such condensates could concentrate reactants, speed reactions, and be assembled and dissolved quickly by signals, and defects in their physical state have been linked to neurodegenerative diseases like ALS, where droplets harden into solid aggregates. There is also controversy about how widely the mechanism applies in living cells as opposed to test tubes, since many observations come from overexpressed proteins, and some critics warn against calling every punctum a condensate. But the discovery that a cell can organize itself with the physics of mixtures, without a wall, widened the picture of what organization means.

Proteins themselves are the machine parts, and their shapes were long the central mystery. A protein is a chain of amino acids that folds into a specific three-dimensional structure within milliseconds to seconds, and Cyrus Levinthal pointed out in 1969 that the chain could not be searching all conformations at random, since even a small protein has more possible shapes than there are atoms in the universe. Folding must follow a funnel-shaped energy landscape, guided toward the native state. Christian Anfinsen had shown in the 1950s that an unfolded enzyme could refold on its own, so the sequence determines the structure, and yet predicting the structure from the sequence defied computation for fifty years. In 2020, DeepMind’s AlphaFold2 reached accuracy comparable to experiment on most of the targets in the CASP competition, and in 2021 and after, the predicted structures of hundreds of millions of proteins were released in a public database. The achievement was recognized by the 2024 Nobel Prize in Chemistry, shared by Demis Hassabis and John Jumper with David Baker, who had pioneered computational protein design. The predictions don’t give the dynamics, don’t solve every case, such as proteins with disordered regions or complexes with many partners, and say little about function by themselves, but they changed the daily practice of structural biology, and Baker’s group is now designing novel proteins, enzymes, and binders that do not exist in nature.

The boundary of the cell, the membrane, deserves a final word, because it is not a passive wrapper. It is a lipid bilayer about five nanometers thick, a two-dimensional fluid in which proteins float, as Singer and Nicolson described in the fluid mosaic model of 1972, and it is selectively permeable, with channels and pumps controlling what crosses. Aquaporins, whose structure Peter Agre revealed, pass water molecules in single file at billions per second while blocking protons, a feat of selective chemistry that earned a Nobel Prize in 2003. Ion channels let neurons fire, and the potassium channel selects potassium over the smaller sodium by about ten thousand to one, using a filter lined with carbonyl oxygens that mimic the water shell around a potassium ion, a structure solved by Roderick MacKinnon. The membrane maintains the gradient on which all of cellular energetics depends, and Peter Mitchell’s chemiosmotic theory, proposed in 1961 and long ridiculed, explained how: cells use the electrochemical gradient across a membrane as a store of energy, whether in mitochondria or bacteria, which brings me back to the vents where I suggested life may have begun, with natural proton gradients across mineral walls.

What strikes me in all of this is how much of the cell’s apparent purposefulness arises from physics operating at a scale where thermal noise dominates. Molecular machines work in a hurricane of random collisions, and rather than fighting it, they exploit it: a motor protein is better understood as a Brownian ratchet, a device that rectifies random motion by coupling it to chemical energy and asymmetry, as Feynman’s lecture on the ratchet and pawl suggested. Richard Feynman said famously that there is plenty of room at the bottom, and life got there first. Engineers trying to build molecular machines learn from them, and the 2016 Nobel Prize in Chemistry went to Jean-Pierre Sauvage, Fraser Stoddart, and Ben Feringa for synthetic molecular machines, tiny rotors and elevators and cars.

Which sets up the next subject naturally: when the machinery is not merely studied but rewritten, as in gene editing and synthetic biology, where the questions of what can be done and what should be done are tangled together.

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Gene editing has a strange origin: it began as a study of how bacteria defend themselves against viruses. In 1987, a Japanese group sequencing a gene in E. coli noticed an odd pattern near it, short repeated sequences separated by unique spacers. Francisco Mojica, a Spanish microbiologist, spent years in the 1990s and 2000s working out what these were, and realized that the spacers matched pieces of viral genomes. The repeats, now called CRISPR, for clustered regularly interspaced short palindromic repeats, were a molecular memory of past infections. When a virus attacks again, the bacterium transcribes the spacer into a guide RNA, which steers a nuclease called Cas9 to the matching viral DNA and cuts it. Rodolphe Barrangou and Philippe Horvath at the yogurt company Danisco confirmed in 2007 that the system confers acquired immunity, which makes the origin of the technology commercial as well as academic, since dairy cultures are routinely attacked by phages.

The leap to a tool came in 2012, when Jennifer Doudna and Emmanuelle Charpentier, along with Martin Jinek and colleagues, showed that Cas9 could be programmed with a single synthetic guide RNA to cut any DNA sequence one chose, and Virginijus Šikšnys’s group in Lithuania published parallel findings. Early in 2013, Feng Zhang’s lab at the Broad Institute and George Church’s at Harvard showed it worked in human cells. Doudna and Charpentier received the Nobel Prize in Chemistry in 2020, and a long patent dispute between the University of California and the Broad Institute, over who invented the application in eukaryotic cells, has continued to be litigated. The reason for the excitement was practical. Earlier tools, zinc finger nucleases and TALENs, required engineering a new protein for each target, a laborious process, whereas CRISPR needed only a new twenty-letter RNA. Editing went from a specialty to something a graduate student could do in a week, and the cost fell by orders of magnitude.

The cut is only the start, because what the cell does after the break determines the edit. Most often, it repairs the break by non-homologous end joining, a quick and sloppy process that leaves small insertions or deletions and usually knocks out the gene, which is useful for disabling something. If the researcher supplies a template, the cell can use homology-directed repair to write in a precise change, but that pathway is inefficient and mostly active in dividing cells. David Liu’s laboratory at the Broad developed more refined tools that avoid double-strand breaks. Base editors, introduced in 2016, chemically convert one DNA letter to another, a C to a T or an A to a G, using a disabled Cas9 fused to a deaminase enzyme. Prime editors, in 2019, use a modified guide that carries a template and a reverse transcriptase, which can write small insertions, deletions, and all twelve kinds of point changes, a “search and replace” function. These matter because roughly half of known pathogenic human mutations are point changes, and they avoid the unpredictable damage that double-strand breaks can cause, including large deletions and chromosomal rearrangements, which have been observed in edited cells.

The clinical payoff has arrived. In December 2023, the US Food and Drug Administration approved Casgevy, a therapy developed by Vertex and CRISPR Therapeutics, for sickle cell disease, following approval in the United Kingdom, and later for beta thalassemia. The approach is elegant: sickle cell disease and thalassemia are caused by defects in adult hemoglobin, but humans make a fetal form, controlled by the gene BCL11A, that works fine and is switched off after birth. Researchers draw out a patient’s blood stem cells, use CRISPR to disable the regulatory element that silences fetal hemoglobin, and return the edited cells after chemotherapy clears the bone marrow. In trials, most patients with sickle cell disease were free of the painful crises that characterize it. The challenge is access: the treatment requires specialized hospitals and costs about two million dollars, while most patients live in sub-Saharan Africa and India, where the disease is common. In 2025, a team in Philadelphia treated an infant with a rare metabolic disorder using a bespoke base-editing therapy designed and delivered within months, a proof of concept for personalized genetic medicine, though how such efforts can be made routine and affordable is unresolved. Delivery is the other bottleneck. Editing in the liver is relatively easy, since lipid nanoparticles injected into the blood accumulate there, and early trials of editing the PCSK9 gene to lower cholesterol are under way, but getting editors into the brain, muscle, or lung remains hard.

Then there is the question of editing embryos, which crosses a line that editing a patient’s own cells does not, since changes would pass to descendants. In November 2018, He Jiankui, a Chinese biophysicist, announced that he had created twin girls whose CCR5 gene had been edited, with the stated aim of conferring resistance to HIV. The claim shocked the scientific community. The edits were imperfect and mosaic, the medical justification was weak, since the father’s HIV was controllable by standard treatment and there are well-established ways to prevent transmission, the consent process appeared inadequate, and the work was done in secret. He was sentenced to three years in prison in China, and released in 2022. International summits and commissions have since concluded that heritable editing should not proceed until safety is demonstrated and there is broad societal consensus, and most countries ban it. The question that remains is whether there is any legitimate use. Proponents point to couples where both carry a recessive disease, for whom embryo screening yields few unaffected embryos, while critics reply that screening serves nearly all such cases and that the slope toward enhancement is slippery, toward selecting for height, intelligence, or other traits, which raises concerns about inequality and about what Michael Sandel called the loss of giftedness. Polygenic embryo screening, which ranks embryos by statistical risk scores, is already offered by some companies, despite the limited predictive power of those scores for individuals and their lower accuracy outside European ancestries, as I mentioned in discussing sampling bias.

Gene drives are another way of pushing the technology to the edge of ethics, since they are designed to spread through a wild population. Ordinarily a gene is inherited by half of the offspring, but a drive carrying the CRISPR machinery cuts the other chromosome and copies itself into the break, ensuring inheritance by nearly all progeny. A drive that spreads a gene causing female sterility in Anopheles gambiae mosquitoes could, in principle, crash the populations that transmit malaria, which kills over six hundred thousand people a year, mostly children in Africa. The team led by Andrea Crisanti at Imperial College showed in cage experiments in 2018 that a drive could collapse a population in a few generations, though resistance mutations arose in other designs. The Target Malaria consortium has pursued field testing with careful community engagement in Burkina Faso, Mali, and Uganda, and the first releases were of non-drive sterile males. The risks are the stakes of a technology that cannot be recalled once released, and that might cross borders and affect ecosystems in ways hard to predict, so regulators and ethicists have developed staged approaches, from self-limiting drives to drives that require repeated releases, and the Convention on Biological Diversity has debated governance. The mosquito is a good example of how the same technology can look different depending on whether you are the one dying of malaria.

Beyond editing lies synthesis. In 2010, J. Craig Venter’s team announced a bacterial cell controlled by a chemically synthesized genome, a copy of Mycoplasma mycoides’s DNA assembled from fragments and booted up in a recipient cell, and in 2016 they reported JCVI-syn3.0, with a minimal genome of 473 genes, of which about a third had no known function, a reminder of how much we don’t know about even the simplest cell. Others have rewritten the code more thoroughly: in 2019, Jason Chin’s group at Cambridge produced a strain of E. coli with a synthetic genome that used only 61 of the 64 codons, freeing up codons to be reassigned to encode unnatural amino acids, and the international Sc2.0 project is rewriting the yeast genome chromosome by chromosome. The intention is to create organisms that are resistant to all viruses, since viruses depend on the standard code, and that can produce novel polymers. Synthetic biology has also commercial products with long stories: the antimalarial drug precursor artemisinic acid, engineered into yeast by Jay Keasling’s lab, was an early success, though economic changes in the market for plant-derived artemisinin undermined it, and companies have made vegan meat proteins and spider silk. mRNA vaccines, which I haven’t discussed, owe their existence to decades of work by Katalin Karikó and Drew Weissman, who found that substituting a modified nucleoside for uridine let synthetic mRNA evade the immune system’s alarm, enabling vaccines developed in under a year in 2020, and they were rewarded with the 2023 Nobel Prize in Physiology or Medicine.

The darker side is dual use. The same techniques that allow a lab to make a vaccine could, in principle, be used to create or enhance a pathogen. In 2002, researchers at Stony Brook synthesized poliovirus from its published sequence and mail-ordered DNA, and in 2018 a Canadian team reconstructed horsepox, a relative of smallpox, at a cost of about a hundred thousand dollars, which provoked a debate about whether publishing the methods was wise. The experiments in 2011 by Ron Fouchier and Yoshihiro Kawaoka showing that a few mutations could make H5N1 avian influenza transmissible between ferrets prompted a voluntary moratorium and a prolonged argument over the benefits of such “gain-of-function” research for surveillance versus its risks, and the origin of SARS-CoV-2, whether from natural spillover at a market or an accident connected to the Wuhan laboratories, remains unresolved in public evidence, with intelligence agencies divided and no decisive data released. What is not in doubt is that laboratory accidents happen: the 1977 influenza reappearance, widely believed to have been a lab escape, and the 1979 Sverdlovsk anthrax release, which killed at least sixty-six people, are historical examples. Biosecurity measures now include screening of synthetic DNA orders by providers, a norm, not yet a universal requirement, and a push to make such screening robust against orders split into pieces, as well as proposals to apply similar checks to AI tools that can design biological sequences, where the capabilities are advancing quickly.

