Karl Gantner, writer

The Singularity is Already Here

Karl Gantner — Wed, 17 Apr 2024 00:00:00 +0000

The technological singularity…is a hypothetical future point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable consequences for human civilization.

Wikipedia entry on the technological singularity

Although we might not feel that we have past into the technological singularity, much of it may already be here. What we imagine the post-singularity world to be like is largely here. We’ve quietly surrendered control of vital parts of our lives to algorithms, from how we connect and communicate to how we manage our finances. Turns out that it’s just not as flashy or interesting as some had hoped.

Most of social media is just algorithms that present us content they think we find engaging from people we don’t know, who we’ve never met, and have no idea how to contact us. Americans spend over 1,000 hours on social media each year (2.7 hours per day) getting news and staying connected to their culture. All of this is mediated by algorithms. Even the things we share with friends and family are dictated by what we see in our feeds and share with others (this has gone so far that some of my own conversations with friends are just an endless stream of reels/tiktoks the algorithm has shown us and we are now sharing). Perhaps the most fundamental human activity, interacting with other humans, is now veiled by an algorithm.

The stakes in social media aren’t high–if the algorithm doesn’t work, you can just stop looking at your phone–but we’ve seeded significant control to machines even in high stakes areas like finance. 60-70% of all trades on US, European and Asian exchanges are executed by algorithmic traders. High-frequency trading algorithms execute strategies at speed, capitalizing on minute price fluctuations while influencing market stability. Even personal finance is controlled by algorithms–robo-advisors use data-driven models to build investment portfolios, and loan approvals often rely on algorithmic risk assessments. Essentially, algorithms have become the engines driving the speed, efficiency, and sometimes the volatility of the modern financial system.

I won’t say that the singularity is here in full-force. It’s not (where’s my flying car?). But in crucial ways, our world is already looking much like a post-singularity. Every day we cede more control over important facets of our life and future to algorithms. This isn’t necessarily bad, there are many things that computers can do much better than humans. But it’s important to notice what’s happening, because we might not need anything like true super-intelligent computers to start living in a post-singularity world.

Should we fear aliens?

Karl Gantner — Fri, 01 Jul 2022 00:00:00 +0000

Should we fear aliens? On one hand, it seems absurd that intelligent extraterrestrial life would come all this way to harm us when there is such abundance in the galaxy. Earth has no unique minerals that couldn’t more easily be mined from asteroids. Watts generated from our Sun are no different than from any of the millions of other stars in the galaxy. The fear we feel imagining some alien invasion (à la War of the Worlds) must be nothing more than an evolutionary leftover that has us imagining stalking tigers behind every rustling bush.

But what if that fear is not vestigial? What if it’s telling us something important about our universe and survival? Exploring this idea is at the center of my story, [KILLING A TIGER].

KILLING A TIGER is centered around a decision that the protagonist, Rodion, must make when his spacecraft observes an alien probe departing the Solar System. Does he let it go, and reveal humanity’s existence to some unseen life form, or does he shoot it down, and keep our system hidden from any covetous alien eyes? You’ll have to read and let me know what you think he ends up choosing. More on Rodion later.

Inspiration for this story came from an idea: if evolution is true here on Earth, then it is true throughout the universe, and the process of natural selection that has produced humans–with all our flaws and neuroses–will be the same one that produces all intelligent extraterrestrial life we may encounter.

This should concern us.

We need only look at life on Earth to see that the traits we hope ET to have (kindness, agreeableness, empathy) are exceptionally rare, and when they do exist, only extend to kin (of which aliens most certainly won’t confuse us for). We should recognize that any intelligent alien life we may contact is unlikely to be the scientific humanitarians we hope for, and more likely will be tough survivalists with a deep innate drive to aggressively spread.

If aliens can navigate the stars to come to visit us, it’s natural to expect them to be highly intelligent, rational beings; however, rationality is not a requirement to spread. By biomass, arthropods (cicadas, scorpions, butterflies, crabs, centipedes) represent the most successful animal life on Earth, with more than 17-times greater biomass than humans. No arthropod has kindness, agreeableness, empathy, or an ability to apply abstract logic to draw conclusions about the outside world. Arthropods operate according to survival heuristics programmed into their comparatively simple brains to survive long enough till they can reproduce above the replacement level.

Evolution has favored rationality in only 0.01% of all life by biomass. Though it seems unlikely that any life without rationality would be able to leave its homeworld and travel across the stars, that may be our bias talking. Remember, we don’t have any evidence yet that rationality is capable of getting us to the stars in any meaningful, enduring way either. Those noble traits we as humans uphold are very uncommon in the grand scheme of life.

But even if alien life is rational, can we expect them to treat us with kindness and empathy? Evidence is to the contrary. When these behaviors do evolve in nature, they evolve in animals that rely on a social group to survive and almost always are directed towards kin. There is a depressingly long history of humans showing compassion to their kin-group while treating other humans with disdain and hatred. (For evidence, open the nearest history book and flip to any page.) Kindness and empathy exist in nature, but there is no evolutionary pressure for it to be extended beyond the social group, much less to members of different species. There are no natural forces of selection that motivate intelligent alien life to show any compassion or empathy towards us.

