In a previous book review I described exclusive nightclubs as the particle colliders of sociology—places where you can reliably observe extreme forces collide. If so, military coups are the supernovae of sociology. They’re huge, rare, sudden events that, if studied carefully, provide deep insight about what lies underneath the veneer of normality around us.

That’s the conclusion I take away from Naunihal Singh’s book Seizing Power: the Strategic Logic of Military Coups. It’s not a conclusion that Singh himself draws: his book is careful and academic (though much more readable than most academic books). His analysis focuses on Ghana, a country which experienced ten coup attempts between 1966 and 1983 alone. Singh spent a year in Ghana carrying out hundreds of hours of interviews with people on both sides of these coups, which led him to formulate a new model of how coups work.

I’ll start by describing Singh’s model of coups. Then I’ll explain how the dynamics of his model also apply to everything else, with reference to Timur Kuran’s excellent book on preference falsification, Private Truths, Public Lies. In particular, I’ll explain threshold models of social behavior, which I find extremely insightful for understanding social dynamics.

Both of these books contain excellent sociological analyses. But they’re less useful as guides for how one should personally respond to the dynamics they describe. I think that’s because in sociology you’re always part of the system you’re trying to affect, so you can never take a fully objective, analytical stance towards it. Instead, acting effectively also requires the right emotional and philosophical stance. So to finish the post I’ll explore such a stance—specifically the philosophy of faith laid out by Soren Kierkegaard in his book Fear and Trembling.

The revolutionary’s handbook

What makes coups succeed or fail? Even if you haven’t thought much about this, you probably implicitly believe in one of two standard academic models of them. The first is coups as elections. In this model, people side with the coup if they’re sufficiently unhappy with the current regime—and if enough people side with the coup, then the revolutionaries will win. This model helps explain why popular uprisings like the Arab Spring can be so successful even when they start off with little military force on their side. The second is coups as battles. In this model, winning coups is about seizing key targets in order to co-opt the “nervous system” of the existing government. This model (whose key ideas are outlined in Luttwak’s influential book on coups) explains why coups depend so heavily on secrecy, and often succeed or fail based on their initial strikes.

Singh rejects both of these models, and puts forward a third: coups as coordination games. The core insight of this model is that, above all, military officers want to join the side that will win—both to ensure their and their troops’ survival, and to minimize unnecessary bloodshed overall. Given this, their own preferences about which side they’d prefer to win are less important than their expectations about which side other people will support. This explains why very unpopular dictators can still hold onto power for a long time (even though the coups as elections model predicts they’d quickly be deposed): because everyone expecting everyone else to side with the dictator is a stable equilibrium.

It also explains why the targets that revolutionaries focus on are often not ones with military importance (as predicted by the coups as battles model) but rather targets of symbolic importance, like parliaments and palaces—since holding them is a costly signal of strength. Another key type of target often seized by revolutionaries is broadcasting facilities, especially radio stations. Why? Under the coups as battles model, it’s so they can coordinate their forces (and disrupt the coordination of the existing regime’s forces). Meanwhile the coups as elections model suggests that revolutionaries should use broadcasts to persuade people that they’re better than the old regime. Instead, according to Singh, what we most often observe is revolutionaries publicly broadcasting claims that they’ve already won—or (when already having won is too implausible to be taken seriously) that their victory is inevitable.

It’s easy to see why, if you believed those claims, you’d side with the coup. But, crucially, such claims can succeed without actually persuading anyone! If you believe that others are gullible enough to fall for those claims, you should fall in line. Or if you believe that others believe that you will believe those claims, then they will fall in line and so you should too. In other words, coups are an incredibly unstable situation where everyone is trying to predict everyone else’s predictions about everyone else’s predictions about everyone else’s predictions about everyone else’s… about who will win. Once the balance starts tipping one way, it will quickly accelerate. And so each side’s key priority is making themselves the Schelling point for coordination via managing public information (i.e. information that everyone knows everyone else has) about what’s happening. (This can be formally modeled as a Keynesian beauty contest. Much more on this in follow-up posts.)