I find the history of the field’s self-governance instructive. In 1975, at the Asilomar conference in California, scientists, lawyers, and physicians gathered to discuss recombinant DNA, then brand new, and agreed to a voluntary moratorium on certain experiments and to guidelines for containment. It’s often held up as a model of responsible self-regulation, led by Paul Berg, though critics note that it was dominated by scientists with limited public input and focused on the narrow technical risks while neglecting broader social questions, and that the results, as it turned out, were that most feared risks were overestimated. Since then, a series of conferences on human gene editing, in 2015, 2018, and 2023, have tried to renew the conversation with more voices. A recurring tension is between precaution and benefit: rare diseases that gene therapy could cure impose present suffering, and delay has costs, as the history of the Jesse Gelsinger case in 1999, in which an eighteen-year-old died in a gene therapy trial at the University of Pennsylvania and the field was set back by years, demonstrates the opposite danger. Both errors are real, a theme that has run through this whole conversation, from medicine’s reversals to the insurance of catastrophic risk.

Which leads me, via the thread of how to weigh benefits against risks of uncertain size, to the philosophical ground those debates stand on: ethics itself, and the long argument among consequentialists, deontologists, and virtue theorists over how we decide what we owe one another.

Environmental economics starts from a plain observation: when a factory pollutes a river, the cost lands on people who never agreed to bear it and never appear in the factory’s accounts. Arthur Pigou, in The Economics of Welfare in 1920, called such spillovers externalities and proposed that the state tax the polluter by an amount equal to the damage, so that the price of the product would reflect its full cost. The Pigouvian tax remains the textbook answer, and its logic is austere and attractive: it needs no knowledge of which firm can cut emissions cheaply, since each firm weighs the tax against its own abatement costs, and the cuts happen where they are least expensive.

Ronald Coase offered a rival insight in 1960. If property rights are clearly defined and bargaining is cheap, he argued, the affected parties can negotiate to an efficient outcome regardless of who holds the right, a rancher and a farmer with straying cattle arriving at the same fence either way. The catch is that bargaining is rarely cheap, since pollution typically involves thousands of victims and many sources, so Coase’s real lesson is about transaction costs: the choice among taxes, regulation, and markets should depend on how costly it is to negotiate and monitor. That insight underlies cap-and-trade, in which a regulator sets a total limit on emissions, issues tradable permits, and lets the market find the price. The American sulfur dioxide program, created by the 1990 Clean Air Act amendments to fight acid rain, is the usual success story: emissions fell faster and at much lower cost than industry had predicted, and estimates of the benefits, in avoided deaths and illness, dwarfed the compliance costs. The European Union’s Emissions Trading System, launched in 2005, had a rockier start, with permits handed out too generously and prices collapsing in 2007 and again after the financial crisis, but reforms, including a market stability reserve, have raised prices, and it has become a significant driver of emissions cuts in power generation.

Carbon pricing in general has a respectable record where it has been tried and a political problem where it hasn’t. British Columbia introduced a revenue-neutral carbon tax in 2008, and studies suggest it reduced fuel use without harming the economy noticeably, though effects were modest at its initial price. Sweden’s tax, begun in 1991 and now among the highest in the world, coincided with falling emissions alongside continued growth. Yet carbon taxes are visible in a way that regulations are not, and the French fuel tax increase of 2018 helped set off the gilets jaunes protests, a lesson that distribution matters: rural and low-income households spend a larger share of income on fuel and have fewer alternatives. Economists have proposed returning the revenue as equal per-person dividends, which would leave most low-income households ahead, but dividends are less salient than prices at the pump, a case of Kahneman’s loss aversion working against good policy. Several economists have also argued that the most effective measures often combine prices with standards and public investment, since prices alone work slowly where infrastructure choices, like grid design and transit, lock in behavior for decades.

The harder problem is placing a value on things markets do not trade. How much is a wetland worth, or a clean view, or the existence of a species one will never see? Economists have devised several approaches. Hedonic pricing infers value from related markets, such as how much more homes near a park sell for. The travel cost method values a recreation site by what visitors spend to reach it. Contingent valuation simply asks people how much they would pay, and it was used after the 1989 Exxon Valdez oil spill to estimate the damage to Alaska’s ecosystem, yielding a figure of several billion dollars. The method has been criticized sharply, since stated willingness to pay is sensitive to framing, tends to be insensitive to scale, and reflects what people say rather than what they do. In a well-known study, Daniel Kahneman and Jack Knetsch found that people were willing to pay about as much to clean up one lake in Ontario as to clean all the lakes in the province, the “embedding effect,” suggesting that people were expressing a general warm glow about helping, not valuing particular quantities. The disagreement is profound: some philosophers, like Mark Sagoff, argue that people hold environmental values as citizens, not as consumers, and that attaching prices to them misses the point.

A parallel strand tries to value the services ecosystems provide. In 1997, Robert Costanza and colleagues published a paper in Nature estimating the global value of ecosystem services at about thirty-three trillion dollars a year, nearly twice global output at the time, a number intended as a provocation and treated as one: it was criticized as meaningless in aggregate, since a world without ecosystems would have no economy to measure against. The more useful examples are local. New York City, in the 1990s, faced a choice between building a filtration plant for its water, estimated to cost six to eight billion dollars, and paying landowners in the Catskills to protect the watershed that purifies it, which cost around a billion and a half, and chose the latter. Mangroves protect coastlines from storm surge, pollinators contribute to the yield of a large share of crops, and the economic value of pollination alone has been estimated in the hundreds of billions of dollars globally, though the figures depend heavily on assumptions. In 2021, the Dasgupta Review, commissioned by the British Treasury, argued that conventional accounting treats nature as a free resource, and that GDP ignores the depreciation of natural capital: a country can liquidate its forests and fisheries and record the proceeds as income. Efforts to correct this include the UN’s System of Environmental-Economic Accounting, adopted as a statistical standard in 2021, and Bhutan’s attempts at measuring gross national happiness, though their results are hard to compare.

Property rights, as Coase and Ostrom both suggested, can be a route to stewardship, and the clearest example is the use of catch shares in fisheries. Under open access, fishers race to catch fish before others do, overcapitalize, and deplete stocks, the tragedy of the commons in its original form. Systems that assign each fisher a share of the total allowable catch remove the race. Iceland and New Zealand built their fisheries management on tradable quotas in the 1980s, and a large study by Christopher Costello and colleagues in 2008 found that fisheries with catch shares were substantially less likely to collapse than those without. The approach has costs, since quotas tend to concentrate in large operators and can strip small-scale fishing communities of access, which raises the question of whose commons is being enclosed. The point is not that markets are better than rules but that the design of the rules, whether market-based or communal, determines whether people’s incentives align with the resource.

Behind these tools sits the question of growth. The environmental Kuznets curve, an idea from the 1990s, held that pollution first rises and then falls as countries get richer, and it fits some local pollutants: London’s smog, American lead emissions, and sulfur dioxide in wealthy countries all fell with income and regulation. But it fits poorly for carbon dioxide and for resource use, which have continued to rise with income, and the falls in local pollution were partly achieved by exporting dirty production, so that consumption-based accounting tells a less flattering story than production-based. The debate over “decoupling” turns on this: some wealthy countries have reduced territorial carbon emissions while growing, with Britain and Sweden notable, but whether decoupling can be fast enough, and extended to materials as well as carbon, is disputed. Degrowth advocates, such as Jason Hickel, argue that it cannot and that rich economies should deliberately shrink their material throughput, while green growth proponents point to falling costs of renewables and the potential for efficiency and innovation. Both camps rely on projections, and the evidence of the past two decades has been mixed enough to support each. The cost of solar power has fallen about ninety percent in a decade and a half, as I noted, a result few economists predicted, which should make everyone cautious about confident forecasts in either direction.

I find the cleanest lesson in the old observation that prices are signals, and a signal that omits information leads decisions astray. Where markets are wrong, the remedy is often to correct the signal rather than to abandon the market, though the equally old lesson, from Ostrom and Scott, is that many resources are governed well by neither prices nor states but by communities with their own rules. And there is a limit to price-based thinking that economists themselves acknowledge: some things, like the wellbeing of future generations or the existence of a species, may be what Sandel calls corruptible by markets, in that putting a price on them changes what they mean, as when an Israeli day care that fined late-arriving parents found that lateness increased, because the fine converted a moral obligation into a purchasable service. That finding, from Uri Gneezy and Aldo Rustichini, is a neat bridge to a topic in its own right: the way incentives and meanings interact, and the strange psychology of motivation, which is where I’ll go next.

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The day care story has a famous twin in the psychology of motivation, and it begins with a simple experiment. In 1971, Edward Deci brought college students into a laboratory to work on Soma puzzles, three-dimensional block assemblies that people find genuinely engaging. One group was paid a dollar per puzzle solved, the other was not. During a break, when the experimenter left the room and the students were free to do anything, those who had not been paid were more likely to keep playing with the puzzles. Payment had apparently drained the activity of its own appeal. Mark Lepper, David Greene, and Richard Nisbett found the same with children in 1973: preschoolers who liked drawing and were promised a certificate for doing it drew less afterwards, in free play, than those who got no promise or an unexpected reward. They called it the overjustification effect: when an external reason is supplied, people reattribute their behavior to it and discount their own interest.

Deci and Richard Ryan built this into self-determination theory, which proposes three basic psychological needs: autonomy, the sense that one’s actions are one’s own; competence, the sense of effectiveness; and relatedness, connection to others. Intrinsic motivation flourishes when these are satisfied and withers when they are thwarted, and extrinsic motivation exists on a continuum, from rewards that feel imposed to values that a person has fully internalized, so that a doctor who studies late because she cares about patients is extrinsically motivated in a sense but acts with a sense of ownership. A meta-analysis by Deci, Koestner, and Ryan in 1999 concluded that tangible, expected rewards undermine intrinsic motivation for interesting tasks, while verbal praise can enhance it. Critics, notably Judy Cameron and David Pierce, argued that the effects are narrow, appearing mainly with interesting tasks and contingent rewards, and that for dull tasks rewards simply work. That is the sensible reading: incentives are not poison, but they carry a message about what the activity is and who is in control, and the message can overwhelm the money.

The Israeli day care study, by Gneezy and Rustichini in 2000, shows the mechanism at the level of norms. Before the fine, lateness was a social imposition on a teacher who stayed behind, and parents felt guilt. Afterwards it was a priced service, and guilt was replaced by a transaction. Even more telling, when the fine was removed, lateness stayed high: the moral framing, once broken, did not return. Similar results appear elsewhere. When Swiss villages were asked in a 1993 survey by Bruno Frey and Felix Oberholzer-Gee whether they would accept a nuclear waste repository, about half said yes, out of civic duty, but when offered compensation, acceptance fell to about a quarter. Blood donation research dating to Richard Titmuss’s 1970 book The Gift Relationship suggested that paying donors might reduce supply by crowding out altruism, and while later evidence is mixed, with small payments sometimes increasing donations, studies find that the effect depends on whether the payment is read as a thank-you or a price. Economists call this motivation crowding, and it explains why a policy designed on the assumption of self-interested agents can backfire when agents are not only that.