Traits that would turn extraterrestrial life into scientists and humanitarians, are not likely to be the dominant ones that we encounter in life outside our planet. We should expect to find aliens that are survivors and spreaders, who can withstand hardship and multiply themselves aggressively in a wide variety of environments.

So what should we do about it? First, we should be listening for signs of intelligent life to understand how many there are out there. And by no means should we be advertising our own position. Secondly, we should not be under any illusions about how we should expect alien life to behave. Don’t expect aliens to be reasonable scholars eager to learn about new life for the intellectual pleasure of it. Expect nervous xenophobes, curious but skittish scavengers, uninterested leviathans on million-year migrations, or hungry tigers pushed from rich hunting grounds into unexplored space.

But I could be wrong. What makes this debate difficult is that we haven’t observed any form of alien life–intelligent or not. Maybe evolution doesn’t work the same on other planets? Maybe there’s a deeper mechanism at play we can’t yet see? Which brings us back to Rodion, the protagonist in KILLING A TIGER. What Rodion chooses depends not so much on these arguments, but on what past events speak to him in that moment where he must decide to kill or free the alien probe. If you were in his place, with just a moment to react, what part of your own history would influence your decision?

As a fiction author, my job is to understand what goes on in the mind during these critical decisions. These moments define our characters, and give us insight into our own lives. None of us may live long enough to witness contact with intelligent extraterrestrial life–if it ever happens–but through engaging with fictional characters as they struggle through difficult choices, we can better appreciate the messy human calculus that influences our own lives.

Killing a Tiger

Karl Gantner — Fri, 01 Jul 2022 00:00:00 +0000

High Saturn Orbit, 2554: Rodion watched the red marker tick across the display with each sweep. The unidentified object had been using the inner planets to accelerate its departure from the solar system, toward some distant home the astrogators had not yet discerned. They hypothesized that the object had spent at least three years within the Solar System. Its dark carapace and razor-thin profile meant it would only ever be discovered when it kicked on its exotic engine and accelerated into deep space. The science officers debated it, but Rodion knew, it was the first sign of intelligent life. Humanity was not alone.

Rodion imagined how the object had drifted through human space collecting radio transmissions from Earth, Luna and Mars, analyzing and cataloging human technological sophistication, defensive capabilities and modes of thought. And now, its databanks full, the object was leaving without a word.

At first they thought it was a missile. Reacting, Rodion ordered the rail cannon charged and the object targeted. He wondered how long this probe had traveled. How many other solar systems had it stalked? If he were to open its databanks, how many other civilizations would he find there? It would be obscene to destroy such a thing, this herald of new life. What threat could the object pose when there is such abundance of resources and energy throughout the universe?

He stared at the hazy pixelated blur on the telescopic sights, at a product of minds not human. His heart trilled at the thought of the object’s owners one day returning to welcome humanity into the galactic community.

Still, he had belayed two requests to de-charge the rail cannon and disengage target-lock. Some idea, buried deep in his mind, prevented Rodion from standing down. There was a fear hanging in his belly that told him this probe was a predator. An old fear. He could destroy the object now if he wished and humanity would stay hidden in its pocket of the universe until a time of its choosing. Or he could let it go and reveal humanity to these unknown stalkers.

Amur Forest, Russian Far East, 2543: Rodion chose to spend his leave hunting and fishing in the Amur. He’d grown up in the far eastern town of Khabarovsk where his father used to take him boating on the Amur River. Rodion hired a man named Saveliy who worked in counter-poaching for the Oblast and guided under the table. A rotocopter carried them 160 km into the forest.

They had been trekking along a tributary when Saveliy stopped them near a small pile of dirty snow. He pointed at a paw print fifteen centimeters across. “Tiger. Adult male. In the neighborhood,” Saveliy pointed out. The idea of being close to such a rare and powerful creature excited Rodion, but when he stood up, Saveliy’s face was tight. “We have to kill it,” the guide said.

“What? Why?”

Saveliy scanned the far embankment, looking for signs. “Little game left in forest today. If wind changes, we will be hunted.”

“I thought you were supposed to stop poaching?”

“No. I stop tigers from poaching humans.”

Saveliy routed them through the forest, but when they lost the tiger’s tracks he resolved to keep them upwind.

That night they discovered the corpse of a rare deer scavenged by birds. Saveliy chased them off and inspected the carcass. It had been killed in the last thirty-six hours by the same tiger they spotted earlier.

“So it’s full,” Rodion said. “It’ll leave us alone. No problem.”

“Yes problem,” Saveliy countered. “Nature made tigers to hunt. No matter the weight in their stomachs.”