Singh calls the process of creating self-fulfilling common knowledge making a fact. I find this a very useful term, which also applies to more mundane situations—e.g. taking the lead in a social context can make a fact that you’re now in charge. Indeed, one of the most interesting parts of Singh’s book was a description of how coups can happen via managing the social dynamics of meetings of powerful people (e.g. all the generals in an army). People rarely want to be the first to defend a given side, especially in high-stakes situations. So if you start the meeting with a few people confidently expressing support for a coup, and then ask if anyone objects, the resulting silence can make the fact that everyone supports the coup. This strategy can succeed even if almost all the people in the meeting oppose the coup—if none of them dares to say so in the meeting, it’s very hard to rally them afterwards against what’s now become the common-knowledge default option.

One of Singh’s case studies hammers home how powerful meetings are for common knowledge creation. In 1978, essentially all the senior leaders in the Ghanaian military wanted to remove President Acheampong. However, they couldn’t create common knowledge of this, because it would be too suspicious for them to all meet without the President. Eventually Acheampong accidentally sealed his fate by sending a letter to a general criticizing the military command structure, which the general used as a pretext to call a series of meetings culminating in a bloodless coup in the President’s office.

Meetings are powerful not just because they get the key people in the same place, but also because they can be run quickly. The longer a coup takes, the less of a fait accompli it appears, and the more room there is for doubt to creep in. Singh ends the book with a fascinating case study of the 1991 coup attempt by Soviet generals against Gorbachev and Yeltsin. Even accounting for cherry-picking, it’s impressive how well this coup lines up with the “coups as coordination games” model. The conspirators included almost all of the senior members of the current government, and timed their strike for when both Gorbachev and Yeltsin were on vacation—but made the mistake of allowing Yeltsin to flee to the Russian parliament. From there he made a series of speeches asserting his moral legitimacy, while his allies spread rumors that the coup was falling apart. Despite having Yeltsin surrounded with overwhelming military force, bickering and distrust amongst the conspirators delayed their assault on the parliament long enough for them to become demoralized, at which point the coup essentially fizzled out.

Another of Singh’s most striking case studies was of a low-level Ghanaian soldier, Jerry Rawlings, who carried out a successful coup with less than a dozen armed troops. He was able to succeed in large part because the government had shown weakness by airing warnings about the threat Rawlings posed, and pleas not to cooperate with him. This may seem absurd, but Singh does a great job characterizing what it’s like to be a soldier confronted by revolutionaries in the fog of war, hearing all sorts of rumors that something big is happening, but with no real idea how many people are supporting the coup. In that situation, by far the easiest option is to stand aside, lest you find yourself standing alone against the new government. And the more people stand aside, the more snowballing social proof the revolutionaries have. So our takeaway from the Soviet coup attempt shouldn’t be that making a fact is inherently difficult—just that rank and firepower are no substitute for information control.

I don’t think of Singh as totally disproving the two other theories of coups—they probably all describe complementary dynamics. For example, if the Soviet generals had captured Yeltsin in their initial strike, he wouldn’t have had the chance to win the subsequent coordination game. And though Singh gives a lot of good historical analysis, he’s light on advance predictions. But Singh’s model is still powerful enough that it should constrain our expectations in many ways. For example, I’d predict based on Singh’s theory that radio will still be important for coups in developing countries, even now that it’s no longer the main news source for most people. The internet can convey much more information much more quickly, but radio is still better for creating common knowledge, in part because of its limitations (like having a fixed small number of channels). If you think of other predictions which help distinguish these three theories of coups, do let me know.

From explaining coups to explaining everything

Singh limits himself to explaining the dynamics of coups. But once he points them out, it’s easy to start seeing them everywhere. What if everything is a coordination game?

That’s essentially the thesis of Timur Kuran’s book Private Truths, Public Lies. Kuran argues that a big factor affecting which beliefs people express on basically all political topics is their desire to conform to the opinions expressed by others around them—a dynamic known as preference falsification. Preference falsification can allow positions to maintain dominance even as they become very unpopular. But it also creates a reservoir of pent-up energy that, when unleashed, can lead public opinion to change very rapidly—a process known as a preference cascade.