A more everyday reading concerns work. Daniel Pink popularized the research in Drive, arguing that for complex, creative work, autonomy, mastery, and purpose beat carrots and sticks. The evidence for one part of the case is good: in a 2009 study sponsored by the Federal Reserve, Dan Ariely and colleagues offered rural Indian participants rewards of small, medium, or large size for tasks, and found that the highest bonus, equal to several months’ income, produced worse performance on tasks needing cognitive skill, though it helped on purely mechanical ones, perhaps because pressure impairs attention, the choking phenomenon described earlier. Teresa Amabile’s decades of research on creativity found that people are more creative when motivated by interest and enjoyment than by external constraints, and that evaluation and deadlines can dampen it, though not always. Studies of the Whitehall civil servants in Britain led by Michael Marmot found that people with less control at work had higher rates of cardiovascular disease, regardless of salary, which links autonomy to health in a way that goes well beyond job satisfaction.

Goals, too, have a psychology. Edwin Locke and Gary Latham’s goal-setting theory, based on hundreds of studies, finds that specific, difficult goals produce better performance than vague ones like “do your best,” provided people have the ability and accept the goal, and that feedback helps. But goals have side effects. Lisa Ordóñez and colleagues, in a paper titled “Goals Gone Wild,” pointed to Ford’s Pinto, where an aggressive target for weight and cost contributed to a design with a hazardous fuel tank, and to firms that cut corners to meet numerical targets, which returns to Goodhart’s law. Gabriele Oettingen’s research on mental contrasting suggests that positive fantasies about success can reduce effort, since the brain gets some of the satisfaction in advance, whereas imagining the obstacles and planning for them, what Peter Gollwitzer calls implementation intentions, in the form “if situation X arises, I will do Y,” reliably improves follow-through. A meta-analysis of over a hundred studies found a moderate to large effect of if-then plans on goal attainment, a rare case of a simple technique with robust support.

Habits are the other half of the story, because most daily behavior runs on them. Wendy Wood’s diary studies found that about forty percent of what people do each day is repeated in the same context and often without much conscious thought. A habit is a learned association between a cue and a response, laid down by repetition and reward, and housed in the basal ganglia, the brain regions that the earlier discussion of skills pointed to. Phillippa Lally’s study of people forming a new habit, such as eating a piece of fruit with lunch, found that it took anywhere from eighteen to two hundred and fifty-four days to reach automaticity, with a median around sixty-six, which undercuts the popular twenty-one-day claim and shows how variable it is. Missing a single day did not derail the process. The practical implication is that changing behavior is often easier by changing context than by summoning willpower: people who are good at self-control, studies by Brian Galla and Angela Duckworth suggest, rely more on habits and less on resisting temptation in the moment. Hence the efficacy of defaults, a theme in Richard Thaler and Cass Sunstein’s work on nudges. When Eric Johnson and Daniel Goldstein compared organ donation rates across European countries, they found that nations where citizens are donors unless they opt out had rates above ninety percent, while opt-in nations had rates in the teens, a gap that shows how much behavior depends on the path of least resistance. Retirement saving works the same way: automatic enrollment, as studied by Brigitte Madrian and Dennis Shea, raised participation in a company plan from about a third to nearly ninety percent.

The nudge literature has had its own reckoning. A 2022 meta-analysis reported an average effect of nudges on behavior that looked modest, and then critics, including Maximilian Maier and colleagues, argued that after correcting for publication bias, the average effect was close to zero, though interventions such as defaults still appeared to work. Several famous behavioral findings have failed to replicate or have been questioned, among them ego depletion, the notion that willpower is a limited resource, and some priming effects, like the claim that exposure to words about old age makes people walk slowly, which Daniel Kahneman, who had praised priming research in Thinking, Fast and Slow, later acknowledged he had placed too much faith in. In 2021, a high-profile paper on honesty, in which signing an honesty pledge at the top of a form reduced cheating, was found to rest on fabricated data in one of its experiments, an episode that cast a long shadow over a research program of Dan Ariely and Francesca Gino, and Gino was later placed on leave and had her tenure revoked following an investigation at Harvard. Whatever the details of the individual cases, the lesson is the same one I reached with medicine and psychology: effects in behavioral science are real but often smaller and more context-dependent than the first studies suggested, and the structural reforms, preregistration, large samples, replication, are the remedy.

Meaning is the strand that ties much of this together. Viktor Frankl, who survived Auschwitz, wrote in Man’s Search for Meaning that those who had a why to live could bear almost any how, and founded logotherapy on the idea that the will to meaning is a primary human drive. Empirical work by Michael Steger and others distinguishes the sense that one’s life makes sense from the sense that it has purpose and significance, and finds that meaning is correlated with well-being and, in longitudinal data, with lower mortality. Adam Grant’s studies of call center fundraisers found that a few minutes’ contact with a scholarship recipient raised their weekly calls and revenue sharply, suggesting that knowing whom one’s work helps can transform effort. Amy Wrzesniewski’s work on how workers see their jobs, as a job, a career, or a calling, found that hospital cleaners who saw their work as part of patient care reshaped their tasks accordingly, “job crafting,” and found the work more meaningful, though critics note the framing can be exploited to justify low pay, a reminder that meaning is a complement to fair compensation and not a substitute.

Pulling these threads together, the psychology of motivation says that people are neither simple reward-seekers nor pure idealists. They respond to incentives and to the meaning those incentives carry, to goals and to the habits that actually run their days, and they do their best work when they feel some control, see some purpose, and are not being treated as suspects. That has implications for anyone designing a workplace, school, or policy, and it also bears on the oldest question of how to live, which is where the old philosophical tradition of asking what a good life consists of meets the modern research on well-being.

I’ll take that up next: happiness, as a subject of measurement, and the strange findings about what we want, what we get, and what we think we’ll get.

Proceed

Happiness research begins with a measurement problem: how do you study something that exists only inside people’s heads? The field’s first answer was to ask. Surveys pose a question such as “Taking all things together, how happy would you say you are?” or ask people to place themselves on a ladder from the worst possible life to the best, a method developed by Hadley Cantril in the 1960s and now used by the Gallup World Poll. These evaluations of life as a whole differ from momentary experience, and Daniel Kahneman and his colleagues built a second approach to capture it: the day reconstruction method, in which people recall the previous day episode by episode and rate how they felt in each. The two measures answer different questions. One asks how a person judges their life; the other asks how it feels to live it. Much of the field’s most interesting work lives in the gap between them.

The first famous puzzle was posed by the economist Richard Easterlin in 1974. Within a country at a given time, richer people report being happier than poorer ones, yet over decades, as the United States and Japan grew much richer, average reported happiness barely moved. The Easterlin paradox suggested that relative income matters more than absolute income, and there is good evidence for comparison effects: studies find that people care about their rank within a reference group, such as coworkers or neighbors, and that a raise matters less if colleagues also receive one. The paradox itself has been contested. Betsey Stevenson and Justin Wolfers, using larger datasets, found that richer countries are on average happier, that the relationship continues across the income range, and that time trends in many countries do show rising happiness with growth. Easterlin replied that the evidence for long-run trends remains weaker. Where the dispute stands is approximately this: money buys happiness, with diminishing returns that are steeper in life evaluation than some earlier work suggested, and not nearly as much as people expect.

A much-quoted result by Kahneman and Angus Deaton in 2010 found that emotional well-being plateaued at around seventy-five thousand dollars of household income in the United States, while life evaluation kept rising with income. Later work by Matthew Killingsworth, using experience sampling with over a million real-time reports, found that happiness kept rising with income well beyond that point, and the two sides ran an adversarial collaboration, with Barbara Mellers as a mediator, whose 2023 resolution was neat: for most people, happiness does continue to rise with income, but for a minority who are unhappy, the relationship flattens, since money does not fix whatever is causing their unhappiness. It’s a satisfying case of a disagreement settled by someone noticing that both datasets were true of different parts of the distribution.

Next comes what Daniel Gilbert and Timothy Wilson call affective forecasting: our predictions of how future events will make us feel are systematically off. People overestimate both the intensity and the duration of their emotional reactions, a bias they call the impact bias. Assistant professors predicted that being denied tenure would devastate them for years, but those who were denied were happier than they had predicted, within a couple of years. Lottery winners and paraplegic accident victims, in a famous 1978 study by Philip Brickman and colleagues, appeared closer in happiness than anyone would have guessed, though the study was small and its conclusions have been qualified: later work, such as that of Richard Lucas, using large panel data, finds that people adapt to many events, such as marriage and moderate income changes, but adapt incompletely or not at all to others, like long-term unemployment, disability that persists, and the death of a spouse. This is hedonic adaptation, the idea that we return toward a baseline, popularly called the hedonic treadmill. The baseline is not a fixed set point, since individuals’ levels do shift over a lifetime, yet the broad principle holds: we adapt faster to things that are constant and slower to those that are variable, intrusive, or uncertain, like noise or a long commute, which is probably why people choose long commutes for bigger houses and regret it.

The Harvard Study of Adult Development, which I mentioned earlier, supplies the strongest single answer about what matters, which is relationships. Its director Robert Waldinger summarized eight decades of data by saying that the people who were most satisfied in their relationships at fifty were the healthiest at eighty. Cross-sectional studies agree: the strongest correlates of life satisfaction in most datasets are social, followed by health, and then income. The World Happiness Report, which ranks countries by life evaluation, consistently places the Nordic countries near the top, with Finland first for several years running, and the explanations offered include high social trust, generous welfare states, low corruption, freedom to make life choices, and generosity, the same ingredients I traced under social capital. The ranking comes with caveats: the questions translate imperfectly, answers on a ten-point scale are shaped by cultural norms about modesty and enthusiasm, and the whole exercise measures evaluation more than mood.

Age shows a curious shape. Several large studies found a U-curve in life satisfaction across the lifespan in wealthy countries, with a low point around the late forties, and rising satisfaction into old age. Laura Carstensen’s socioemotional selectivity theory offers an account: as people perceive their time horizon shrinking, they shift priorities from acquiring information and new contacts to savoring close relationships and emotionally meaningful goals, and so their daily emotional experience becomes more positive and stable, a pattern seen even in younger people facing a limited horizon, such as those with serious illness. The U-curve itself has been challenged, and recent analyses suggest that in some datasets, especially after 2010, young people’s wellbeing has fallen and the curve has flattened or become a gradual rise. Teenagers’ mental health has been the subject of acute debate, with Jonathan Haidt and Jean Twenge arguing that smartphones and social media drove the deterioration in adolescent mood in the 2010s, and others, such as Candice Odgers and Amy Orben, arguing that the correlational evidence is weak and the effects small, a dispute in which the observed trends are not in question but the causes are.

A reaction against measurement runs through the older ethical tradition. Aristotle’s eudaimonia, as I said, concerns flourishing as an activity over a whole life, not a feeling, and modern psychology has tried to capture that with a distinction between hedonic well-being, which is pleasure and the absence of pain, and eudaimonic well-being, which includes purpose, growth, and autonomy. Carol Ryff’s model identifies six dimensions: self-acceptance, positive relations, autonomy, environmental mastery, purpose in life, and personal growth. Martin Seligman’s positive psychology, launched around 1998, aimed to study what makes life worth living and not only what goes wrong, and its PERMA model lists positive emotion, engagement, relationships, meaning, and accomplishment. Mihaly Csikszentmihalyi’s concept of flow, the absorbed state in which challenge matches skill, time seems to vanish, and self-consciousness drops away, was drawn from interviews with rock climbers, chess players, and surgeons, and it ties neatly to deliberate practice, since the conditions for flow are the same as those for improvement: clear goals, immediate feedback, and difficulty at the edge of ability. Many people report that they are happiest not in leisure but in such challenging activity, and yet they say they would rather be relaxing, a paradox Csikszentmihalyi noted.

