# Ultrafilters, Ultraproducts, and Hypernaturals 1: Introduction

This is the series I wish would have existed a month ago when I first started learning about ultrafilters and ultraproducts. First of all, what’s the motivation for learning about these ultra-objects? I imagine that if you’re here, you probably already have some degree of interest in ultrafilters, ultraproducts, or nonstandard models of arithmetic. But I’ll see if I can bolster that interest a little bit.

The most exciting application to me personally is that ultraproducts give you a recipe for constructing new mathematical structures out of familiar ones. Out of some model M one can construct a a new model *M, which typically has a much richer and stranger structure, but is nonetheless elementarily equivalent to M (this is called Łoś’s theorem). Elementary equivalence means that all the expressive resources of first-order logic are insufficient to tell M and *M apart: they agree on all first-order sentences.

For example, the ultraproduct of ℝ (the real numbers) is *ℝ, the hyperreal numbers. The hyperreals contain an enormous supply of infinitesimal quantities clustered around every real, as well as infinitely large quantities. And all the usual operations on ℝ, like addition and multiplication, are already nicely defined in *ℝ, meaning that these infinitesimals and infinities have a well-defined algebraic structure. And Łoś’s theorem tells us that ℝ and *ℝ are elementary equivalent: any first-order sentence true of the reals is also true of the hyperreals.

Another example: the ultraproduct of ℕ (the natural numbers) is *ℕ, the hypernaturals. The hypernaturals don’t contain any infinitesimals, but they do contain an uncountable infinity of infinite numbers. And since *N and N are elementarily equivalent, *ℕ is a model of true arithmetic! This is super exciting to me. True arithmetic is this unimaginably complicated set of sentences; there’s no Turing machine that decides which sentences reside in true arithmetic, nor is there a Turing machine with a halting oracle that does the job, nor even a Turing machine with a halting oracle for Turing machines with halting oracles! The computational complexity of true arithmetic is the limit of this sequence of progressively harder halting problems: you can only decide the set if you have an oracle for the halting problem, plus an oracle for the halting problem for TMs with oracles for the halting problem, plus an oracle for the halting problem for TMs with oracles for the halting problem for TMs with oracles for the halting problem, and so on. This level of complexity is known as 0(ω).

So true arithmetic is this impossibly uncomputable set of sentences. We know trivially that ℕ is a model of true arithmetic, because that’s how true arithmetic is defined { φ | ℕ ⊨ φ }. And we also know that true arithmetic has nonstandard models, because it’s a first-order theory and no first order theory can categorically define ℕ (I think the easiest way to see why this is true is to apply the Löwenheim-Skolem theorem, but – self-promotion alert – there’s also this very nice and simple proof from first principles). And ultraproducts allow us to construct an explicit example of one of these nonstandard models! You can actually write down some of these nonstandard numbers (they look like infinite sequences of natural numbers) and discover their strange properties. For example, (2, 3, 5, 7, 11, …) is a nonstandard prime number, (1, 2, 4, 8, 16, …) is infinitely even, and (0!, 1!, 2!, 3!, 4!, …) is a multiple of every standard natural number. We’ll dive into all of this soon enough.

Ultrafilters and ultraproducts also have applications outside of logic. A fairly basic result about ultrafilters (that every ultrafilter over a finite set contains a singleton) is equivalent to Arrow’s impossibility theorem in voting theory (that any voting system with unanimity, independence of irrelevant alternatives (IIR), and finitely many voters contains a dictator). And the existence of a singleton-free ultrafilter over an infinite set (a free ultrafilter) shows that with infinitely many voters, there’s a non-dictatorial voting system with unanimity and IIR. There’s a pretty good description of those results here, but finish reading my post first!

Nick Bostrom in an old paper titled Infinite Ethics describes how to apply ultrafilters to resolve some of the issues that arise when trying to apply utilitarian ethics in a universe with infinitely many experiencers. Suppose that the current universe contains an infinite number of experiencers, each of whom is having an experience with a valence of +1. By pressing a button, you can immediately transition to a universe where everybody is now experiencing a valence of +2. Clearly pressing the button is a utilitarian good. But 1+1+1+… = ∞ = 2+2+2+…. So it looks like a standard account of utility as real numbers says that the utility of the +2 world is the same as the utility of the +1 world (both just ∞). But if we treat utilities as hyperreal numbers instead of ordinary real numbers, then we can do the comparison and get the right answer. It’s worth noting that this approach doesn’t fix all the problems raised by an infinite universe; for instance, if pressing the button only increases a finite number of experiencers from valence +1 to valence +2, then the net utility of the universe is unchanged, even with hyperreal utilities. (This corresponds to a property that we’ll see soon, which is that a free ultrafilter contains no finite sets.)

Okay, introduction over. Hopefully you’re now excitedly asking yourself the question “So what are these ultrafilters and ultraproducts anyway??” The story begins in the next post!

Hypernaturals Simplified

# What precedent does the Trump Twitter ban set?

I don’t know if anybody needs me to tell them this, but Trump has recently been permanently banned from Twitter, following a violent attack on the capitol inspired by his conduct since his election loss. This decision by Twitter has inspired a lot of discussion about free speech and censorship, and I want to chime in with one small point about a common argument you hear in these discussions.