The most extreme preference cascades come during coups when common knowledge tips towards one side winning (as described above). But Kuran chronicles many other examples, most notably the history of race relations in America. In his telling, both the end of slavery and the end of segregation happened significantly after white American opinion had tipped against them—because people didn’t know that other people had also changed their minds. “According to one study [in the 70s], 18 percent of the whites favored segregation, but as many as 47 percent believed that most did so.” And so change, when it came, was very sudden: “In the span of a single decade, the 1960s, the United States traveled from government-supported discrimination against blacks to the prohibition of all color-based discrimination, and from there to government-promoted discrimination in favor of blacks.”

According to Kuran, this shift unfortunately wasn’t a reversion from preference falsification to honesty, but rather an overshot into a new regime of preference falsification. Writing in 1995, he claims that “white Americans are overwhelmingly opposed to special privileges for blacks. But they show extreme caution in expressing themselves publicly, for fear of being labeled as racists.” This fear has entrenched affirmative action ever more firmly over the decades since then, until the very recent and very sudden rise of MAGA.

Kuran’s other main examples are communism and the Indian caste system. His case studies are interesting, but the most valuable part of the book for me was his exposition of a formal model of preference falsification and preference cascades: threshold models of social behavior. For a thorough explanation of them, see this blog post by Eric Neyman (who calls visual representations of threshold models social behavior curves). Here I’ll just give an abbreviated introduction by stealing some of Eric’s graphs.

The basic idea is that threshold models describe how people’s willingness to do something depends on how many other people are doing it. Most people have some threshold at which they’ll change their public position, which is determined by a combination of their own personal preferences and the amount of pressure they feel to conform to others. For example, the graph below is a hypothetical social behavior curve of what percentage of people would wear facemasks in public, as a function of how many people they see already wearing masks. (The axis labels are a little confusing—you could also think of the x and y axes as “mask-wearers at current timestep” and “mask-wearers at next timestep” respectively.)

On this graph, if 35% of people currently wear masks, then once this fact becomes known around 50% of people would want to wear masks. This means that 35% of people wearing masks is not an equilibrium—if the number of mask-wearers starts at 35%, it will increase over time. More generally, whenever the percentage of people wearing a mask corresponds to a point on the social behavior curve above the y=x diagonal, then the number of mask-wearers will increase; when below y=x, it’ll decrease. So the equilibria are places where the curve intersects y=x. But only equilibria which cross from the left side to the right side are stable; those that go the other way are unstable (like a pen balanced on its tip), with any slight deviation sending them spiraling away towards the nearest stable equilibrium.

I recommend staring at the graph above until that last paragraph feels obvious.

I find the core insights of threshold models extremely valuable; I think of them as sociology’s analogue to supply and demand curves in economics. They give us simple models of moral panics, respectability cascades, echo chambers, the euphemism treadmill, and a multitude of other sociological phenomena—including coups.

We can model coups as an extreme case where the only stable equilibria are the ones where everyone supports one side or everyone supports the other, because the pressure to be on the winning side is so strong. This implies that coups have an s-shaped social behavior curve, with a very unstable equilibrium in the middle—something like the diagram below. The steepness of the curve around the unstable equilibrium reflects the fact that, once people figure out which side of the tipping point they’re on, support for that side snowballs very quickly.

This diagram illustrates that shifting the curve a few percent left or right has highly nonlinear effects. For most possible starting points, it won’t have any effect. But if we start off near an intersection, then even a small shift could totally change the final outcome. You can see an illustration of this possibility (again from Eric’s blog post) below—it models a persuasive argument which makes people willing to support something with 5 percentage points less social proof, thereby shifting the equilibrium a long way. The historical record tells us that courageous individuals defying social consensus can work in practice, but now it works in theory too.

Having said all that, I don’t want to oversell threshold models. They’re still very simple, which means that they miss some important factors:

They only model a binary choice between supporting and opposing something, whereas most people are noncommittal on most issues by default (especially in high-stakes situations like coups). But adding in this third option makes the math much more complicated—e.g. it introduces the possibility of cycles, meaning there might not be any equilibria.
Realistically, support and opposition aren’t limited to discrete values, but can range continuously from weak to strong. So perhaps we should think of social behavior curves in terms of average level of support rather than number of supporters.
Threshold models are memoryless: the next timestep depends only on the current timestep. This means that they can’t describe, for example, the momentum that builds up after behavior consistently shifts in one direction.
Threshold models treat all people symmetrically. By contrast, belief propagation models track how preferences cascade through a network of people, where each person is primarily responding to local social incentives. Such models are more realistic than simple threshold models.