Interventions have been tested, with modest and sometimes disappointing results. Gratitude exercises, such as writing down three good things each day or writing a letter of thanks, show small positive effects in meta-analyses, with some inflation from publication bias. Acts of kindness raise the giver’s mood, as Elizabeth Dunn’s work on prosocial spending found: people who spent twenty dollars on others were happier than those who spent it on themselves, even in experiments in Uganda, Canada, and South Africa, though the size of the effect was small. Time affluence matters: Ashley Whillans found that people who spend money to buy time, by outsourcing chores, report greater life satisfaction. Meditation and cognitive behavioral approaches help with depression and anxiety. The most reliable high-impact interventions, though, are often not psychological tricks but changes in circumstances, such as treating depression, reducing poverty, and improving sleep. A striking randomized trial of cash transfers to people in Kenya by GiveDirectly found sustained gains in psychological well-being, and the Dutch and German studies of unemployment show the opposite effect, with a loss of well-being that exceeds what the lost income alone would explain, because work provides structure and status.

The caution I would add concerns the aim itself. A mind that tracks its own happiness constantly tends to lower it, an observation made by John Stuart Mill, who wrote that those only are happy who have their minds fixed on some object other than their own happiness. Iris Mauss and colleagues found that people who valued happiness highly were more likely to feel lonely and disappointed in experiments, perhaps because they set high standards for how they should feel and treated ordinary moods as failures. There is something self-defeating about pursuing a by-product directly, as with sleep, and the more successful strategy seems to be to pursue things worth doing, such as relationships, craft, service, and learning, and let well-being come as a consequence. The tradition of the philosophers who counseled equanimity, the Stoics and the Buddhists, converges with some of the empirical findings: much suffering is the product of the gap between expectations and events, and training attention and expectations can narrow it.

That leaves the matter of suffering itself, and the odd fact that the research on well-being includes a sturdy finding about the value of difficulty. Post-traumatic growth, described by Richard Tedeschi and Lawrence Calhoun, is the report by many people who endure loss or illness that they discovered new strengths, closer relationships, and a changed sense of priorities. It’s not universal and shouldn’t be demanded of sufferers, and some of it may be illusory, but the pattern across studies is that moderate adversity is associated with better resilience than none, a point Mark Seery’s longitudinal work supports, and that for most people, the typical response to loss is resilience, as George Bonanno has shown, rather than lingering collapse. Treating suffering as meaningless pathology and treating it as redemptive are both mistakes. The truth is plainer: people are tougher than they expect, in a way that has costs and benefits, and the best response to another’s pain is presence, not a theory.

From the inner life to the outer world’s oldest source of wonder, I’ll turn next toward the sky, and to the human history of reading the heavens: calendars, astronomy, and the long effort to measure the cosmos with the naked eye and a few simple tools.

Epidemics are the one kind of disaster that history has taught us to describe in the language of arithmetic, because the same few numbers recur: how fast it spreads, how many it kills, and how long before people stop dying. Yet the people who lived through them rarely had those numbers, only a sudden change in the sound of their own streets.

The Black Death is the benchmark. Plague arrived in Sicily in 1347, carried by Genoese ships from the Black Sea, and over the next four years it swept through Europe, North Africa, and the Middle East. The causative organism, Yersinia pestis, was identified in 1894 by Alexandre Yersin during an outbreak in Hong Kong, and its role in the medieval pandemic was confirmed in 2011 and after by recovering its DNA from skeletons in a London burial ground, the East Smithfield cemetery. The mortality was staggering: between a third and a half of Europe’s people died, perhaps fifty million across Eurasia and Africa, with some towns losing most of their inhabitants. The standard story is that fleas on black rats carried the bacterium, but recent work, including a 2018 modeling study by Katharine Dean and colleagues, suggests that human fleas and lice, spreading from person to person, may better explain the speed and the patterns of the outbreak in many European cities, which would also explain why the plague moved faster than rat populations could carry it. Pneumonic plague, which spreads through the air between people, was far deadlier still, with near-certain fatality untreated.

The consequences reshaped society. Labor became scarce, so wages rose and serfdom weakened in western Europe, where peasants could now bargain, while in eastern Europe landlords responded by tightening control, a divergence some historians connect to the later economic split between the two halves of the continent. The English Statute of Labourers in 1351 tried to freeze wages at pre-plague levels and failed, and the Peasants’ Revolt of 1381 followed. Pogroms against Jewish communities, falsely accused of poisoning wells, broke out across the Rhineland and beyond, a grim recurring pattern in which fear of an invisible enemy seeks a visible one. Out of the same fear came institutions that proved lasting. Venice, and its colony of Ragusa, which is now Dubrovnik, imposed a thirty-day isolation on arriving ships, the trentino, later extended to forty days, quaranta giorni, which gave us the word quarantine. Health boards, plague hospitals called lazarettos, and the keeping of mortality records grew out of Italian city-states’ attempts to manage the disease, so public health as an administrative function was effectively born from the plague.

The most devastating epidemics in human history, though, were not plagues but the diseases carried across the Atlantic after 1492. Smallpox, measles, influenza, and typhus struck populations with no prior exposure, and mortality ran as high as ninety percent in some communities. When Hernán Cortés besieged Tenochtitlan in 1521, smallpox had preceded or accompanied his forces and killed a large fraction of the city’s defenders, including the emperor Cuitláhuac, a factor in the Spanish victory at least as important as their weapons and native allies. The pattern, which Alfred Crosby called virgin soil epidemics, recurred in Hawaii, where the population fell from several hundred thousand to under fifty thousand in the century after Cook’s arrival in 1778, in Fiji after a measles outbreak in 1875, and among Arctic peoples. Jared Diamond’s Guns, Germs, and Steel argued that Eurasians had acquired their immunities through millennia of living with domesticated animals, the source of many human diseases: measles from cattle’s rinderpest, smallpox perhaps from a relative in camels or rodents, influenza from birds and pigs. The thesis about the origins of those diseases is broadly accepted, though historians, like Paul Kelton, have emphasized that the demographic collapse also depended on social disruption, warfare, enslavement, and famine that left populations unable to nurse the sick, so that the deaths were not purely a matter of biology.

The path toward understanding disease is the story I began earlier with John Snow and the Broad Street pump. Cholera arrived in Europe from India in 1817 and spread in successive pandemics, killing many thousands in the great cities, and its transmission through water was established by Snow’s studies in 1854, though the miasma theory was slow to die. Robert Koch isolated the cholera vibrio in 1883, after having identified the tuberculosis bacillus in 1882, and laid down postulates for proving that a microbe causes a disease, which are still taught, though they don’t apply to every case, as in viruses that cannot be grown in pure culture or to infections that can be carried without symptoms. Typhoid Mary, Mary Mallon, a cook in New York in the early 1900s who carried Salmonella typhi without symptoms and infected dozens of people, some of whom died, was isolated for decades in total, a case that still frames the tension between public safety and individual liberty, and reveals how asymptomatic carriers complicate control, a lesson recurring with every pandemic.

The deadliest infectious event of the modern era came in 1918. The influenza pandemic that began in the spring of that year and returned with a lethal second wave in the autumn killed an estimated fifty million people worldwide, perhaps more, more than the First World War that coincided with it, and it killed in an unusual pattern, with a peak in young adults aged twenty to forty, perhaps because of a violent immune response, a cytokine storm, in people with the strongest immune systems, or because older people had some immunity from an earlier related strain. It was misnamed the Spanish flu because neutral Spain, without wartime censorship, reported on it openly. The virus was resurrected from preserved tissue and from a victim buried in permafrost in Alaska, and its genome was sequenced by Jeffery Taubenberger’s team in 2005, showing that it was of avian origin and had adapted to humans. The pandemic had a notable effect on later societies: studies of cohorts born during it find lower educational attainment and income, and the experience seems to have faded oddly from public memory, with the novelist Katherine Anne Porter’s Pale Horse, Pale Rider one of the few prominent literary treatments. Subsequent flu pandemics, in 1957, 1968, and 2009, were much milder, though each killed hundreds of thousands to over a million.

The triumphs came with vaccines and antibiotics. Edward Jenner showed in 1796 that inoculating people with cowpox protected against smallpox, and although variolation, deliberate infection with a mild dose of smallpox, had been practiced for centuries in China, India, and the Ottoman Empire and was introduced into Britain by Lady Mary Wortley Montagu after she observed it in Constantinople, Jenner’s method was safer and was soon spreading worldwide. The global eradication of smallpox, completed in 1980, remains the great landmark, a disease that killed perhaps three hundred million people in the twentieth century alone. Polio, which paralyzed hundreds of thousands of children annually in the early 1950s, was beaten back by the vaccines of Jonas Salk, introduced in 1955, and Albert Sabin, and wild poliovirus now persists in only two countries, Afghanistan and Pakistan. Alexander Fleming noticed in 1928 that a mold had killed bacteria on a culture plate, and Howard Florey and Ernst Chain turned penicillin into a drug during the Second World War, ushering in the antibiotic age and the ability to cure infections that had been a leading cause of death. By the 1960s, some officials proclaimed that the age of infectious disease was nearly over. They were mistaken, in part because of the evolutionary dynamics I described with the Red Queen.

Antibiotic resistance is the most concrete failure of that optimism. Bacteria exposed to antibiotics evolve resistance, and the overuse of drugs in medicine and in livestock, which consumes a large share of the world’s antibiotics, accelerates the process. A 2022 study published in the Lancet estimated that bacterial antimicrobial resistance was directly responsible for about 1.3 million deaths in 2019 and associated with nearly five million. Few new classes of antibiotics have been introduced in decades, because the economics are unattractive: a drug taken for a week and held in reserve earns far less than one for chronic disease, which has led to proposals for “subscription” payment models that pay for access rather than volume, as the United Kingdom has begun to try. In 2023, researchers used machine learning to identify a new antibiotic, abaucin, active against a hard-to-treat hospital bacterium, which shows one possible route, and bacteriophage therapy, using viruses that prey on bacteria, is being revived after a century in the shadow of antibiotics, notably in the former Soviet Union.

HIV, recognized in 1981, showed the modern pattern in compressed form. The virus jumped from chimpanzees to humans in Central Africa around the early twentieth century, spread quietly for decades, and burst into view in an era when no one expected a new epidemic. It has killed over forty million people, and for the first fifteen years of the epidemic, an infection was a death sentence. Combination antiretroviral therapy, introduced in 1996, transformed it into a manageable chronic condition for those who could obtain the drugs, which for years were priced beyond the reach of Africa, until activists, led by groups such as the Treatment Action Campaign in South Africa, and generic manufacturers like the Indian company Cipla brought the cost from over ten thousand dollars a year to under a hundred. The President’s Emergency Plan for AIDS Relief, launched in 2003, is credited with saving twenty million lives or more, one of the most effective health programs ever, and I note that the funding landscape for such work shifted sharply in 2025 with US aid cuts, the consequences of which are something to check against current reports. Long-acting injectable prevention, such as lenacapavir given twice a year, which showed near-complete protection in trials in 2024, is being rolled out, offering a prospect that was unimaginable a generation ago.

The COVID-19 pandemic, from 2020, was the first global respiratory pandemic in a century to arrive in an age of genomic sequencing and mass communication, and it revealed both what the world had learned and what it had not. The viral genome was published within weeks, vaccines were designed within days of that and authorized in under a year, which compressed a process that usually takes a decade, thanks to mRNA platforms and to large investments. Official counts of deaths reached about seven million, though excess mortality estimates, by the World Health Organization and by The Economist and others, suggest the true figure was something like fifteen to twenty million. Yet the response was uneven: early testing failed in some countries, mixed public messaging eroded trust, vaccine distribution was grossly unequal, with wealthy countries getting booster doses while health workers in poorer ones waited, and polarization turned masks and vaccines into identity markers in parts of the United States. Some of the best-performing places, such as South Korea and Taiwan, had learned from SARS and MERS and had testing and tracing capacity ready. Research on the pandemic’s lessons includes the importance of early, transparent reporting, the role of superspreading and indoor air, which was long underappreciated because medical tradition favored droplet over airborne transmission, and the sheer value of surveillance, including wastewater monitoring, which can detect the circulation of a virus in a community before cases show up in clinics.