A lot of the anti-ban discussion that I’ve seen relates to fears about setting a bad precedent for future decisions by tech companies. I think that these are legitimate concerns, but that they are often stated over-confidently. When trying to judge the precedent that an action sets, there’s a type of scope ambiguity that arises. What exactly is the precedent that Twitter’s ban of Trump sets? Is it that any time social media authorities find somebody’s ideas repugnant enough they will ban them? That’s probably too broad, but somebody that thinks that this is the precedent will find it overly subjective and judge it to be overreach. Is the precedent that Twitter will ban the President of the United States if his conduct leads to an assault on the Capitol? That’s probably too narrow, but might be a standard we find acceptable.

Sometimes there’s less ambiguity, like when the action fits nicely within a predetermined paradigm for precedent-breaking (e.g., perhaps making a new amendment to the Constitution), and other times there’s more ambiguity. Sometimes actions come with a detailed description of why they’re being taken, and this can be some guide to what precedent we consider the action to be setting. But other times they don’t, and even in those cases that they do we might have reasons to distrust the description, or to think the precedent being set is actually different from the description.

I guess we could say: there’s the intent in the mind of the Twitter authorities, and there’s their actual statement of the reasons for the action. There’s the explicit policy they are citing for their action (in other cases, they might be creating a new policy), and there’s the way that that action is understood among the general public. What we really care about is the likely future consequences of the act, and all of these things are relevant to judging this to one degree or another. Framing it this way, it’s a very complicated prediction about the world that one makes when they say that the Trump Twitter ban is setting a bad precedent: it’s saying that the future actions of Twitter and other tech companies are worse than they would have been in the counterfactual world where they had held off on the ban. This is a claim I have a lot of uncertainty about, and I think probably most other people should as well.

# In defense of collateralized debt obligations (CDOs)

If you’ve watched some of the popular movies out there about the 2008 financial crisis, chances are that you’ve been misled about one or two things. (I’m looking at you, Big Short.) For example:

Entertaining? Sure! Accurate? No, very much not so. This analogy is very much off the mark, as you’ll see in a minute.

Here’s a quote from Inside Job, often described as the most rigorous and well-researched of the popular movies on the crisis:

In the early 2000s, there was a huge increase in the riskiest loans, called subprime. But when thousands of subprime loans were combined to create CDOs, many of them still received AAA ratings.

The tone that this is stated in is one of disbelief at the idea that by combining subprime loans you can create extremely safe loans. And maybe this idea does sound pretty crazy if you haven’t studied much finance! But it’s actually correct. You can, by combining subprime loans, generate enormously safe investments, and thus the central conceit of a CDO is actually entirely feasible.

The overall attitude taken by many of these movies is that the financial industry in the early 2000s devoted itself to the scamming of investors for short-term profits through creation of complicated financial instruments like CDOs. As these movies describe, the premise of a CDO is that by combining a bunch of risky loans and slicing-and-dicing them a bit, you can produce a mixture of new investment opportunities including many that are extremely safe. This is all described in a tone that is supposed to convey a sense that this premise is self-evidently absurd.

I want to convince you that the premise of CDOs is not self-evidently absurd, and that in fact it is totally possible to pool risky mortgages to generate extremely safe investments.

So, why think that it should be possible to pool risky investments and decrease overall risk? Well first of all, that’s just what happens when you pool assets! Risk always decreases when you pool assets, with the only exception being the case where the assets are all perfectly correlated (which never happens in real life anyway).

As an example, imagine that we have two independent and identical bets, each giving a 90% chance of a \$1000 return and a 10% chance of nothing.

Now put these two together, and split the pool into two new bets, each an average of the original two:

Take a look at what we’ve obtained. Now we have only a 1% chance of getting nothing (because both bets have to fail for this to happen). We do, however, have only a 81% chance of getting \$1000, as opposed to the 90% we had earlier. But what about risk? Are we higher or lower risk than before?

The usual way of measuring risk is to look at standard deviations. So what are the standard deviations of the original bet and the new one?

Initial Bet
Mean = 90% (\$1000) + 10% (\$0) = \$900
Variance = 90% (100^2) + 10% (900^2) = 90,000
Standard deviation = \$300

New Bet
Mean = 81% (\$1000) + 18% (\$500) + 10% (\$0) = \$900
Variance = 81% (100^2) + 18% (400^2) + 1% (900^2) = 45,000
Standard deviation = \$216.13

And look at what we see: risk has dropped, and fairly dramatically so, just by pooling independent bets! This concept is one of the core lessons of financial theory, and it goes by the name of diversification. The more bets we pool, the further the risk goes down, and in the limit of infinite independent bets, the risk goes to zero.

So if you’re watching a movie and somebody says something about combining risky assets to produce safe assets as if that idea is self-evidently absurd, you know that they have no understanding of basic financial concepts, and especially not a complex financial instrument like a CDO.

In fact, let’s move on to CDOs now. The setup I described above of simply pooling bets does decrease risk, but it’s not how CDOs work. At least, not entirely. CDOs still take advantage of diversification, but they also incorporate an additional clever trick to distribute risk.