They were forced to make another detour, attempting to stay upwind of the tiger. Saveliy told Rodion, “Not to worry. We find some decent fishing still. I know a place.”

Tomsk State University, Tomsk, Russia, 2529: Rodion had wanted to study evolutionary biology and Professor Krupin was the only one in Tomsk who studied it. Rodion didn’t understand the precise work Krupin specialized in, but when Rodion asked him about his lab, Krupin was so thrilled by the curiosity that he gave Rodion a personal tour.

“We study the behavior of insects.” Krupin said. Krupin led him into a room called the menagerie. One wall was stacked to the ceiling with brown plastic tubs containing all sorts of large and angry insects. The other was tiled with cork-board on which dead insects were pinned and preserved. “Their brains are very simple, which makes it easier to understand which neurons and pathways contribute to and cause behavior.” Krupin pulled out one of the plastic bins. Inside was a ten centimeter bush cricket, a large green-carapaced bug that slammed itself against the walls. “These guys are aggressive.”

Krupin went on to talk about how they were surgically altering neurons in the brains of crickets to try to change their innate behavior. As he was explaining and pointing at the tiny circuit board transmitter attached to the cricket’s head, his thumb nudged the release and the bin’s hatch sprung open. The cricket and professor noticed the mistake at the same time. The cricket leaped for freedom. Krupin slammed the lid down and caught the cricket halfway up the abdomen. It tumbled to the ground, landing on its back. Krupin and Rodion saw that the cricket’s abdomen had split open from the impact. Krupin swore. They watched as out of the abdomen bloomed intestines and fatty tissue. Without hesitation, the cricket began pulling its own viscera into its mouth, stuffing its small head with its own insides. Rodion watched with disgust and confusion. Krupin dispatched the bug, cleaned up its entrails and tossed it in the trash.

After the cricket was removed, Rodion he found the words to express his confusion. “Why would it eat itself? How could it not feel that?”

Krupin smiled and launched into a long lecture of the selection pressures on all multicellular organisms. It came down to the fact that fat was hard to come by in nature, so the cricket evolved a behavior to consume it wherever it was found. “Never bothered to evolve the behavior not to consume its own fat,” Krupin laughed. “Evolution is not interested in rationality. Just survival.”

Amur Forest, Russian Far East, 2543: Saveliy found a nice river for them to fish. It was one of the few places the canopy opened and Rodion could see the sky. Rodion spent the day in the stream fishing. Never mind that nothing was biting and there wasn’t anything larger than juvenile perch. He was enjoying his leave. The tiger was long from his mind. He found a nice eddy where the water was shaded and still and began pulling up perch. He was looking to freeze some to keep for a nice treat next time his stomach got tired of the high-protein mix they serve on spacecraft.

Saveliy fished with him until lunch, then went back upstream to their camp to fetch more bait. A few minutes after Saveliy left, the wind shifted to the south and sprayed water in Rodion’s face. He waded closer to shore where he was less exposed to the wind. When Saveliy didn’t return, Rodion figured he must have gotten tired and taken a rest at the campsite. Good. The guide spent too much time by himself and had forgotten how to have a conversation. Might as well just be alone, thought Rodion.

When the sun met the horizon, Rodion packed up his fishing rod, waders and pail of fish and hiked back to camp.

As Rodion approached the camp, he found Saveliy’s fishing pole laying across the path. Ahead, his backpack sat face-down in the mud, its contents spewed across the ferns and rocks. Rodion crept forward. He saw the wall of their teal tent flapping in the wind. A large red stain blotted the fabric. Saveliy was gone. All that Rodion found was a trail of blood leading into the forest.

Saveliy was right. The tiger would still hunt. It hunted not because it was hungry but because it couldn’t help itself. It evolved for survival, not moderation. Especially now, when the forests were tightening, the only tigers one would expect to find were the best hunters, those that were the most hungry.

Rodion was alone now. No wise guide to help him find his way out, what to avoid, what to trust, how to survive. He understood the tiger then. Nature rewarded only survivors. The fish in his pail were more than he could eat today. But still he caught them.

High Saturn Orbit, 2554: Captain Rodion turned to his weapons officer and gave his order.

The Species Mill

Karl Gantner — Wed, 27 Mar 2019 00:00:00 +0000

Buy the paperback

History is a weapon, and Taukudo—an historian traveling between solar systems aboard an interstellar trade ship—is about to discover its power.

A drain on the ship’s bottom line, Taukudo dreams of a way to convince the rest of the crew of the value of history—and thus validate his presence aboard. As the ship approaches the planet Khronica, he believes he has his chance.

Khronica is home to his teacher—a brilliant historian who has just completed a work of genius: a complete synthesis of human history. Taukudo will be the work’s first evangelist and convert the crew to his belief in history’s worth.

But when the ship arrives, Taukudo sees that something has gone wrong. Khronica is a wasteland. Civilization has collapsed. So Taukudo descends to the surface to determine what happened. But he only has three days to do so—as the ship slingshots through the system—or he will be left on the ruined world forever.