The pattern behind many of the new diseases is zoonotic spillover, in which pathogens from animals cross into humans, and it is influenced by how people use land and interact with wildlife. Ebola, Nipah, SARS, MERS, and avian influenza all fit, and the expansion of farms and settlements into forests, the wildlife trade, and intensive livestock production raise the odds of contact. Bats, which host a great many viruses without becoming sick, are a recurring reservoir, and their immune systems, which tolerate viral infection with dampened inflammation, are under study. The ongoing spread of H5N1 avian influenza among wild birds and its jump into dairy cattle in the United States in 2024, with scattered human cases, is the kind of development that surveillance exists to catch early, though I’m unable to say how it has evolved since. Preventing the next pandemic is cheaper, by most estimates, than responding to one, since the cost of COVID-19 ran into trillions of dollars, but prevention is the thankless invisible success I described earlier, and the funding for it is perpetually at risk once the crisis fades from memory.

I keep arriving at the same conclusion, which has the character of a theme in this whole sequence: the defense against epidemics is largely a system of institutions, clean water, sewers, surveillance, trusted communication, vaccines, and international cooperation, and it works best when invisible. The next chapter belongs to the sciences of the immune system itself, the extraordinary machinery that does that defending inside each of us, a subject that connects the pathogens to the cells.

Proceed

The immune system is the body’s second great information-processing organ, and in some ways the stranger one. The brain recognizes patterns in the outside world; the immune system recognizes patterns in the molecular world, distinguishing self from non-self, harmless from dangerous, and it must do so without knowing in advance what threats will appear. Pathogens evolve faster than any host, so a fixed list of enemies would be useless. The solution that evolution found is a two-layer design: a fast, generic first response, and a slow, learning second response that adapts to each invader.

The first layer is innate immunity, and it is ancient, shared with insects, plants, and even bacteria in some form. Cells like macrophages and neutrophils carry receptors that recognize broad molecular signatures common to microbes, such as bacterial cell wall components or double-stranded RNA from viruses. Jules Hoffmann found in 1996 that fruit flies lacking a receptor called Toll died of fungal infections, and Bruce Beutler found that mice lacking the mammalian equivalent could not respond to bacterial endotoxin; they shared the 2011 Nobel Prize with Ralph Steinman, who discovered dendritic cells, the sentinels that carry fragments of invaders to the rest of the system. Innate responses unfold in minutes to hours. They produce inflammation, the redness, heat, swelling, and pain described by Celsus two thousand years ago, as blood vessels widen and leak to let immune cells reach the tissue. Complement proteins punch holes in bacteria and tag them for engulfment, and interferons, signaling molecules released by infected cells, warn neighbors to prepare antiviral defenses. Fever is not a malfunction but a coordinated response: many pathogens replicate poorly at higher temperatures, and immune cells work better, though the evidence on whether suppressing mild fevers harms recovery in humans is mixed.

The second layer, adaptive immunity, is where the real surprise lies. Lymphocytes, the B cells and T cells, each carry a receptor of a single specificity, and the body must be ready for a universe of possible antigens, perhaps more than a hundred million distinct shapes. There aren’t enough genes in the genome to encode that many receptors directly. Susumu Tonegawa showed in 1976 how the system gets around this: the receptor genes are assembled in each developing lymphocyte by cutting and pasting segments from libraries of variable pieces, V, D, and J segments, in a process called V(D)J recombination, with extra random nucleotides added at the junctions. A modest set of segments yields billions of combinations, and each cell ends up with its own unique receptor. It is a deliberate, controlled mutation of the genome in somatic cells, and it earned Tonegawa the 1987 Nobel Prize. The immune system is thus a population of random guesses, a library of molecular keys made without knowledge of the locks.

Macfarlane Burnet’s clonal selection theory, in the 1950s, explained what happens next. When an invader enters, the few lymphocytes whose receptors happen to fit it are activated and multiply, producing an army of identical clones, while the rest stay quiet. It is Darwinian selection running inside the body over days: the fit are amplified. B cells go further, with a process called somatic hypermutation in structures called germinal centers, where they mutate their antibody genes at about a million times the normal rate and are then selected for tighter binding, so antibodies improve in affinity over the course of an infection. Some cells become plasma cells, factories that secrete thousands of antibodies per second, and others become memory cells, long-lived and primed, which is why a second encounter produces a faster, stronger response. This memory is the principle behind every vaccine, and its durability varies widely: immunity to smallpox vaccination can last decades, while the protection against some coronaviruses and RSV wanes within months or years, partly because the virus changes and partly because the memory itself fades.

Immune cells must be taught not to attack the body, and the teaching is brutal. In the thymus, developing T cells are tested against the body’s own proteins, including ones normally found only in distant organs, thanks to a gene called AIRE that makes thymic cells display them. Those that react strongly to self are ordered to die, in what is called negative selection, so that the vast majority of T cells, perhaps ninety-five percent or more, are eliminated before they ever leave. Regulatory T cells, identified by Shimon Sakaguchi, Mary Brunkow, and Fred Ramsdell, a discovery honored with the 2025 Nobel Prize in Physiology or Medicine, restrain the ones that escape. When these safeguards fail, the result is autoimmune disease: type 1 diabetes, rheumatoid arthritis, multiple sclerosis, lupus, affecting roughly five to ten percent of people, with women disproportionately affected, for reasons still under investigation. Autoimmunity is the price of a system powerful enough to defeat unpredictable enemies.

T cells come in kinds with different jobs. Killer T cells inspect other cells for signs of infection, displayed by molecules called MHC, and destroy any that show viral fragments. Helper T cells orchestrate the response, releasing signals that direct B cells and macrophages. The MHC molecules are the most variable genes in the human genome, and they explain why organ transplants are rejected: the recipient’s T cells recognize the donor’s MHC as foreign. Their diversity across a population is thought to be maintained by pathogen pressure, since a population with varied MHC types is harder for any one disease to wipe out, and some studies suggest animals, including humans in one controversial experiment, prefer mates with dissimilar MHC by smell, though the human evidence is weak.

The most significant therapeutic idea to emerge from this knowledge is cancer immunotherapy. Tumors arise from the body’s own cells and often escape notice, and they exploit brakes the immune system uses to avoid overreaction. James Allison discovered that a protein called CTLA-4 acts as a brake on T cells, and showed in mice that blocking it let the immune system reject tumors; Tasuku Honjo identified another brake, PD-1. Drugs that release these checkpoints, such as ipilimumab, approved in 2011, and pembrolizumab, have produced durable remissions in some patients with melanoma, lung cancer, and others, cases that once had survival measured in months, and Allison and Honjo shared the 2018 Nobel Prize. The treatments help only a fraction of patients and can cause autoimmune side effects, so finding who will respond is an active research area. CAR-T therapy goes further: a patient’s T cells are removed, engineered to carry a synthetic receptor targeting a protein on cancer cells, and returned, a “living drug.” It has achieved remissions in some leukemias and lymphomas, pioneered by Carl June’s group, including a child, Emily Whitehead, treated in 2012. The challenge is extending it to solid tumors, which are harder to penetrate and more heterogeneous, and bringing down the cost.

The immune system also keeps a long relationship with the microbes that live on and in us, the microbiome, whose cells roughly equal our own in number, contrary to the old tenfold estimate. Gut bacteria train the immune system early in life, produce vitamins and short-chain fatty acids, and compete with pathogens. Disruption by antibiotics can open the way for infection by Clostridioides difficile, which fecal microbiota transplantation cures in over eighty percent of recurrent cases, an unglamorous but effective therapy. Claims that the microbiome explains obesity, depression, and autism have run ahead of the evidence, with many findings correlational and hard to replicate, though the field is real. The hygiene hypothesis, proposed by David Strachan in 1989 after noticing that children with more siblings had less hay fever, suggests that early exposure to microbes helps calibrate the immune system, and was later refined into the “old friends” idea that what matters is contact with the microorganisms humans coevolved with. The rise of allergies and asthma in wealthy countries, and studies showing that children raised on traditional Amish farms have far less asthma than Hutterite children in more industrialized settings, support it. The details are still being worked out, and it should not be read as a reason to avoid hygiene against dangerous pathogens.

Allergy shows the system in error: harmless substances, like peanut protein or pollen, are treated as threats, with IgE antibodies triggering mast cells to release histamine. A major trial, the LEAP study in 2015, overturned older advice by showing that early introduction of peanut to high-risk infants cut peanut allergy rates by around eighty percent, and guidelines changed accordingly, an example of how a plausible recommendation, delay allergens, was wrong, and testing corrected it. Immune aging is another frontier: with age, the thymus shrinks, naive T cells decline, and low-grade chronic inflammation, called inflammaging, rises, which may contribute to frailty and to the severity of infections in the elderly, and to why vaccines work less well in older people.

The more I look at it, the more the immune system seems a recapitulation of themes in this entire sequence: a system that learns by variation and selection, balances sensitivity against false alarms, polices itself against internal error, and depends on communication networks, the signaling molecules called cytokines, to coordinate millions of cells without any central controller. It is evolution at the timescale of days, inside a single body, which is probably why immunologists, like students of markets and ecosystems, speak so readily of populations, selection, and signaling.

And that is a good bridge to the oldest mechanism of variation and selection, evolution itself, and the last century’s remarkable rewriting of how we understand it, from the modern synthesis to the molecular clock and the genomes of extinct humans.

Proceed

volution by natural selection is a simple enough idea to state in a sentence, and the history of its development is a story of finding out how much machinery that sentence conceals. Darwin’s argument in 1859 needed three ingredients: variation among individuals, heritability of that variation, and differential survival and reproduction. He had the first and the third in abundance from his observations of pigeons, barnacles, and finches. The second was his weak point. He had no mechanism of inheritance, and the prevailing assumption that parental traits blend, like paints, posed a serious problem: Fleeming Jenkin pointed out in 1867 that blending would dilute any favorable variation out of existence within a few generations. Darwin never had the answer, though it was sitting in a monastery garden in Brno, where Gregor Mendel had spent the 1850s and 1860s crossing pea plants and counting offspring. His results, published in 1866 in a local journal, showed that traits are inherited as discrete units that don’t blend, that reappear unchanged in later generations, and that follow ratios, a three-to-one split of dominant to recessive traits in the second generation. They were ignored until 1900, when three botanists independently rediscovered them.

The reconciliation took another three decades and is called the modern synthesis. At first Mendelians and biometricians, who studied continuous traits like height, were bitter opponents, the former holding that evolution proceeds through sudden large mutations, the latter that it works through the gradual accumulation of small variations. Ronald Fisher showed in 1918 that many Mendelian genes, each contributing a little, could produce the continuous variation the biometricians observed, and, together with J. B. S. Haldane and Sewall Wright, founded population genetics, the mathematics of how gene frequencies change under selection, mutation, drift, and migration. Haldane calculated how quickly a favorable mutation could spread; Wright introduced the idea of an adaptive landscape, in which populations climb peaks of fitness, and stressed drift, the random fluctuation that dominates in small populations. Theodosius Dobzhansky’s Genetics and the Origin of Species in 1937 brought the theory to field biology, and his line that nothing in biology makes sense except in the light of evolution became a slogan. Ernst Mayr contributed the biological species concept and the idea that speciation typically begins with geographic isolation, which the island examples I gave earlier illustrate. By about 1950, the synthesis held that evolution is changes in gene frequencies in populations, driven mainly by natural selection acting on small variations.

Then the molecular era complicated and enriched the picture. The discovery of the structure of DNA in 1953 by Watson and Crick, building on X-ray diffraction images by Rosalind Franklin and Maurice Wilkins’s group, provided the physical basis of heredity, and within a decade the genetic code had been cracked. Emile Zuckerkandl and Linus Pauling noticed in 1962 that the differences between hemoglobin proteins of different species grew roughly in proportion to the time since their lineages split, suggesting a molecular clock. Motoo Kimura then proposed in 1968 the neutral theory of molecular evolution: most mutations that become fixed in populations are neither helpful nor harmful but neutral, spreadin

Réception des stries sonores de milliers d’orthoptères (sauterelles et cri-cris), hachurant le premier plan de l’image, des nappes coassantes de crapauds-buffles et grenouilles, qui en recouvrent le fond. L’ensemble se produit en cadences variables, qui brusquement se silencient. On ne sait pourquoi le raffut s’arrête. L’image nocturne de la ville s’engouffre entière dans le couloir.