The idea is that you take a pool of risky assets, and you create from it a new pool of non-identical assets with a spectrum of risk profiles. Previously all of the assets that we generated were identical to each other, but what we’ll do now with the CDO is that we’ll split up our assets into non-identical assets in such a way as to allocate the risk, so that some of the assets that we get will have very high risk (they’ll have more of the risk allocated to them), and some of them will have very little risk.

Alright, so that’s the idea: from a pool of equally risky assets, you can get a new pool of assets that have some variation in riskiness. Some of them are actually very safe, and some of them are very, very risky.  How do we do this? Well let’s go back to our starting example where we had two identical bets, each with 90% chance of paying out \$1000, and put them together in a pool. But this time, instead of creating two new identical bets, we are going to differentiate the two bets by placing an order priority of payout on them. In other words, one bet will be called the “senior tranche”, and will be paid first. And the other bet will be called the “junior tranche”, and will be paid only if there is still money left over after the senior tranche has been paid. What do the payouts for these two new bets look like?

The senior tranche gets paid as long as at least one of the two bets pays out, which happens with 99% probability. Remember, we started with only a 90% probability of paying out. This is a dramatic change! In terms of standard deviation, this is \$99.49, less than a third of what we started with!

And what about the junior tranche? Its probability of getting paid is just the probability that both people don’t default, which is 81%. And its risk has gone up, with a standard deviation of \$392.30. So essentially, all we’ve done is split up our risk. We originally had 90%/90%, and now we have 99%/81%. In the process, what we’ve done is we’ve created a very very safe bet and a very very risky bet.

Standard Deviations
Original: \$300
Simple pooling: \$216.13
CDO senior tranche: \$99.49
CDO junior tranche: \$392.30

The important thing is that these two bets have to both be sold. You can’t just sell the senior tranche to people who want safe things (pension funds), you have to also sell the junior tranche. So how do you do that? Well, you just lower its price! A higher rate of return awaits the taker of the junior tranche in exchange for taking on more risk.

Now if you think about it, this new lower level risk we’ve obtained, this 1% chance of defaulting that we got out of two bets that had a 10% chance of defaulting each, that’s a real thing! There really is a 1% chance that both bets default if they are independent, and so the senior tranche really can expect to get paid 99% of the time! There isn’t a lie or a con here, a pension funds that gets sold these senior tranches of CDOs is actually getting a safe bet! It’s just a clever way of divvying up risk among two assets.

I think the idea of a CDO is cool enough by itself, but I think that the especially cool thing about CDOs is that they open up the market to new customers. Previously, if you wanted to get a mortgage, then you had to find basically a bank that was willing to accept your level of risk, whatever it happens to be. And it could be that if you’re too high risk, then nobody wants to give you a mortgage, and you’d just be out of luck. Even prior to CDOs, when you had mortgage pooling but no payment priority, you have to have investors that are interested in the level of risk of your pool. The novelty of CDOS is in allowing you to alter the risk profile of your pool of mortgages at will.

:Let’s say that you have 100 risky loans, and there’s only enough demand for you to sell 50 of them. What you can do is create a CDO with 50 safe loans and 50 risky loans. Now you get to not only sell your risky loans, but you can also sell your safe loans to interested customers like pension funds! This is the primary benefit of the new financial technology of CDOs: it allows banks to generate tailor-made risk levels for the set of investors that are interested in buying, so that they can sell more mortgage-backed securities and get more people homes. And if everything is done exactly as I described it, then everything should work out fine.

But of course, things weren’t done exactly as I described them. The risk levels of individual mortgages were given increasingly optimistic ratings with stated-income loans, no-down-payment loans, and no-income no-asset loans. CDOs were complex and their risk level was often difficult to assess, resulting in less transparency and more ability for banks to over-report their safety. And crucially, the different tranches of any given CDO are highly dependent on each other, even after they’ve been sold to investors that have nothing to do with each other.

Let’s go back to our simple example of the two \$1000 bets for illustration. Suppose that one of the two bets doesn’t pay out (which could correspond to one home-owner defaulting on their monthly payment). Now the senior tranche owner’s payment is entirely dependent on how the other bet performs. The senior tranche owner will get \$1000 only if that remaining bet pays out, which happens with just 90% probability. So his chance of getting \$1000 has dropped from 99% to 90%.

That’s a simple example of a more general point: that in a CDO, once the riskier tranches fail, the originally safe tranches suddenly become a lot riskier (so that what was originally AA is now maybe BBB). This helps to explain why once the housing bubble had popped, all levels of CDOs began losing value, not just the junior levels. Ordinary mortgage backed securities don’t behave this way! A AA-rated mortgage is rated that way because of some actual underlying fact about the reliability of the homeowner, which doesn’t necessarily change when less reliable homeowners start defaulting. A AA-rated CDO tranche might be rated that way entirely because it has payment priority, even though all the mortgages in its pool are risky.

Another way to say this: An ordinary mortgage backed security decreased risk just because of diversification (many mortgages pooled together make for a less risky bet than a single mortgage). But a CDO gets decreased risk because of both diversification and (in the upper tranches) the order priority (getting paid first). In both cases, as some of the mortgages in the pool fail, you lose some of the diversification benefit. But in the CDO case, you also lose the order priority benefit in the upper tranches (because, for example, if it takes 75 defaults in your pool for you to lose your money and 50 have already failed, then you are at a much higher risk of losing your money than if none of them have failed). Thus there is more loss of value in safe CDOs than in safe MBSs as default rates rise.