Taukudo knows something of his teacher has survived. He descends to the planet, intent on finding the value of history, unsuspecting that its value may be more malignant than he imagines.

Karl Gantner — Mon, 01 Jan 0001 00:00:00 +0000

Why friends are always right – no matter their views

Author: Tim Harford Publication: Financial Times (FT Magazine / “Undercover Economist” column) First published: 23 February 2024 — https://www.ft.com/content/cfa85b4e-025a-44b7-b45b-9407aae5a5e7 Reposted on author’s site: 21 March 2024 — https://timharford.com/2024/03/why-friends-are-always-right-no-matter-their-views/

Local research copy, saved for personal study. Text © Tim Harford / Financial Times. This is the FT column the project brief was trying to recall — the “students at larger universities don’t compromise” point comes from the Bahns, Pickett & Crandall (2012) study discussed below.

My colleague John Burn-Murdoch recently presented striking evidence of a new trend: young men and young women are becoming politically segregated. Young men now sit substantially to the right of young women on the political spectrum. This is an international phenomenon and it’s new.

Should we be surprised? Society seems to be polarising along every possible axis and on every conceivable issue. Consider the apparently simple question of how the US economy is faring. The answer is simple: it depends whether the sitting president is on your team or not. Little else matters. From the public’s perspective anyway.

According to Gallup, Democrats are 57 percentage points more likely than Republicans to say that the economy is improving. Wind back four years, to early 2020 when Donald Trump rather than Joe Biden was president, and you find a very similar gap: 54 percentage points. Back then, naturally, it was the Republicans who believed the economy was improving.

To pick another issue, should there be a memorial for those killed by the Covid-19 pandemic? The death toll in the US alone is more than a million people. That seems like it might be worth some sort of public monument, but what should it say and how? The podcast 99% Invisible recently followed the efforts of bereaved families to galvanise support for something more than a national memory of “the time that we all couldn’t find fucking yeast”. But even a memorial is controversial. One Republican politician told the podcast he’d support a memorial that apologised for the Covid vaccine.

It is tempting to blame the politicians for all this polarisation. Yet if successful politicians are more inflammatory than they used to be, more keen to make enemies than friends, that is probably a response to something else. But what?

Consider a few thought-provoking findings from social science. Nearly twenty years ago three academics, Cass Sunstein, Reid Hastie and David Schkade, assembled focus groups from left-leaning Boulder, Colorado, and separately from conservative Colorado Springs. Participants were privately asked their views on politically heated topics, then put into groups with others from their town and asked to discuss the issues together.

We might hope that this process would lead people to question their certainties, making them more humble and perhaps pulling them towards the political centre. The opposite was true. Individuals from Boulder moved further to the left after discussing the matter with fellow Boulderites. They also became more similar, converging on a leftwing view. Finally, they became more confident that they were correct.

The mirror image applied to the participants from Colorado Springs. After discussion with others from their town they moved further to the right and became more certain of themselves. The two groups, not so different at the start, moved far apart as a result of exposure to other people with similar views. This process is known as “group polarisation”.

Another study examined student friendships. The researchers, Angela Bahns, Kate Pickett and Christian Crandall, compared the behaviour of students at small campuses, with about 500 students each, to the friendship structure at the University of Kansas, which has the student population in the tens of thousands. The researchers sought out pairs of people who were chatting in the student union or cafeteria and gathered a host of telling details: students’ age, sexual orientation, ethnicity, how much they drank, smoked or exercised and their attitudes to a variety of social and political questions. They were also asked about their friendships.

In principle, the University of Kansas offered a far greater diversity of views and lifestyles, with 25,000 possible friends to choose from. But in practice, students on the smaller campuses had more diverse friendship groups. The reason? On a large campus, students could find their social and ideological soulmates. On small campuses, they had less choice and so had to make friendships work even when they bridged social or ideological gaps.

Taken together, these studies suggest an unnervingly plausible two-part engine of polarisation: first, given the choice, we seek out other people like us. Then, being surrounded by people like us makes us more extreme in our views and more confident that those views are correct.

Our current information ecosystem offers us more choice than ever. Alongside social media we can pick and choose from websites, podcasts and YouTube channels to reflect any interest, geography and ideology. And how do we use that choice? Generally, by seeking out people who share our views, broadcasters who seem to “get” us and, often, by avoiding news altogether.

I am wary of blaming social media for all our ills. It can be a great source of support and information, particularly for people in an unusual situation: anything from having a disability to a minority sexual orientation to a niche hobby. There is a real benefit to being able to reach out and find like-minded people.

Yet we must acknowledge the risk that we are self-selecting into echo chambers. Social media algorithms may be giving us a push, recommending content to us that drives “engagement”, the most surprising, outrageous and often toxic material. But we shouldn’t blame algorithms steering us away from serious and thoughtful exposure to different points of view. We are quite capable of choosing that for ourselves.