I’d be very interested to hear about extensions to threshold models which avoid these limitations.

From explaining everything to influencing everything

How should understanding the prevalence of preference falsification change our behavior? Most straightforwardly, it should predispose us to express our true beliefs more even in controversial cases—because there might be far more people who agree with us than it appears. And as described above, threshold models give an intuition for how even a small change in people’s willingness to express a view can trigger big shifts.

However, there’s also a way in which threshold models can easily be misleading. In the diagram above, we modeled persuasion as an act of shifting the curve. But the most important aspect of persuasion is often not your argument itself, but rather the social proof you provide by defending a conclusion. And so in many cases it’s more realistic to think of your argument, not as translating the entire curve, but as merely increasing the number of advocates for X by one.

There’s a more general point here. It’s tempting to think that you can estimate the social behavior curve, then decide how you’ll act based on that. But everyone else’s choices are based on their predictions of you, and you’re constantly leaking information about your decision-making process. So you can’t generate credences about how others will decide, then use them to make your decision, because your eventual decision is heavily correlated with other people’s decisions. You’re not just intervening on the curve, you are the curve.

More precisely, social behavior is a domain where the correlations between people’s decisions are strong enough to make causal decision theory misleading. Instead it’s necessary to use either evidential decision theory or functional decision theory. Both of these track the non-causal dependencies between your decision and other people’s decisions. In particular, both of them involve a step where you reason “if I do something, then it’s more likely that others will do the same thing”—even when they have no way of finding out about your final decision before making theirs. So you’re not searching for a decision which causes good things to happen; instead you’re searching for a desirable fixed point for simultaneous correlated decisions by many people.

I’ve put this in cold, rational language. But what we’re talking about is nothing less than a leap of faith. Imagine sitting at home, trying to decide whether to join a coup to depose a hated ruler. Imagine that if enough of you show up on the streets at once, loudly and confidently, then you’ll succeed—but that if there are only a few of you, or you seem scared or uncertain, then the regime won’t be cowed, and will arrest or kill all of you. Imagine your fate depending on something you can’t control at all except via the fact that if you have faith, others are more likely to have faith too. It’s a terrifying, gut-wrenching feeling.

Perhaps the most eloquent depiction of this feeling comes from Soren Kierkegaard in his book Fear and Trembling. Kierkegaard is moved beyond words by the story of Abraham, who is not only willing to sacrifice his only son on God’s command—but somehow, even as he’s doing it, still believes against all reason that everything will turn out alright. Kierkegaard struggles to describe this level of pure faith as anything but absurd. Yet it’s this absurdity that is at the heart of social coordination—because you can never fully reason through what happens when other people predict your predictions of their predictions of… To cut through that, you need to simply decide, and hope that your decision will somehow change everyone else’s decision. You walk out your door to possible death because you believe, absurdly, that doing so will make other people simultaneously walk out of the doors of their houses all across the city.

A modern near-synonym for “leap of faith” is “hyperstition”: an idea that you bring about by believing in it. This is Nick Land’s term, which he seems to use primarily for larger-scale memeplexes—like capitalism, the ideology of progress, or AGI. Deciding whether or not to believe in these hyperstitions has some similarity to deciding whether or not to join a coup, but the former are much harder to reason about by virtue of their scale. We can think of hyperstitions as forming the background landscape of psychosocial reality: the commanding heights of ideology, the shifting sands of public opinion, and the moral mountain off which we may—or may not—take a leap into the sea of faith.

Becoming a knight of faith

Unfortunately, the mere realization that social reality is composed of hyperstitions doesn’t give you social superpowers, any more than knowing Newtonian mechanics makes you a world-class baseball player. So how can you decide when and how to actually swing for the fences? I’ll describe the tension between having too much and too little faith by contrasting three archetypes: the pragmatist, the knight of resignation, and the knight of faith.