# Why do prediction markets work?

Is there a paradox in the continued existence of prediction markets? Recently I’ve been wondering this. Let me start with a little background for those that are unfamiliar with the concept of prediction markets.

Prediction markets are markets that allow you to bet on the outcomes of real-life events. This gives financial incentives to predict accurately, and as such the market price of a given bet reflects a kind of aggregate credence for that event occurring. There’s a whole bunch of results, theoretical and applied, that indicate that prediction markets serve to give robustly accurate probability estimates for real-world events.

Here’s a great paper by Robin Hanson about a political system based on prediction markets, named futarchy. Essentially, the idea is that voters determine a nation’s values, so as to generate some average national welfare metric, and then betting markets are used to decide policy. Some quotes:

On info-failures as a primary problem for democracy

According to many experts in economics and development, governments often choose policies that are “inefficient” in the sense that most everyone could expect to gain from other feasible policies. Many other kinds of experts also see existing policies as often clearly inferior to known alternatives.

If inferior policies would not have been adopted had most everyone known they are inferior, and if someone somewhere knew or could have learned that they are inferior, then we can blame inferior policies on a failure of our “info” institutions. By “info” here I just mean clues and analysis that should change our beliefs. Our info institutions are those within which we induce, express, and evaluate the acquiring and sharing of info. They include public relations teams, organized interest groups, news media, conversation forums, think tanks, universities, journals, elite committees, and state agencies. Inferior policies happen because our info institutions fail to induce people to acquire and share relevant info with properly-motivated decision makers.

[…]

Where might we find better info institutions? According to most experts in economics and finance, speculative markets are exemplary info institutions. That is, active speculative markets do very well at inducing people to acquire info, share it via trades, and collect that info into consensus prices that persuade wider audiences. This great success suggests that we should consider augmenting our political info institutions with speculative market institutions. That is, perhaps we should encourage people to create, trade in, and heed policy-relevant speculative markets, instead of discouraging such markets as we do today via anti-gambling laws.

Laying out the proposal

In futarchy, democracy would continue to say what we want, but betting markets would now say how to get it. That is, elected representatives would formally define and manage an after-the-fact measurement of national welfare, while market speculators would say which policies they expect to raise national welfare. The basic rule of government would be:

• When a betting market clearly estimates that a proposed policy would increase expected national welfare, that proposal becomes law.

Futarchy is intended to be ideologically neutral; it could result in anything from an extreme socialism to an extreme minarchy, depending on what voters say they want, and on what speculators think would get it for them.

Futarchy seems promising if we accept the following three assumptions:

• Democracies fail largely by not aggregating available information.
• It is not that hard to tell rich happy nations from poor miserable ones.
• Betting markets are our best known institution for aggregating information.

On the success of prediction markets

Betting markets, and speculative markets more generally, seem to do very well at aggregating information. To have a say in a speculative market, you have to “put your money where your mouth is.” Those who know they are not relevant experts shut up, and those who do not know this eventually lose their money, and then shut up. Speculative markets in essence offer to pay anyone who sees a bias in current market prices to come and correct that bias.

Speculative market estimates are not perfect. There seems to be a long-shot bias when there are high transaction costs, and perhaps also excess volatility in long term aggregate price movements. But such markets seem to do very well when compared to other institutions. For example, racetrack market odds improve on the predictions of racetrack experts, Florida orange juice commodity futures improve on government weather forecasts, betting markets beat opinion polls at predicting U.S. election results, and betting markets consistently beat Hewlett Packard official forecasts at predicting Hewlett Packard printer sales. In general, it is hard to find information that is not embodied in market prices.

On the possibility of manipulation of prediction markets

What do noise traders have to do with manipulators? Manipulators, who trade hoping to distort prices, are noise traders, since they trade for reasons other than asset value info. Thus adding manipulators to speculative markets doesn’t reduce average price accuracy. This has been verified in theory, in laboratory experiments, and in the field.

Futarchy remains for me one of the coolest and most exciting ideas I’ve heard in political philosophy, and prediction markets fascinate me. But for today, I have the following question about their feasibility:

If the only individuals that are able to consistently profit off the prediction market are the best predictors, then why wouldn’t the bottom 50% of predictors continuously drop out as they lose money on the market? If so, then as the population of market participants dwindles you would end up with a small fraction of really good predictors, each of whom sometimes gets lucky and makes money and sometimes is unlucky and loses some. On average, these people won’t be able to make money any more (as the ability to make money relies on the participation of inferior predictors in the market), so they’ll drop out as well.

If this line of reasoning is right, then it seems like prediction markets should inevitably collapse as their user base drops out. Why, then, do sites like PredictIt keep functioning?

One possibility is that there’s something wrong with the argument. This is honestly where most of my credence lies; tons of smart people endorse the idea, and this seems like a fairly obviously central flaw in the concept for them all to miss. If this argument isn’t wrong, though, then we have an interesting phenomenon to explain.

One explanation that came to my mind is that the continued survival of prediction markets is only possible because of a bug in human psychology, namely, a lack of epistemic humility. People are on average overly confident in their beliefs, and so uninformed people will continue confidently betting on propositions, even when they are generally betting against individuals with greater expertise.