Written for and first published in the Financial Times on 23 February 2024.

Karl Gantner — Mon, 01 Jan 0001 00:00:00 +0000

Literature survey: does community size drive polarization?

Essay: Larger Communities Breed Polarization — “A peaceful world is a disconnected one.” Compiled: 2 September 2026 Companion model: influence.jl (bounded-confidence opinion dynamics; contrasts random interaction with nearest-neighbour / homophilous interaction).

0. The short version

The essay’s thesis — that a larger pool of potential associates lets people sort into like-minded clusters, and that those clusters then push their members to the extremes — is a recombination of three well-established research literatures:

Opportunity → homophily. When people have more choice of associates, they exercise finer-grained preferences for similarity. Friendships on large campuses are more ideologically and behaviourally matched than on small campuses, even though the large campus is objectively more diverse (Bahns, Pickett & Crandall 2012; Bahns et al. 2017). Same mechanism in racially diverse schools (Moody 2001) and immigrant enclaves (Borjas — see §5).
Homophilous discussion → extremity. Deliberation among like-minded people produces group polarization: members move further in their pre-existing direction and become more confident and more internally uniform (Sunstein 2002, 2009; Schkade, Sunstein & Hastie 2007). Cross-cutting exposure does not reliably fix this and can backfire (Bail et al. 2018).
Formal opinion-dynamics models show that whether a population converges or fragments depends on interaction structure and openness — and, directly relevant here, that the number of stable opinion clusters grows with population/group size (Kou et al. 2012; Hegselmann & Krause 2002; Flache et al. 2017).

Two framing caveats worth engaging with in the essay:

Individual-level polarization is rarer than system-level polarization. On Reddit the 2016 shift was driven by new users sorting in, not by existing users radicalising (Waller & Anderson 2021); in US survey data most issue attitudes move in parallel rather than pulling apart (Baldassarri & Bearman 2007). The “engine” is often compositional sorting, not conversion — which is exactly what a size-and-choice argument predicts.
The “Big Sort” geographic-sorting claim is contested (Bishop 2008 vs. Abrams & Fiorina 2012), though newer big-data work revived it (Brown & Enos 2021).

1. The direct precedent — choice, opportunity, and friendship similarity

This is the cluster the half-remembered Financial Times piece belongs to.

The FT article

Harford, T. (2024). “Why friends are always right — no matter their views.” Financial Times, 23 February 2024. → papers/harford-2024-why-friends-are-always-right-FT.md (full text saved).

Harford’s “two-part engine of polarisation”: (1) given the choice, we seek out people like us; (2) being surrounded by them makes us more extreme and more certain. He builds it from the Sunstein Colorado experiment (§2) and the Bahns campus-size study (below). Opens by citing John Burn-Murdoch’s FT data journalism on the young men / young women ideological divergence — a live, international example of a large connected population sorting apart.

→ papers/bahns-pickett-crandall-2012-social-ecology-similarity.pdf · DOI 10.1177/1368430211410751

The keystone paper. “Free-range dyad harvest”: approached pairs of people already socialising at the University of Kansas (~25,000 students) vs. small Kansas colleges (~100–500 students), measured attitudes, values, prejudice, and health behaviours. Friend dyads at the large university were significantly more similar than dyads at the small colleges, across attitudes, beliefs and behaviours. Large-campus students also reported greater “relational mobility” (ability to enter and leave relationships).

Interpretation: a bigger choice set lets latent similarity preferences express themselves at finer grain. This is the empirical core of the essay’s thesis.

→ papers/bahns-etal-2017-similarity-niche-construction.pdf · DOI 10.1037/pspp0000088

11 samples, 1,523 interacting pairs. Tests why friends are similar: selection (“niche construction”) vs. social influence. Finds similarity is mostly selection at the point of formation — it does not grow with closeness, discussion, or relationship length. Important nuance for the model: in this dataset the homophily is front-loaded (who you pick), not an ongoing convergence process. influence.jl’s influence step is the second mechanism (§2), which other work supports more strongly for attitudes under group discussion.

→ papers/mcpherson-etal-2001-birds-of-a-feather.pdf

The canonical homophily review. Distinguishes baseline homophily (induced by the composition of the pool) from inbreeding homophily (preference beyond what the pool forces). Key line for the essay: homophily in larger, more differentiated settings is limited less by opportunity and more by preference — so bigger, more heterogeneous populations permit more preference-driven sorting, not less.

Moody, J. (2001). Race, school integration, and friendship segregation in America. American Journal of Sociology, 107(3), 679–716.

JSTOR: https://www.jstor.org/stable/10.1086/338954 (paywalled — no local copy)

Friendship segregation by race peaks in moderately diverse schools and, crucially, rises again once a minority group is large enough to form a self-sufficient community: students then find enough same-group friends and stop forming cross-group ties. This is the size-threshold (“critical mass”) version of the essay’s argument, with race standing in for ideology. Also: larger schools show more homophily at equal preference (opportunity effect).