The pragmatist treats faith as a decision like any other. They figure out the expected value of having faith—i.e. of adopting an “irrationally” strong belief—and go for it if and only if it seems valuable enough. Doing that analysis is difficult: it requires the ability to identify big opportunities, judge people’s expectations, and know how your beliefs affect common knowledge. In other words, it requires skill at politics, which I’ll talk about much more in a follow-up post.

But while pragmatic political skill can get you a long way, it eventually hits a ceiling—because the world is watching not just what you do but also your reasons for doing it. If your choice is a pragmatic one, others will be able to tell—from your gait, your expression, your voice, your phrasing, and of course how your position evolves over time. They’ll know that you’re the sort of person who will change your mind if the cost/benefit calculus changes. And so they’ll know that they won’t truly be able to rely on you—that you don’t have sincere faith.

Imagine, by contrast, someone capable of fighting for a cause no matter how many others support them, no matter how hopeless it seems. Even if such a person never actually needs to fight alone, the common knowledge that they would makes them a nail in the fabric of social reality. They anchor the social behavior curve not merely by adding one more supporter to their side, but by being an immutable fixed point around which everyone knows (that everyone knows that everyone knows…) that they must navigate.

The archetype that Kierkegaard calls the knight of resignation achieves this by being resigned to the worst-case outcome. They gather the requisite courage by suppressing their hope, by convincing themselves that they have nothing to lose. They walk out their door having accepted death, with a kind of weaponized despair.

The grim determination of the knight of resignation is more reliable than pragmatism. But if you won’t let yourself think about the possibility of success, it’s very difficult to reason well about how it can be achieved, or to inspire others to pursue it. So what makes Kierkegaard fear and tremble is not the knight of resignation, but the knight of faith—the person who looks at the worst-case scenario directly, and (like the knight of resignation) sees no causal mechanism by which his faith will save him, but (like Abraham) believes that he will be saved anyway. That’s the kind of person who could found a movement, or a country, or a religion. It's Washington stepping down from the presidency after two terms, and Churchill holding out against Nazi Germany, and Gandhi committing to non-violence, and Navalny returning to Russia—each one making themselves a beacon that others can’t help but feel inspired by.

What’s the difference between being a knight of faith, and simply falling into wishful thinking or delusion? How can we avoid having faith in the wrong things, when the whole point of faith is that we haven’t pragmatically reasoned our way into it? Kierkegaard has no good answer for this—he seems to be falling back on the idea that if there’s anything worth having faith in, it’s God. But from the modern atheist perspective, we have no such surety, and even Abraham seems like he’s making a mistake. So on what basis should we decide when to have faith?

I don’t think there’s any simple recipe for making such a decision. But it’s closely related to the difference between positive motivations (like love or excitement) and negative motivations (like fear or despair). Ultimately I think of faith as a coordination mechanism grounded in values that are shared across many people, like moral principles or group identities. When you act out of positive motivation towards those values, others will be able to recognize the parts of you that also arise in them, which then become a Schelling point for coordination. That’s much harder when you act out of pragmatic interests that few others share—especially personal fear. (If you act out of fear for your group’s interests, then others may still recognize themselves in you—but you’ll also create a neurotic and self-destructive movement.)

I talk at length about how to replace negative motivation with positive motivation in this series of posts. Of course, it’s much easier said than done. Negative motivations are titanic psychological forces which steer most decisions most people make. But replacing them is worth the effort, because it unlocks a deep integrity—the ability to cooperate with different parts of yourself all the way down, without relying on deception or coercion. And that in turn allows you to cooperate with copies of those parts that live in other people—to act as as part of a distributed agent held together by a shared sense of justice or fairness or goodness. That’s how we can tame the drive towards preference falsification, and steer our desire for collective identities towards those which promote the flourishing of the individuals within them.

Postscript: six months after publishing this post, I feel hesitant about how much I focused on the example of popular revolt (and have edited the last sentence accordingly). Marching in the streets against an oppressive government can be admirable, but it’s also very mob-like. In particular, it doesn’t ensure that the next regime will actually be better (as many Iranian revolutionaries learned at great cost). I’m now more focused specifically on faith that’s oriented towards developing a just, well-governed coalition. I’m not yet quite sure what I mean by that, but I plan to explore that idea further in forthcoming posts.