Is this really what’s going on? I’m not sure. I would be surprised if humans were actually overconfident enough to continue betting on a market that they are consistently losing money on. Maybe they’d find some way to rationalize dropping out of the market that doesn’t amount to them admitting “My opinion is not worth as much as I thought it was”, but surely they would eventually stop betting after enough losses (putting aside whatever impulses drive people to gamble on guaranteed negative-expected-value games until they lose all their money.) On the other hand, it could be that the traffic of less-informed individuals does not consist of the same individuals betting over and over, and instead a constant crowd of new sheep coming in to be exploited by those more knowledgeable. What do you think? How do you explain this?

I recently came across a whole bunch of crazy historical trivia, involving the laws around adultery, BDSM, and divorce. Here are some of the quotes that made me gasp (mostly from Wikipedia):

As of 2019, adultery remains a criminal offense in 19 states, but prosecutions are rare. Although adultery laws are mostly found in the conservative states (especially Southern states), there are some notable exceptions such as New York, Idaho, Oklahoma, Michigan, and Wisconsin consider adultery a felony, while in the other states it is a misdemeanor.

Penalties vary from a \$10 fine (Maryland) to four years in prison (Michigan). In South Carolina, the fine for adultery is up to \$500 and/or imprisonment for no more than one year (South Carolina code 16-15-60), and South Carolina divorce laws deny alimony to the adulterous spouse.

In Florida adultery (“Living in open adultery”, Art 798.01) is illegal; while cohabitation of unmarried couples was decriminalized in 2016.

Under South Carolina law adultery involves either “the living together and carnal intercourse with each other” or, if those involved do not live together “habitual carnal intercourse with each other” which is more difficult to prove.

In Alabama “A person commits adultery when he engages in sexual intercourse with another person who is not his spouse and lives in cohabitation with that other person when he or that other person is married.”

In some Native American cultures, severe penalties could be imposed on an adulterous wife by her husband. In many instances she was made to endure a bodily mutilation which would, in the mind of the aggrieved husband, prevent her from ever being a temptation to other men again. Among the Aztecs, wives caught in adultery were occasionally impaled, although the more usual punishment was to be stoned to death.

The Code of Hammurabi, a well-preserved Babylonian law code of ancient Mesopotamia, dating back to about 1772 BC, provided drowning as punishment for adultery.

Amputation of the nose – rhinotomy – was a punishment for adultery among many civilizations, including ancient India, ancient Egypt, among Greeks and Romans, and in Byzantium and among the Arabs.

In England and its successor states, it has been high treason to engage in adultery with the King’s wife, his eldest son’s wife and his eldest unmarried daughter. The jurist Sir William Blackstone writes that “the plain intention of this law is to guard the Blood Royal from any suspicion of bastardy, whereby the succession to the Crown might be rendered dubious.”

Adultery was a serious issue when it came to succession to the crown. Philip IV of France had all three of his daughters-in-law imprisoned, two (Margaret of Burgundy and Blanche of Burgundy) on the grounds of adultery and the third (Joan of Burgundy) for being aware of their adulterous behaviour. The two brothers accused of being lovers of the king’s daughters-in-law were executed immediately after being arrested.

Until 2018, in Indian law, adultery was defined as sex between a man and a woman without the consent of the woman’s husband. The man was prosecutable and could be sentenced for up to five years (even if he himself was unmarried) whereas the married woman cannot be jailed.

In Southwest Asia, adultery has attracted severe sanctions, including death penalty. In some places, such as Saudi Arabia, the method of punishment for adultery is stoning to death. Proving adultery under Muslim law can be a very difficult task as it requires the accuser to produce four eyewitnesses to the act of sexual intercourse, each of whom should have a good reputation for truthfulness and honesty. The criminal standards do not apply in the application of social and family consequences of adultery, where the standards of proof are not as exacting.

Adultery is no longer a crime in any European country. Among the last Western European countries to repeal their laws were Italy (1969), Malta (1973), Luxembourg (1974), France (1975), Spain (1978), Portugal (1982), Greece (1983), Belgium (1987), Switzerland (1989), and Austria (1997).

In most Communist countries adultery was not a crime. Romania was an exception, where adultery was a crime until 2006, though the crime of adultery had a narrow definition, excluding situations where the other spouse encouraged the act or when the act happened at a time the couple was living separate and apart; and in practice prosecutions were extremely rare.

English common law defined the crime of seduction as a felony committed “when a male person induced an unmarried female of previously chaste character to engage in an act of sexual intercourse on a promise of marriage.” A father had the right to maintain an action for the seduction of his daughter (or the enticement of a son who left home), since this deprived him of services or earnings.

In more modern times, Frank Sinatra was charged in New Jersey in 1938 with seduction, having enticed a woman “of good repute to engage in sexual intercourse with him upon his promise of marriage. The charges were dropped when it was discovered that the woman was already married.”

Buddhist Pali texts narrate legends where the Buddha explains the karmic consequences of adultery. For example, states Robert Goldman, one such story is of Thera Soreyya. Buddha states in the Soreyya story that “men who commit adultery suffer hell for hundreds of thousands of years after rebirth, then are reborn a hundred successive times as women on earth, must earn merit by “utter devotion to their husbands” in these lives, before they can be reborn again as men to pursue a monastic life and liberation from samsara.