2. Homophilous discussion → extremity (group polarization / echo chambers)

Sunstein, C. R. (2002). The law of group polarization. Journal of Political Philosophy, 10(2), 175–195.

→ papers/sunstein-2002-law-of-group-polarization.pdf (text = the 1999 U. Chicago Olin working paper, identical content)

States the “law”: deliberation moves a like-minded group toward a more extreme point in the direction it was already leaning. Two mechanisms — (a) limited argument pools (a like-minded group hears mostly arguments for one side) and (b) social comparison (people adjust toward the group’s perceived valued position). Explicitly extends the argument to the internet and self-selected media.

Schkade, D., Sunstein, C. R., & Hastie, R. (2007). What happened on Deliberation Day? California Law Review, 95, 915–940.

California Law Review: https://www.californialawreview.org/print/what-happened-on-deliberation-day (no clean local PDF)

See also Schkade, Sunstein & Hastie (2010), “When deliberation produces extremism,” Critical Review, 22(2–3), 227–252.

The Boulder / Colorado Springs experiment Harford cites. Liberals from Boulder and conservatives from Colorado Springs discussed climate, affirmative action, and civil unions in politically homogeneous groups. After ~15 minutes: both groups moved to the extreme, became more internally uniform, and the gap between the two towns widened. A clean demonstration that grouping like with like is itself polarising.

Sunstein, C. R. (2009). Going to Extremes: How Like Minds Unite and Divide. Oxford University Press.

→ papers/sunstein-2009-going-to-extremes-ch1-polarization.pdf (Chapter 1)

Book-length treatment; Ch. 1 walks through the Colorado study and the general theory in accessible prose. Good source for a quotable framing.

→ papers/bail-etal-2018-exposure-opposing-views-polarization.pdf

Field experiment: paying Twitter users to follow a bot retweeting the other side made Republicans more conservative and (non-significantly) Democrats more liberal. The naive fix — “just expose people to the other side” — backfires. Relevant to the essay’s implied policy corollary (that forced mixing may not de-polarize).

Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S. J. (2019). The origins and consequences of affective polarization in the United States. Annual Review of Political Science, 22, 129–146.

→ papers/iyengar-etal-2019-affective-polarization.pdf

Review of affective polarization (inter-party animus rather than issue extremity). Identifies partisan social identity + a media/geographic environment that lets partisans avoid the other side as key drivers. Useful for separating “opinions spread apart” from “people come to dislike each other,” which the essay may want to keep distinct.

3. Formal models of opinion dynamics (what `influence.jl` is doing)

Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence: Models, analysis and simulation. JASSS, 5(3), 2.

→ papers/hegselmann-krause-2002.pdf

The HK bounded-confidence model: agents average the opinions of others within a confidence radius ε. Large ε → consensus; small ε → multiple stable clusters (“polarization or fragmentation”). influence.jl’s theta(x,k) = 1/(1+(k-x)^2) is a smooth version of the same idea (weight decays with opinion distance instead of a hard cutoff).

Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3(1–4), 87–98.

→ papers/deffuant-etal-2000-mixing-beliefs.pdf

The other founding bounded-confidence model (pairwise random encounters + partial adjustment). High threshold → one central cluster; low threshold → several. Directly comparable to the use_closest_neighbors = false branch of influence.jl.

Kou, G., Zhao, Y., Peng, Y., & Shi, Y. (2012). Multi-level opinion dynamics under bounded confidence. PLoS ONE, 7(9), e43507.

→ papers/kou-etal-2012-multilevel-opinion-dynamics.pdf

Most directly on “size.” With heterogeneous confidence levels, the number of final opinion clusters is approximately a linearly increasing function of group size, holding the confidence bound fixed. i.e. bigger populations fragment into more distinct camps — a formal statement of the essay’s claim. (Note: a separate strand finds pure sample size negligible when agents are redrawn from the same distribution — the size effect needs either a fixed population with fixed neighbourhoods or heterogeneity.)

→ papers/flache-etal-2017-models-social-influence.pdf

Authoritative review of assimilative, similarity-biased, and repulsive influence models, and why homogeneous influence tends toward consensus while similarity-biased influence (exactly influence.jl’s nearest-neighbour branch) sustains persistent clustering. Best single citation to situate the companion model in the literature.

Mäs, M., Flache, A., & Helbing, D. (2010). Individualization as driving force of clustering phenomena in humans. PLoS Computational Biology, 6(10), e1000959.

→ papers/mas-flache-helbing-2010-individualization.pdf

Adds a “need for distinctiveness” noise term. Produces a metastable regime: consensus within clusters, diversity between them — the structure the essay describes.

Mäs, M., & Flache, A. (2013). Differentiation without distancing: Explaining bi-polarization of opinions without negative influence. PLoS ONE, 8(11), e74516.