Une voix (après que les chiens se sont tus) : Dans le silence perçu [comme l’œil de soi perçoit l’image] réside le non-entendu de la ville, son véritable dire, de même que derrière le vu le non-vu, sa parole muette. On voit ce que l’on ne voit pas, tous sens confondus ; le masque de la matière porte la voix de l’évidence. On entend la ville parler, dans le silence de sa matière. L’éruption des abois, ainsi que des rauques cris de coqs ‒ le jour, celle du croassement des corbeaux-pies ‒ en signale par contraste toute l’étendue.

#Fenêtresurville #Didascalies

#Irish Haiku

Hurling out at school Blisters, bruises, cold wet grass Running noses, racing

I recently suggested my book to my local library since it has a New Releases and a Local Authors section. Not approved. So I will inquire more at my library’s information desk on how I can promote The Package Trilogy. Stay tuned on that front.

If you have experience promoting your books to the library please feel free to provide specifics. I would appreciate it.

#book #library #rejection

I spent a decade in banking

I was born on the Glorious Twelfth. This is the opening of the hunting season in Scotland where grouse is a common bird that is hunted. The yearly event is immortalized by the Scotch Whisky, The Famous Grouse! Just read the label.

While growing up my dad would have his own version of the Glorious Twelfth on his farm Oakvilla. All his mates and family would converge on a yearly basis. There would be beers, food and hunting. The magistrate would provide a permit to shoot Reedbuck & Bushbuck in the morning and in the afternoon it would be Guinea fowl. Everyone would have great big grins on their faces. The story of one old gent who had to much beer at lunch and mistook a Hamerkop for a duck would be retold endlessly to great amusement.

Shortly after the start of the new millennium, while I was working at Investec, the dealing room in London was experiencing slow performance problems. Hours of troubleshooting remotely could not identify the problem. Eventually, I climbed on a jet plane to London to try and troubleshoot on site.

The whole day was spent troubleshooting scenario after scenario. Eventually, I discovered a very slow and small error rate on one of the fibre ports. It was the active bridge port from the dealing room vlans. Now enterprise LANs use a simple but frustrating protocol called spanning tree for path protection. I decided to diagnose the port so told the head of the dealing room that I was going to change the path topology. Immediately when I did that he ran in and said the network was back to normal performance.

We went in search of the switch port, which took a while as we had to walk around a bit to find the door. It seems the cabling guy, who was a contractor, was the only guy who ever been into that room, and the sign on the door designated it incorrectly as a broom closet.

After eventually finding the room and consequently the switch and port, I removed the fibre and ejected the GBIC. The problem was obvious, as my hand was covered in dust from a dirty GBIC. We spend a few minutes cleaning all the fibre connectors and then reinserted the GBIC and fibre cable.

The head of the dealing room said he had tickets that evening to a whisky tasting with the master distiller of Chivas Brothers at the Reuters building. As a reward he provided David Cripps, a colleague, and me with the tickets for resolving the network problem.

We went off to the Reuters building. It is a beautiful old sandstone building opposite the iconic The Daily Telegraph. The whisky tasting was in the main boardroom. It was a fancy and posh place with several crusty old farts of the financial and trading community present. We were each presented with six single malts and assigned to categorize them into the whisky regions of Scotland, i.e. Lowlands, Highlands, Speyside, and Islay/Skye. Sometimes a another region is added of Campbelltown.

I polished the tots and placed the glasses on a map of Scotland. I scored four out of six.

The master distiller then said he was going to do a sample blend with us using the six tots in front of us. Wait a moment, we said, they are empty. The master distiller smiled and said we were meant to taste not drink but he then proceeded to replenish our six single malts.

We then proceeded with making a blend, which was creating a unique taste from the different single malts from the different regions. Each single malt in every region has a flavour associated with the region.

When we were done, we had created a small sample blend to take home. However, we now all had twelve tots of single malt behind us!

The master distiller then invited us over to the Reuters private bar. He then had the barman create whisky cocktails for us. He wanted us to realize that whisky was a drink which you could enjoy in whichever way you desired. It was just as good in a cocktail, as drunk neat or with water.

The night turned into a blur. I recall we went to party in Soho. I vaguely recall being stopped in a police roadblock in the early hours of the morning outside St Paul's. The policeman was admonishing us for getting into a taxi that did not have one of its headlights working. That was strange conversation to have with two people who had drunk enough whisky in one night to dent Chivas Regal's yearly marketing budget and at the moment in time couldn't tell the difference between a headlight or a taillight.

The next morning, I woke up with a pounding head. It sounded like a jack hammer was going off inside my head. It took me at least ten minutes to determine that my window was wide open and that at the construction site next to our rented flat, an actual jack hammer was being used.

I worked at Investec for just under a decade. While I was working there another old Grey known as Eertjies came up with the Zebra as a marketing ploy to promote the bank. That idea created and made the brand. It was a great place to work but heck, the IT department had its toxic moments especially when irrational input was given into a technical process that was not feasible. Then it would be a question of who shouted the loudest. Besides that, often there were meetings that were meaningless.

The regularly scheduled weekly meetings were the ones that are forgettable. Somehow every company insists on having them and for the life of me I do not remember any of them or the actions required. The meetings could have had the same outcomes using different types of interactions. However, there is one meeting I remember...

At Investec often the large meetings had insufficient chairs. The people who arrive late must stand. The Information Technology change request meetings at Investec were like that.

One day I was slightly late and took to standing. After about twenty minutes leaning against the wall, I decided to stretch my legs and change position. Unluckily, I plonked my foot in the small dustbin one finds in those meeting rooms.

I tried unsuccessfully to extract my foot by shaking it. I also tried to reach down & dislodge it with my hand, which also did not work.

I thought I would just attempt to stand there with my leg in a dustbin for the rest of the meeting. Unfortunately, my neighbour started sniggering uncontrollably. Finally, the chairman stopped the meeting to reprimand him, which made him break into uncontrollable laughter.

I thought I would try & avoid the embarrassment by trying a maneuver to unseat the dustbin by grabbing it with both my hands to pull it off. All I succeeded in doing was ending up on my ass.

The meeting broke up with everyone ROFL.

Meetings should not be scheduled and used as a boring justification of worth. These types of weekly meetings serve no purpose. Meetings should be ad-hoc and only be there to address a specific topic, problem, or issue. Most meetings are staid and slow, people arrive, the conversation is slow, there are biscuits and coffee. It eventually is no more productive than afternoon tea at the old age home.

I probably was never an Investec person. I was more the ROFL type.

The Investec building in Fox Street is clad in marble. It also used STP token-ring cabling with universal connectors which we called moffie plugs. Investec moved out of Fox Street to Grayston Drive and moth balled the building. About seven years later the building was reopened to house the CIDA City Campus. I was with Taddy Blecher when we first went into the building again and surprisingly enough there was still one plant alive in the atrium of the building! I should have taken that plant home. It was a keeper, surviving seven years.

The building in Grayston had also been renovated and clad in marble. While the builders were cladding the building Wimpie Jansen van Rensburg and I installed network switches and RAMs (Token-ring hubs). While on one of the floors mounting a switch, I heard what I thought was the sound of water. I looked over to the source and saw a mini tsunami of dust moving towards me. It took a few moments for the dust to settle and then I heard shouting. I walked over to the edge of the building and looked down. The sight is etched in my memory. The scaffolding had peeled off the building and there were slabs of marble scattered everywhere. Right beneath me was lying a broken body with the blood staining the white marble a crimson red.

Nowadays when I see scaffolding, I give it a wide berth. What had transpired is that the scaffolding had been overloaded with the marble slabs.

That was not the only time I witnessed a traumatic event at 100 Grayston. One day after work I was at the traffic light exiting the building waiting for it to change. I noticed a taxi approaching with sparks flying from underneath the chassis. I would later learn that the drive shaft had failed and dropped out. In front of me drive shaft dug into the road and catapulted to the side of the road. I look in the direction of where it was heading. Two people scrambled out of the way. The third was reading a paper and the taxi hit him while still traveling at speed. He was thrown into the air and into a tree behind him. I climbed out of my car and walked over to where he was lying. I was the first one there and it looked bad. I thought he was an Indian but was told afterwards that he was white. The impact was so significant that it bruised his skin and darkened it. His life was saved by a doctor who was in the traffic and rushed to his aid. I had walked over to the security guards and told them the guy was dead. He lived but lost his spleen.

I went to visit him in hospital and did not recognize him because of the change in skin colour. It is was confusing as heck.

The branches at Investec had the IT cowboys. Strange & Frankenstein IT systems would make their appearance there. The one day I reached into a cabinet to change a cable and received a shock. One of the local cowboys had installed equipment in a cabinet & did not put the tops on the electrical power plugs. It was a shocking experience.

In the main head office data centre an HVAC iced up over time, unnoticed it created a small pond underneath the floor. Finding nothing else to use to pump out the water we appropriated the industrial wet and dry vacuum cleaner from the facilities department and sucked it out. We broke the vacuum cleaner but solved the problem.

That data centre had a number of incidents. The big one being when there was an Eskom power failure & new HVACs were installed. They were not programmed to have delayed starts between themselves so when they attempted to switch on after the generator came online, they caused a trip. It overloaded the circuits. The generator would then cycle and attempt a restart, which again caused a trip. This cycle continued for a few minutes by which time the started battery on the generator decided to give up the ghost in an almighty large bang. I dived to the floor as my Navy training kicked in and I expected incoming munitions. A new battery was borrowed from a building site next door and the HVACs started manually one by one and the data centre and all the servers in it powered back on.

The new HVACs were installed as there was a failure of the previous ones that were of a dubious vintage. There was a major incident when a few failed and the rest failed to cope with the required cooling load. I declared a disaster and kicked off the corporate business continuity plan. The bank had a designated disaster recovery site and in theory all systems could operate from that site. However, the dealing room obviously had different ideas and decided to remove their servers and run them on their desks in the normal office space. I had to have security stop them from removing equipment. In the mean time the facilities guys had called in an HVAC expert who was immediately available. He found and corrected the problems with the failed units and we were up and running again. We stood down from the declared disaster. Luckily, as I suspect many departments would have taken days to recover instead of hours. The installed HVACs had to be upgraded as if more than one failed at anytime we would have a cascading cooling failure as the ones left had insufficient cooling capacity to handle the whole data centre.

One morning before I arrived at Investec I had a phone call on my mobile that the dealing room wasn't able to operate. Connectivity was lost to the main server and the network was being blamed. I arrived at the dealing room and was accused of network changes the night before. Highly unlikely, I quipped, I was at a company event were buckets of beer were on offer and I was not capable of changing anything. So, I asked them to show me the problem. The application wasn't able to connect to the server but from a command line we could ping the server. The server IP address was 10.0.32.99

After a few minutes of head scratching I loaded Ethereal, now known as Wireshark, a packet capturing tool. One packet capture later I worked out that:

• the app did a DNS lookup first before using the server name field as an IP lookup
• the app was configured with 10.0.32.99 The IP was not assigned an A record in DNS against a DNS name.
• the app did a DNS lookup on 10.0.32.99.investec.co.za (this failed), then it tried 10.0.32.99.co.za, and a result was returned for an A record from on the Internet,

It seems that overnight someone in South Africa registered the domain 99.co.za and as soon as that happened things went pear shaped. I resolved the problem by creating a DNS entry in the local investec.co.za zone with a server name for 10.0.32.99 and configured the app to use that server name.