According to Muhammad, an unmarried person who commits adultery or fornication is punished by flogging 100 times; a married person will then be stoned to death. A survey conducted by the Pew Research Center found support for stoning as a punishment for adultery mostly in Arab countries; it was supported in Egypt (82% of respondents in favor of the punishment) and Jordan (70% in favor), as well as Pakistan (82% favor), whereas in Nigeria (56% in favor) and in Indonesia (42% in favor) opinion is more divided, perhaps due to diverging traditions and differing interpretations of Sharia.

The Roman Lex Julia, Lex Iulia de Adulteriis Coercendis (17 BC), punished adultery with banishment. The two guilty parties were sent to different islands (“dummodo in diversas insulas relegentur”), and part of their property was confiscated. Fathers were permitted to kill daughters and their partners in adultery. Husbands could kill the partners under certain circumstances and were required to divorce adulterous wives.

Durex’s Global Sex Survey found that worldwide 22% of people surveyed admitted to have had extramarital sex. In the United States Alfred Kinsey found in his studies that 50% of males and 26% of females had extramarital sex at least once during their lifetime. Depending on studies, it was estimated that 26–50% of men and 21–38% of women, or 22.7% of men and 11.6% of women, had extramarital sex. Other authors say that between 20% and 25% of Americans had sex with someone other than their spouse. Three 1990s studies in the United States, using nationally representative samples, have found that about 10–15% of women and 20–25% of men admitted to having engaged in extramarital sex.

The Standard Cross-Cultural Sample described the occurrence of extramarital sex by gender in over 50 pre-industrial cultures. The occurrence of extramarital sex by men is described as “universal” in 6 cultures, “moderate” in 29 cultures, “occasional” in 6 cultures, and “uncommon” in 10 cultures. The occurrence of extramarital sex by women is described as “universal” in 6 cultures, “moderate” in 23 cultures, “occasional” in 9 cultures, and “uncommon” in 15 cultures.

Traditionally, many cultures, particularly Latin American ones, had strong double standards regarding male and female adultery, with the latter being seen as a much more serious violation.

Adultery involving a married woman and a man other than her husband was considered a very serious crime. In 1707, English Lord Chief Justice John Holt stated that a man having sexual relations with another man’s wife was “the highest invasion of property” and claimed, in regard to the aggrieved husband, that “a man cannot receive a higher provocation” (in a case of murder or manslaughter).

The Encyclopedia of Diderot & d’Alembert, Vol. 1 (1751), also equated adultery to theft writing that, “adultery is, after homicide, the most punishable of all crimes, because it is the most cruel of all thefts, and an outrage capable of inciting murders and the most deplorable excesses.”

# On BDSM

The United States Federal law does not list a specific criminal determination for consensual BDSM acts. Some states specifically address the idea of “consent to BDSM acts” within their assault laws, such as the state of New Jersey, which defines “simple assault” to be “a disorderly persons offense unless committed in a fight or scuffle entered into by mutual consent, in which case it is a petty disorderly persons offense”.

Mutual combat, a term commonly used in United States courts, occurs when two individuals intentionally and consensually engage in a fair fight, while not hurting bystanders or damaging property. There is not an official law that forbids mutual combat in the United States. There have been numerous cases where this concept was successfully used in defense of the accused. In some cases, mutual combat may nevertheless result in killings.

Oregon Ballot Measure 9 was a ballot measure in the U.S. state of Oregon in 1992, concerning sadism, masochism, gay rights, pedophilia, and public education, that drew widespread national attention. It would have added the following text to the Oregon Constitution:

All governments in Oregon may not use their monies or properties to promote, encourage or facilitate homosexuality, pedophilia, sadism or masochism. All levels of government, including public education systems, must assist in setting a standard for Oregon’s youth which recognizes that these behaviors are abnormal, wrong, unnatural and perverse and they are to be discouraged and avoided.

Dildos or any object used for “the stimulation of human genital organs” cannot be made or sold in Alabama. The Anti-Obscenity Enforcement Act says that anyone caught with such tools could face a fine up to \$20,000, a one-year jail sentence or 12-months doing hard labor.

Florida bans “lewd and lascivious behavior,” which is defined as a situation where “any man and woman, not being married to each other, lewdly and lasciviously associate and cohabit together.” The misdemeanor is punishable by a fine of up to \$500. In Mississippi, an unmarried couple caught living together “whether in adultery or fornication” can face up to six months in jail and/or a \$500 fine.

In 2003, the U.S. Supreme Court deemed a Texas state law that banned the practice of anal and oral sex between same-sex couples as unconstitutional. Despite the ruling, a sizable list of states, including Texas, still have anti-sodomy laws on the books.

Louisiana’s “crime against nature” statute prohibits the “the unnatural carnal copulation by a human being with another of the same sex or opposite sex or with an animal.” The state legislature in April failed to pass a bill that would have repealed the law except for human-on-animal relations.

Other states that have some form of anti-sodomy laws include Kansas, Oklahoma, Alabama, Florida, Idaho, Louisiana, Mississippi, North Carolina, South Carolina, and Utah, according to the Human Rights Campaign. Virginia repealed its ban in March.