→ papers/mas-flache-2013-differentiation-without-distancing.pdf

Shows you can get bi-polarization from positive influence + homophily alone — you do not need people to actively repel opposing views. Supports a gentler reading of the essay’s mechanism.

Baldassarri, D., & Bearman, P. (2007). Dynamics of political polarization. American Sociological Review, 72(5), 784–811.

→ papers/baldassarri-bearman-2007-dynamics-political-polarization.pdf

Model + GSS data. Explains the “absence and presence” paradox: takeoff polarization on a few salient issues, parallel movement on most. Interaction structure (who talks to whom about what) governs whether an issue takes off. Good counterweight against overclaiming.

Axelrod, R. (1997). The dissemination of culture: A model with local convergence and global polarization. Journal of Conflict Resolution, 41(2), 203–226.

JSTOR: https://www.jstor.org/stable/174371 (paywalled)

Classic: local assimilation on a lattice still leaves stable distinct cultural regions. Larger / more feature-rich spaces sustain more regions. Historical anchor for the modelling section.

DellaPosta, D., Shi, Y., & Macy, M. (2015). Why do liberals drink lattes? American Journal of Sociology, 120(5), 1473–1511.

→ papers/dellaposta-shi-macy-2015-liberals-lattes-LSE-summary.pdf (LSE blog summary) · papers/dellaposta-2018-real-reason-liberals-drink-lattes-contexts.pdf (Contexts, popular write-up). Full article paywalled: https://doi.org/10.1086/681254

Homophily + influence in a large network turns uncorrelated tastes and opinions into a tightly bundled “lifestyle politics” package — “liberals drink lattes because their friends do.” Mechanism for how a large connected population manufactures super-issues.

See also DellaPosta, D. (2020), “Pluralistic collapse,” American Sociological Review 85(3), 507–536 — https://doi.org/10.1177/0003122420922989 (paywalled).

4. Scale of the political unit: size, participation, and democracy

Dahl, R. A., & Tufte, E. R. (1973). Size and Democracy. Stanford University Press.

(book — not in folder)

Foundational: a trade-off between citizen effectiveness (better in small units) and system capacity (better in large units). Larger polities dilute the individual voice, which the essay could tie to disengagement and factionalisation.

Oliver, J. E. (2000). City size and civic involvement in metropolitan America. American Political Science Review, 94(2), 361–373. — and Democracy in Suburbia (Princeton UP, 2001).

APSR: https://doi.org/10.2307/2586017 (paywalled)

Civic and political participation declines as municipality size rises. But there is a twist directly on-theme: homogeneous small towns also depress participation, because there is nothing to argue about. Conflict-free community = disengaged community — a near-restatement of “a peaceful world is a disconnected one,” from the opposite direction.

→ papers/centola-etal-2018-tipping-points-social-convention.pdf · DOI 10.1126/science.aas8827

Controlled experiments on naming conventions: a committed minority flips the group norm once it reaches ~25% of the population, and fails below that. Two implications for the essay: (a) norm change is threshold-driven, so a sub-group that reaches critical mass can lock in and enforce its own convention rather than compromise; (b) in a larger community the absolute number needed to reach 25% of any given interaction neighbourhood is larger, so established positions are stickier and coordination on a shared norm is harder.

Dunbar, R. I. M. (1992). Neocortex size as a constraint on group size in primates. Journal of Human Evolution, 22(6), 469–493.

Plus Dunbar (1993, Behavioral and Brain Sciences 16); Hill & Dunbar (2003, Human Nature 14). (paywalled — no local copy)

“Dunbar’s number” (~150): a cognitive ceiling on stable personal relationships. If real, any community >1,000 must be navigated through sub-groups and categories rather than personal acquaintance — a mechanism for why large communities fracture into identity blocs. Note the critiques: Lindenfors, Wartel & Lind (2021), “‘Dunbar’s number’ deconstructed,” Biology Letters 17 — https://doi.org/10.1098/rsbl.2021.0158 — argue the number is statistically unsupported. Cite carefully.

5. Geography, density, and political sorting

Bishop, B., & Cushing, R. G. (2008). The Big Sort: Why the Clustering of Like-Minded America Is Tearing Us Apart. Houghton Mifflin.

(book) The popular statement of the residential-sorting thesis: Americans increasingly live among the politically like-minded, and homogeneous localities drift to the extremes (explicitly invoking group polarization).

Rebuttal to engage: Abrams, S. J., & Fiorina, M. P. (2012). “The Big Sort that wasn’t: A skeptical reexamination.” PS: Political Science & Politics, 45(2), 203–210 — partisan residential segregation at the county level is modest and fairly stable.

Rodden, J. A. (2019). Why Cities Lose: The Deep Roots of the Urban–Rural Political Divide. Basic Books.

(book) Dense places vote left, sparse places vote right, and the gradient has steepened for a century across democracies. Structural (economic-geography) story rather than a choice story, but complementary: population density itself predicts ideology.