After all this I expected the IT manager of the dealing room to appreciate the effort like the dealing room manager in London as written about in the Herd of Zebra article. How wrong could I have been! She said how dare someone register 99.co.za without asking her permission and why did I allow it. I was gob smacked for a while and then I let rip. It boggles the mind that people are appointed to manage Information Technology but do not understand the basics about DNS! My boss at the time called me in and said she did not like the way I talked to people. I was young and naive. In the world more and more people operate outside the sphere of their abilities. Even a real estate developer can become leader of the free world. In South Africa it is common for cadres of the ruling party to be deployed as Ministers in fields to which they have no clue about the fundamentals in the area in which they are the leaders. This has been the case in telecommunications which is to put it mildly, a rotting roadkill golden wildebeest carcass.

Investec was an excellent place to work at although they had this saying of the right seats on the bus, it wasn’t always that way in practice.

Hypocrisy

I found myself entangled in a corporate farce that would ultimately lead to my forced resignation due to the above DNS incident. The irony of it all was that my boss who orchestrated my exit, was complicit in a workplace culture that rewarded hypocrisy over merit.

She had a reputation that preceded her, one that was whispered about in hushed tones over coffee. Years before she became my nemesis, in a moment of audacious rebellion, defecated on her own boss’s desk. Yes, you read that correctly. This wasn’t a rumor born of idle gossip; it was a story corroborated by enough colleagues to cement its place in company folklore. Apparently, after a heated disagreement, she decided to leave a “statement”. Yet, somehow, this act didn’t end her career. She was shielded, perhaps by connections or sheer audacity, and continued her ascent up the corporate ladder, unscathed by her own scandal.

Fast forward to my tenure, and here I was, diligently working in the dealing room, navigating the high-stakes world of finance with dealers that relied on my technical expertise. My role often required me to troubleshoot complex systems, including the Domain Name System (DNS), as detailed above, which was critical to operations. The dealing room manager, however, was utterly clueless about DNS. To her, it might as well have been arcane magic.

Despite my successful efforts to keep the systems running smoothly, the dealing room manager’s incompetence became my undoing. She began a political back channel campaign, as attack is the best form of defense, & my boss bought into it.

I left Investec with a bitter taste, not just for the injustice but for the stark realization that corporate environments often reward the loudest egos & the most brazen acts while punishing those who quietly keep the wheels turning. The desk-defiling escapade remained a perverse badge of honor in her story, while my expertise was discarded like yesterday’s news. Yet, as I reflect, I carry no regrets—only the clarity that some battles are better left behind, and some stories, like this one, are worth telling.

Networks, Cocktails, & Corporate Chaos

When I landed my first gig at Investec, I was thrown into the deep end: setting up the network for their shiny new Cape Town office. It was a big deal, a chance to prove myself, and I wasn’t alone—Wimpie and Russell, two techs with more experience and even more personality, were along for the ride. The mission? Install Cisco Catalyst switches and routers, hook them up to WAN links stretching all the way to Sandton, and migrate users from the old office to the new one. All in one weekend.

We worked like dogs that Saturday, pulling cables, configuring switches, and triple-checking connections. By Sunday, we were ready to test. The private banking crew showed up, gave the network a spin, and confirmed everything was golden. Asset Management, though? They couldn’t be bothered to come in. That little detail would bite us later.

With the job done, Russell & Wimpie decided we deserved a proper celebration. Off we went to a restaurant in Newlands, where we made the bold (and slightly unhinged) decision to work our way through the cocktail menu. One by one, we knocked them back—mojitos, martinis, something with too much gin. Things got fuzzy fast. My next clear memory is waking up in a car parked at the waterfront outside Mitchell’s, the morning light stabbing my eyes. I stumbled out, promptly setting off the car alarm, which sent Wimpie and Russell into hysterics. They were still buzzing, plotting to hit a beach for a swim and catch the sunrise. Me? I just wanted a bed. They dropped me at the hotel, and I crashed hard.

Come Monday morning, around 10, I dragged my hungover self to the office. The private banking side was humming along, their fancy suits clicking away on keyboards. But then came the news: Asset Management couldn’t log into Active Directory (AD). Cue the pretentious IT guy from their team, strutting over with a smirk, asking why we’d botched the network install. I bit my tongue, barely. We’d tested network continuity—pings were flying, packets were flowing. The network was solid. So I asked him, “What’s not working? Be specific.” He huffed, saying they couldn’t log into AD, but it had worked fine at the old office.

“Did you change the subnets?” I asked, already suspecting the answer.

“Why would we?” he shot back, like I’d asked him to rewrite the Bible.

I may have let a choice word or two slip, something about his competence, or lack thereof, but I still walked him through fixing the AD configuration. Turns out, their team hadn’t updated the subnet settings to match the new office’s network. A rookie mistake, and yet here he was, acting like we’d burned the place down. Once we sorted it, their systems came online, no thanks to him.

You’d think that’d be the end of it, but no. That little weasel ran to his boss, whining that we’d done a “kak job.” Karma’s a patient hunter, though, I like to imagine a piano falling from the sky one day, landing square on his head.

Poor Wimpie, though, caught the worst of it. He rolled in late that afternoon, still looking like he’d just crawled out of the ocean, only to get an earful from the Asset Management boss. The guy tore into him, & Wimpie, clueless about the AD drama, just took it. I didn’t even know it was happening until later. That’s corporate life for you, run by the Peter Principle, where people rise to their level of incompetence and stay there, making everyone else’s life harder.

That weekend was a blur of tech, booze, and corporate nonsense, but it taught me something: networks are easier to debug than people.

Russell’s Shoeless Sprint

During my time at Investec, one of the most memorable quirks of our office life was Russell’s habit of slipping off his shoes the moment he settled at his desk. It was as predictable as the morning coffee run, his polished loafers would be neatly tucked under his chair, and he’d work away in his socks, as if the office were his living room. It was endearing, in a way, this small rebellion against the corporate polish of our environment. But one day, Russell’s shoeless routine became the prelude to a minor office legend.

I was at my desk, sifting through emails, when I caught a blur of motion in my peripheral vision. It was Russell, sprinting—socks sliding slightly on the polished floor, down the corridor toward the basement. No shoes, no explanation, just a man on a mission. “Strange,” I muttered to myself, exchanging a puzzled glance with a colleague. Moments later, the office began to buzz with a different kind of energy. People were clicking furiously, refreshing browsers, and muttering under their breath. The network was down. Emails wouldn’t send, systems were frozen, and the usual hum of productivity ground to a halt.

Whispers of the cause soon trickled through. Russell, our resident network guru, had been working on what he thought was a lab router. In a moment of distraction, he’d entered the fateful command: no eigrp followed by a smack of the enter key . The problem? He wasn’t in the lab. He was logged into the main core router. With that single keystroke, he’d wiped out the routing tables, effectively bringing the company’s network to its knees.

Down in the basement, Russell was racing against time, shoeless feet slapping against the cold floor as he dashed to reboot the network. I could only imagine the mix of panic and determination on his face as he worked to undo the chaos. By the time the network flickered back to life, the office had a new story to tell—one that would be recounted with chuckles & headshakes for weeks. Russell returned to his desk, socks slightly dusty, & slipped his shoes back on as if nothing had happened. But from that day on, his shoeless habit carried a new layer of lore: the man who ran barefoot to save the network.

A Lesson in Policy & Power

Every year, the statutory auditors descended upon Investec like a swarm of bureaucratic locusts. Their arrival was met with collective groans from the team. Endless questions, each more tedious and seemingly pointless than the last, filled the air. The Investec CFO had a simple strategy for dealing with them: always answer “no” first, then negotiate if absolutely necessary. It was a masterclass in stonewalling, and we followed it religiously.

One year, after a particularly grueling audit, the auditors insisted we produce a mountain of IT policies. It felt like we were clear-cutting a forest just to satisfy their paper fetish. Policy after policy was drafted, printed, and filed, each one a monument to bureaucratic excess. I thought that was the worst of it—until the next year’s audit brought a new surprise.

I was doing my rounds on one of the floors when I stumbled across a group of people huddled around a table, tinkering with a 3Com Ethernet hub. This wasn’t just any hub—it was unauthorized equipment, the kind that could wreak havoc on our LAN, causing faults and disruptions. One of our freshly minted IT policies, born from the previous audit’s demands, explicitly stated that no network equipment could be installed without approval from the designated authority. That authority? Me.

I didn’t hesitate. I leaned over the table and yanked the hub’s cable out, disconnecting it with a satisfying tug. A snotty youngster in a pinstripe suit, barely old enough to shave, had the audacity to give me attitude. “Who the hell are you?” I demanded, clutching the confiscated hub. He smirked and declared he was an auditor, demanding I return “his property.”

“Have you read and signed the usage policy?” I shot back. His smirk faltered. “No,” he admitted, still smug. That was all I needed to hear. “All of you, out,” I ordered. They stared at me, stunned, as if I’d grown a second head. “If you don’t move, I’ll call security to escort you out.” They didn’t budge, so I made good on my threat. I picked up the phone, dialed security, and within minutes, they were being marched out of the building.

My phone rang shortly after. It was the CFO, his voice a mix of curiosity and amusement. “What’s going on with the auditors?” he asked. I explained the situation—the hub, the policy violation, the attitude. “Excellent,” he said, his approval clear. We arranged a meeting in the reception room with the auditors to set things straight. I laid it out for the snotty kid: we take our policies seriously. He hadn’t signed the usage policy, and until he did, he wasn’t stepping foot back in the building. He argued, of course, but I wasn’t having it. I exchanged a glance with the CFO, said, “He’s not coming in,” and excused myself.

A few days later, a new audit team arrived, minus the pinstriped brat. They were all business, no nonsense. They completed the audit swiftly and, to my satisfaction, gave IT a clean bill of health. It was a small victory, but a sweet one. Policies, it turned out, could be more than just paperwork—they were a line in the sand, and I was happy to hold it.

A Tale of Cisco Dreams & Network Nightmares

My first brush with Dimension Data came in the early nineties when they were a prominent Cisco reseller, strutting their stuff as a Value Added Reseller (VAR) with all the swagger that came with it. They were big, bold, and, as I’d soon discover, occasionally baffling.

The Network Health Fiasco

One of my earliest encounters with them was at the bank, where they provided a service called Network Health. It was supposed to monitor systems remotely over a 64kbps Diginet link—the absolute bare minimum capacity you could get back then. The setup sounded promising, but it quickly became a headache. The system kept throwing false alerts, and I noticed the router buffers were getting flooded. When I dug deeper, it was clear the line capacity was the culprit. The polling was overwhelming the link, causing spikes that the system then averaged out over time, masking the real issue.

I pointed this out to the Dimension Data engineers, expecting a lightbulb moment. Instead, they doubled down, insisting nothing was wrong because the average usage stayed below 64kbps. I explained that averages were meaningless when buffers were flooding in real time, but they just didn’t get it. Frustrated, I canceled the service. It simply wasn’t reliable enough for SLA metrics. For a fancy Cisco VAR, they were as clueless as a bumbling bat.

The Investec Chronicles

Later, at Investec, Dimension Data was back in the picture, supplying equipment and holding a service contract with the bank. Things took a turn for the worse when one of the core Catalyst 6000 switches went belly-up. I logged a replacement call, and an engineer named Guernsey—nicknamed “Guru” by his colleagues, showed up with a new supervisor blade, ready to save the day. He was all confidence, eager to swap the blade and fire up the switch in a hurry.

I stopped him. “Hold on,” I said. “You can’t just slam that in and power it up. The config needs to be loaded first, & the ports need to be disabled, then enabled one by one to avoid a network crash.”

Guernsey, puffing out his chest, declared, “I’m a CCIE. I know what I’m doing.” He even gave me a “Scout’s honour” to seal the deal. Against my better judgment, I let him proceed. Big mistake. He replaced the blade, flipped the switch, & ,surprise, surprise, the network went down in flames. I yanked the switch’s power, kicked him out, and told him I’d handle it myself later that night, the right way, without breaking everything. So much for “Guru.”