# On Divorce

Today, every state plus the District of Columbia permits no-fault divorce, though requirements for obtaining a no-fault divorce vary. California was the first U.S. state to pass a no-fault divorce law. Its law was signed by Governor Ronald Reagan, a divorced and remarried former movie actor, and came into effect in 1970. New York was the last state to pass a no-fault divorce law; that law was passed in 2010.

Prior to the advent of no-fault divorce, a divorce was processed through the adversarial system as a civil action, meaning that a divorce could be obtained only through a showing of fault of one (and only one) of the parties in a marriage. This required that one spouse plead that the other had committed adultery, abandonment, felony, or other similarly culpable acts. However, the other spouse could plead a variety of defenses, like recrimination (essentially an accusation of “so did you”). A judge could find that the respondent had not committed the alleged act or the judge could accept the defense of recrimination and find both spouses at fault for the dysfunctional nature of their marriage. Either of these two findings was sufficient to defeat an action for divorce, which meant that the parties remained married.

Before no-fault divorce was available, spouses seeking divorce would often allege false grounds for divorce. Removing the incentive to perjure was one motivation for the no-fault movement.

In the States of Wisconsin, Oregon, Washington, Nevada, Nebraska, Montana, Missouri, Minnesota, Michigan, Kentucky, Kansas, Iowa, Indiana, Hawaii, Florida, Colorado and California, a person seeking a divorce is not permitted to allege a fault-based ground (e.g. adultery, abandonment or cruelty).

In some states, requirements were even more stringent. For instance, under its original (1819) constitution, Alabama required not only the consent of a court of chancery for a divorce (and only “in cases provided for by law”), but equally that of two-thirds of both houses of the state legislature. This requirement was dropped in 1861, when the state adopted a new constitution at the outset of the American Civil War. The required vote in this case was even stricter than that required to overturn the governor’s veto in Alabama, which required only a simple majority of both houses of the General Assembly.

These requirements could be problematic if both spouses were at fault or if neither spouse had committed a legally culpable act but both spouses desired a divorce by mutual consent. Lawyers began to advise their clients on how to create legal fictions to bypass the statutory requirements. One method popular in New York was referred to as “collusive adultery”, in which both sides deliberately agreed that the wife would come home at a certain time and discover her husband committing adultery with a “mistress” obtained for the occasion. The wife would then falsely swear to a carefully tailored version of these facts in court (thereby committing perjury). The husband would admit a similar version of those facts. The judge would convict the husband of adultery, and the couple could be divorced. Specifically, they report that “states that adopted no-fault divorce experienced a decrease of 8 to 16 percent in wives’ suicide rates and a 30 percent decline in domestic violence.”

The Code of Hammurabi (1754 BC) declares that a man must provide sustenance to a woman who has borne him children, so that she can raise them:

If a man wish to separate from a woman who has borne him children, or from his wife who has borne him children: then he shall give that wife her dowry, and a part of the usufruct of field, garden, and property, so that she can rear her children. When she has brought up her children, a portion of all that is given to the children, equal as that of one son, shall be given to her. She may then marry the man of her heart.

In the 1970s, the United States Supreme Court ruled against gender bias in alimony awards and, according to the U.S. Census Bureau, the percentage of alimony recipients who are male rose from 2.4% in 2001 to 3.6% in 2006. In states like Massachusetts and Louisiana, the salaries of new spouses may be used in determining the alimony paid to the previous partners.

Some of the possible factors that bear on the amount and duration of the support are:

 Factor Description Length of the marriage or civil union Generally, alimony lasts for a term or period. However, it will last longer if the marriage or civil union lasted longer. A marriage or civil union of over 10 years is often a candidate for permanent alimony. Time separated while still married In some U.S. states, separation is a triggering event, recognized as the end of the term of the marriage. Other U.S. states do not recognize separation or legal separation. In a state not recognizing separation, a 2-year marriage followed by an 8-year separation will generally be treated like a 10-year marriage. Age of the parties at the time of the divorce Generally, more youthful spouses are considered to be more able to ‘get on’ with their lives, and therefore thought to require shorter periods of support. Relative income of the parties In U.S. states that recognize a right of the spouses to live ‘according to the means to which they have become accustomed’, alimony attempts to adjust the incomes of the spouses so that they are able to approximate, as best possible, their prior lifestyle. Future financial prospects of the parties A spouse who is going to realize significant income in the future is likely to have to pay higher alimony than one who is not. Health of the parties Poor health goes towards need, and potentially an inability to support oneself. The courts are disinclined to leave one party indigent. Fault in marital breakdown In U.S. states where fault is recognized, fault can significantly affect alimony, increasing, reducing or even nullifying it. Many U.S. states are ‘no-fault‘ states, where one does not have to show fault to get divorced. No-fault divorce spares the spouses the acrimony of the ‘fault’ processes, and closes the eyes of the court to any and all improper spousal behavior. In Georgia, however, a person who has an affair that causes the divorce is not entitled to alimony.

# The North Korea problem isn’t solved

Donald Trump and Kim Jong Un just met and signed a deal committing North Korea to nuclear disarmament. Yay! Problem solved!

Except that there’s a long historical precedent of North Korea signing deals just like this one, only to immediately go back on them. Here’s a timeline for some relevant historical context.