Brown, J. R., & Enos, R. D. (2021). The measurement of partisan sorting for 180 million voters. Nature Human Behaviour, 5(8), 998–1008.

https://doi.org/10.1038/s41562-021-01066-z (paywalled; preprint via Harvard DASH)

Individual-level geolocation of ~180M registered voters: the typical voter’s nearest neighbours are strongly co-partisan; partisan isolation is high across densities, not just in cities vs. countryside. Revives the sorting thesis with far better data than Bishop had.

https://doi.org/10.1016/j.jesp.2013.10.010 (paywalled)

People who feel a values-mismatch with their local community are more likely to move, and they move toward more ideologically congruent places — a behavioural micro-foundation for the sort. The larger and more varied the set of destinations, the easier this is.

Scipioni, M., & Tintori, G. (2021). A rural-urban divide in Europe? An analysis of political attitudes and behaviour. JRC Technical Report EUR 30587 EN, European Commission.

→ papers/scipioni-tintori-2021-rural-urban-divide-europe-JRC.pdf

European Social Survey, 2002–2018: a robust urban–rural gradient in attitudes to immigration, the EU, and trust in institutions, widening over time — the divide is a continuous function of settlement size/density, not a binary. Directly usable evidence that the pattern is not US-specific.

Related (paywalled): Kenny, M., & Luca, D. (2021), Cambridge Journal of Regions, Economy and Society 14(3), 565–582; and “Drifting further apart?” (Political Geography, 2024).

Cramer, K. J. (2016). The Politics of Resentment. University of Chicago Press.

(book) Ethnographic account of rural Wisconsin identity as defined against the metropolis — the felt experience of the density divide.

6. Online communities: size and toxicity

→ papers/waller-anderson-2021-nature-published-version.pdf (published) · papers/waller-anderson-2021-polarization-online-platforms.pdf (arXiv 2010.00590 preprint)

Neural embeddings of 5.1B Reddit comments across 10,000 communities, 2005–2018. Reddit polarized sharply around 2016 — but driven by newly arriving users sorting into partisan communities, not by existing users becoming more extreme. Strong support for the compositional sorting reading of the essay: scale enables sorting; sorting, not conversion, produces the aggregate shift.

Further (not downloaded):

Cinus et al. / Nithyanand et al. and the Reddit-toxicity-vs-size literature — several studies find larger subreddits sustain more toxic and more homogeneous discussion; worth a targeted search if the essay leans on online examples.
Reuters Institute (2018), Echo chambers, filter bubbles and polarisation: a literature review — https://reutersinstitute.politics.ox.ac.uk/echo-chambers-filter-bubbles-and-polarisation-literature-review — sober summary; the echo-chamber effect is real but smaller and rarer than popular accounts claim.

7. How each strand maps onto the essay + `influence.jl`

Essay claim	Best support	Caveat / opposing view
More people → finer sorting into like-minded groups	Bahns 2012/2017; McPherson 2001; Moody 2001	Sorting is at formation, not ongoing convergence (Bahns 2017)
Like-minded groups become more extreme	Sunstein 2002/2009; Schkade et al. 2007	Effect sizes vary; affective ≠ issue polarization (Iyengar 2019)
Bigger population → more distinct opinion clusters	Kou et al. 2012; HK 2002; Flache 2017	Pure sample size alone is negligible without fixed structure/heterogeneity
Mixing / exposure doesn’t fix it	Bail et al. 2018	Context-dependent; some deliberation designs do reduce extremity
Pattern is general (not just US)	Scipioni & Tintori 2021; Rodden 2019	Urban–rural is partly structural/economic, not choice-driven
Aggregate shift = sorting, not conversion	Waller & Anderson 2021; Baldassarri & Bearman 2007	—
Small/disconnected communities are more moderate but also less engaged	Oliver 2000/2001; Dahl & Tufte 1973	Homogeneous small towns disengage too — “peaceful” can mean “inert”

Model note. influence.jl already contains the crucial contrast: use_closest_neighbors = true (homophilous/similarity-biased influence → persistent clusters) vs. false (random mixing → consensus). That is precisely the Flache et al. (2017) distinction. The natural extension for the essay’s argument is to vary num_samples while holding num_closest_neighbors and the θ-kernel width fixed, and show the number of surviving clusters rising with population size — the Kou et al. (2012) result.

8. Gaps / suggested next searches

More “group size → less compromise / norm-enforcement” experiments beyond Centola et al. 2018 (§4) — especially work that varies absolute group size directly.
Minority group size and assimilation: Borjas on ethnic enclaves (larger co-ethnic community → slower linguistic/economic assimilation) — the clearest non-ideological analogue of the thesis; only summaries gathered so far.
LGBTQ / subculture “critical mass” literature — coming-out rates and identity consolidation as a function of local community size.
Firm / organization size and the formation of internal factions or “cliques.”
Whether any study operationalises the essay’s specific >1,000-person threshold (most campus work contrasts ~500 vs ~25,000; nothing found at fine resolution).