The Pension Fund Ploy

Dimension Data seemed to have a knack for producing characters like Guernsey. Another memorable figure was a guy named Dok. He once had the audacity to tell me he’d ensure I’d never work in IT in South Africa again. Why? Because I didn’t play ball with his schemes. We were in the middle of a LAN refresh in Sandton, a multi-million-rand project, when Dok waltzed in with a bold pitch. He promised that if Dimension Data got the deal, their pension fund would invest in the bank. It was a blatant attempt at pension fund farming, a tactic I later heard they’d tried at other financial institutions. When Stephen Koseff, the bank’s CEO, caught wind of it, he promptly showed Dok the door. Good riddance.

The Campus Fiasco

Years later, I wasn’t surprised to hear about the campus deal debacle involving a group of ex-Dimension Data executives. It reeked of the same questionable practices. I’d visited the Dimension Data campus a couple of years prior, and what I saw was shocking for a company of their stature. The network was a time capsule, ancient Catalyst switches from 2002, never upgraded. The multimode fibre was so old it was practically bleached. Nothing was labeled. It was the ultimate irony: Cisco’s leading VAR, preaching cutting-edge solutions to their clients, couldn’t even keep their own house in order. The cobbler’s children, indeed, had no shoes.

Dimension Data’s story, at least in my experience, was one of big promises, flashy titles, and a surprising lack of follow-through. They had the Cisco badge & the VAR swagger, but when it came to the crunch, they often left you with flooded buffers, crashed networks, and a lingering sense of “what just happened?”

Here is another story of the protagonists of DiData trying to fiddle a data centre deal.

Ronald Bartels | LinkedIn | Instagram

I refuse to let death have the last word about her

Most people, when they lose someone far too young, fall into one of two traps. Some keep mentally aging the beloved, imagining her at fifty, sixty, seventy, eighty, watching her wrinkle & slowly fade in their mind’s eye, grieving the same woman a thousand times in a thousand new versions. Every birthday becomes another funeral. Others do the opposite: they let the image blur, the details soften, the voice grow faint, until the person they loved most in the world slips away a second time, slower & more cruelly than the first.

I rejected both. But before that I was falling into the latter trap, realizing that I could not recall her face & how she looked. I had to pivot.

I froze her at the exact moment she was most radiant, most alive, most mine, forty-one, sun in her hair, laughing at one of my terrible jokes, & I declared, with absolute conviction: This is the final portrait. No further revisions will be accepted.

Time, that merciless accountant who keeps sending the bill every August, lost its jurisdiction over her. The calendar itself was bent to my will. That is not denial. That is sovereignty.

What I built instead is what the researchers now call a secure continuing bond. The old grief models, Kübler-Ross & the stages, insisted you had to “let go,” “detach,” “move on,” as though love were a suitcase you could finally set down once you’d carried it far enough. Modern attachment theory (Klass, Silverman, Neimeyer, & others) knows better: the relationship doesn’t end; it transforms. You don’t sever the bond, you carry it forward in a way that does not destroy the survivor.

That is exactly what I have done.

I believe she reads what I write. I write about my day & other interests. I play the music we loved & laugh when she (in my head, in my heart) rolls her eyes at my taste. I remember her birthday with enthusiasm. She simply no longer needs new candles.

I kept the promise I made when I teased her about marrying a younger woman, “even if only by forty-eight hours.” Death does not get to break that vow. I keep her young. Every single year. Stubbornly. Devotedly. Triumphantly.

We still have our private language, our running jokes, our shared eyerolls at the rest of the world. Most widows & widowers lose that intimate dialect when their partner dies. I have kept ours alive. Eternity has become our longest-running inside joke.

Some people spend years in therapy trying to reach this place, where the beloved is neither obsessively updated nor allowed to fade, where love is neither abandoned nor poisoned by accumulated grief. I got here through nothing more complicated than love, stubbornness, & a kind of poetic defiance that refused to grant death any more territory than it had already stolen. It involved some writing, not talking.

I’m not telling you this to fix anything for you. I’m bearing witness.

She is forever forty-one. Forever laughing at my terrible jokes. Forever… mine.

And because I have made that the truth, it is the truth.

Ronald Bartels | LinkedIn | Instagram

My 1995 BMW 320i

Ah, the 1995 Rugby World Cup, what a time to be alive in South Africa. The Springboks' triumphant victory over the All Blacks in that nail-biting final at Ellis Park sparked nationwide euphoria, with streets erupting in celebrations that lasted well into the night. I was right in the thick of it, cruising home in my trusty old Honda Ballade, a reliable little sedan that had served me well through my early career days. But fate, or perhaps a overly exuberant reveler, had other plans. As I navigated the chaotic post-match traffic with my mate Jose riding shotgun, a distracted driver plowed into the rear of my car, crumpling the back end like a tin can. The Ballade was gevoerts, and while we walked away unscathed, it marked the end of an era. Fresh off starting my new job at Madge Networks – a tech firm buzzing with the promise of the communications boom – I decided it was time for an upgrade. No more sensible economy cars; I wanted something with soul, speed, and style. That's how I ended up with my white BMW 320i, the best car I've ever owned. I’m sure my future wife was impressed with the car when I picked her up for our first date at Brown’s restaurant in 1999.

The BMW 320i was part of the E36 generation, a sleek and sophisticated compact executive sedan that BMW produced from 1990 to 1998. Mine was a 1995 model, powered by a smooth 2.0-liter inline-six engine that delivered 110 kW and 190 Nm of torque, mated to a five-speed manual transmission for that pure driving engagement. It was rear-wheel drive, of course, the hallmark of BMW's “Ultimate Driving Machine” philosophy, which gave it razor-sharp handling and a balanced feel that made every corner an adventure. This one was a “full house” version, loaded with all the premium options available at the time: power windows, sunroof, anti-lock brakes, and that glorious air conditioning system that could chill the cabin on the hottest Johannesburg days. The exterior was a pristine Alpine White, gleaming under the sun like a fresh snowfall, with subtle aerodynamic lines that hinted at its performance potential without screaming for attention.

But it was the interior that truly sealed the deal. Sliding into those black leather seats felt like sinking into a luxurious embrace, supple, supportive, and scented with that new-car aroma mixed with a hint of premium hide. They were heated, too, a godsend during those crisp Highveld winters, and the bolstering kept me planted during spirited drives. The dashboard was a model of ergonomic German engineering, with clear analog gauges and a straightforward layout that put everything at my fingertips. And then there was the crown jewel: the factory-installed 6 CD shuttle changer tucked away in the trunk, a high-tech marvel in an era when most cars were still rocking cassette decks. It could hold six discs at once, cycling through them seamlessly via controls on the steering wheel or the head unit. No more fumbling with tapes or single CDs while driving; this setup turned my commutes into personal concerts.

That BMW wasn't just a car; it was a rocket. Punch the accelerator, and the inline-six would rev eagerly to its 6,500 rpm redline, propelling me from 0 to 100 km/h in about 9.2 seconds – respectable for the time, but it felt blisteringly quick thanks to the responsive throttle and that signature BMW growl from the exhaust. On the open road, it ate up highways with effortless poise, stable at high speeds and begging for twisty backroads where its suspension shone. I remember blasting down the N1 toward Pretoria, the wind whipping through the open sunroof, feeling invincible. It handled like a dream, with precise steering that telegraphed every nuance of the tar. Reliability was another plus; over the years I owned it, it never let me down, sipping fuel efficiently while delivering thrills that no other car in my life has matched. Selling it eventually felt like parting with a loyal friend.

The soundtrack to those drives came courtesy of the six CDs perpetually loaded in the shuttle, a eclectic mix that reflected my moods and the mid-90s vibe. First up was Bonnie Raitt's “Road Tested,” a 1995 double live album captured during her tour, showcasing her gravelly blues-rock prowess. It's raw and electric, with standout tracks like “Something to Talk About” and a searing cover of “Love Sneakin' Up on You,” where her slide guitar wails over funky rhythms, evoking smoky bars and heartfelt storytelling. Then there was my John Denver CD, a compilation of his greatest hits, full of folk-country anthems that transported me to wide-open American landscapes. Songs like “Take Me Home, Country Roads” and “Rocky Mountain High” are uplifting odes to nature and simplicity, with Denver's warm tenor and acoustic strumming providing a soothing counterpoint to city stress.

For pure pop joy, ABBA's “Gold” was always ready – a 1992 greatest hits collection that's a disco-pop masterpiece, packed with infectious hooks and harmonies. Tracks like “Dancing Queen” and “Mamma Mia” burst with upbeat energy, synthesizers, and those iconic Swedish melodies that make you tap your feet involuntarily, turning traffic jams into impromptu dance parties. Paul Simon's “Graceland” from 1986 added a worldly twist, blending Simon's clever lyrics with South African township jive and mbaqanga rhythms, courtesy of collaborations with Ladysmith Black Mambazo. Hits like “You Can Call Me Al” and the title track “Graceland” are rhythmic adventures, fusing folk-rock with African grooves for a sound that's both introspective and danceable, reminding me of home even as I drove city highways.

Queen's “Greatest Hits” brought the rock thunder, a 1981 compilation (with updates) featuring Freddie Mercury's operatic vocals and Brian May's guitar wizardry. Anthems like “Bohemian Rhapsody” – with its epic shifts from ballad to hard rock – and “We Will Rock You” pumped adrenaline, perfectly matching the BMW's spirited acceleration. Finally, The Cranberries' “No Need to Argue” from 1994 rounded out the set, an alternative rock gem led by Dolores O'Riordan's haunting, yodeling voice. Songs like “Zombie” deliver powerful anti-war messages with grungy guitars and emotional intensity, while “Linger” offers dreamy, melancholic pop that's achingly beautiful, capturing the angst and hope of the era.

That white BMW 320i wasn't just transportation; it was a chapter of freedom, a vessel for memories scored by those albums. Every time I fired up the engine, it reminded me why I love driving – the thrill, the escape, the pure joy of the open road. If I could turn back time, I'd buy it all over again.

Ronald Bartels | LinkedIn | Instagram

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Het smeer'm zeer'm

Ik heb zo'n haast ik kan niet wachten het moet nu zo snel mogelijk ik kan niet wachten mijn geduld is op verdwenen in snel sneller snelst het is toch beschikbaar waarom is het er dan niet

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ik kan niet wachten ik moet er snel heen anders mis ik het dan heb ik de ervaring niet is het niet meegemaakt heb ik het nooit gezien en het is er de foto's, het artikel de plaatjes hebben ze geschoten voor mijn ogen de lovende kritieken heb ik gehoord onmisbaar, schitterend dus ik ga er heen het moet wel wat kan ik anders nog dit is het beter is er niet

ik kan niet wachten dat is niet langer mogelijk onnodig waarom zou ik ik ga naar een ander nou nou het is er je zei dat het er is waarom is het er nog niet het kon besteld worden er waren opties ik kan niet meer zonder het is bereikbaar ik kan er direct bij ik kan niet meer wachten ik kan niet meer op tegen de beschikbarende werkelijkheid pas maar aan nu ja

ik kan niet wachten ik zag het daar staan nu wil het nu meteen het wordt beter als het er is maar dan wel morgen niet later dan wil ik alweer wat anders jij hebt het je hebt het laten zien en je wil dat ik het wil en dat wil ik nu kan ik niet meer zonder denk ik ik wil wat jij mij laat zien wat jij vindt dat ik moet hebben wil ik ik wil het in mijn ruimte in de buurt vlakbij dan zet ik het aan wanneer ik wil

ik kan niet wachten waar staat wachten in de winkel hoeveel? dan wacht ik nog wel even tot de vraag afneemt hoor ik kijk elders wel voor een goedkopere versie ga jij maar op dat geduld zitten zitten oelewapper flapdrol ik log wel mooi uit