1985: North Korea signs Nuclear Non-Proliferation Treaty
1992: North Korea signs historic agreement to halt nuclear program! (#1)
1993: North Korea is found to be cheating on its commitments under the NPT
1994: In exchange for US assistance in production of proliferation-free nuclear power plants, North Korea signs historic agreement to halt nuclear program! (#2)
1998: North Korea is suspected of having an underground nuclear facility
1998: North Korea launches missile tests over Japan
1999: North Korea signs historic agreement to end missile tests, in exchange for a partial lifting of economic sanctions by the US.
2000: North Korea signs historic agreement to reunify Korea! Nobel Peace Prize is awarded
2002-2003: North Korea admits to having a secret nuclear weapons program, and withdraws from the NPT
2004: North Korea allows an unofficial US delegation to visit its nuclear facilities to display a working nuclear weapon
2005: In exchange for economic and energy assistance, North Korea signs historic agreement to halt nuclear program and denuclearize! (#3)
2006: North Korea fires seven ballistic missiles and conducts an underground nuclear test
2006: North Korea declares support for denuclearization of Korean peninsula
2006: North Korea again supports denuclearization of Korean peninsula
2007: In exchange for energy aid from the US, North Korea signs historic agreement to halt nuclear program! (#4)
2007: N&S Korea sign agreement on reunification
2009: North Korea issues a statement outlining a plan to weaponize newly separated plutonium
2010: North Korea threatens war with South Korea
2010: North Korea again announces commitment to denuclearize
2011: North Korea announces plan to halt nuclear and missile tests
2012: North Korea announces halt to nuclear program
2013: North Korea announces intentions to conduct more nuclear tests
2014: North Korea test fires 30 short-range rockets, as well as two medium missiles into the Sea of Japan
2015: North Korea offers to halt nuclear tests
2016: North Korea announces that it has detonated a hydrogen bomb
2016: North Korea again announces support for denuclearization
2017: North Korea conducts its sixth nuclear test
2018: Kim Jong Un announces that North Korea will mass produce nuclear warheads and ballistic missiles for deployment
2018: In exchange for the cancellation of US-South Korea military exercises, North Korea, once again, commits to “work toward complete denuclearization on the Korean peninsula”

Maybe this time is really, truly different. But our priors should be informed by history, and history tell us that it’s almost certainly not.

# Some social justice factoids

Starting on a brief personal note…

I’m a bit disappointed with myself for being absent from this blog for the past few weeks. In a Reddit AMA last week, my favorite blogger said that the limiting factor on his productivity is the amount of time he has in a day. This to me is an ideal that I wish I could always be at. The limiting factor on my productivity is almost always my mental capacity to avoid the infinite potential sources of short-term gratification, and to motivate myself to do the things that I get deeper and more long-lived satisfaction out of. Writing this blog is one of those things. My capacity to enforce mental discipline is pretty correlated with my overall state of mind and mood. I think you can actually probably fairly reliably track my mental health by just looking at how often I’m posting here!

I’m also disappointed because I have been thinking about a great many interesting things that deserve posts. I like the idea of using this blog as a faithful recording of my intellectual life, and having discontinuities doesn’t help with this. Much of what I’ve been thinking about over the past few weeks is related to meta-ethics, but it also goes more broadly into the nature of philosophy in general. I hope to write up some posts on these soon.

In the meantime, I’ve also been compiling some interesting factoids I’ve recently encountered related to social justice. Here they are, with sources!

# Race

• Bias against blacks in the justice system can be found in sentencing and in arrests for drug use, but not in arrest rates for violent crimes, police shootings, prosecution rates, or conviction rates. Source.
• Juries in the Deep South were commonly all-white up until the 1986 case Batson v Kentucky (where loopholes that allowed exclusion of blacks from juries were closed). (from Just Mercy, p. 60)
• Black Americans graduate from high school at the same rate as white Americans (92.3% vs 95.6%). Source.
• In 1968, these numbers were 54.4% and 75%.
• Percentage of college graduates age 25 to 29: 22.8% and 42.1%. (19.3% gap)
• White adults who don’t graduate high school, don’t get married before having children, and don’t work full time have much greater median wealth than comparable black and Latino adults. Source.
• Consumption habits can’t explain the wealth gap: white households spend more than black households of comparable incomes.
• The median white single parent has 2.2 times more wealth than the median black two-parent household and 1.9 times more wealth than the median Latino two-parent household.
• Poverty rates among African Americans have declined substantially: 34.7% in 1968 to 21.4% in 2016. Source.
• Among whites: 10% in 1968 to 8.8% in 2016.
• Great table showing the change in socioeconomic circumstances of blacks and whites in the US from 1968 to 2018: (Source)
• Most strikingly in that table… Median household wealth is 10 times higher for white Americans than black Americans (but it used to be 20 times higher).

# Gender

• There is a 7% unexplained wage gap between men and women in the US. Source.
• Controlling for college major selection, occupational segregation, hours worked, unionization, education, race, ethnicity, age, and marital status.
• Female leaders are evaluated slightly more negatively than equivalent male leaders (controlling for leadership style). Source.
• The discrepancy is more pronounced for autocratic leadership styles, and vanishes for democratic leadership styles.
• Most anthropologists hold there are no known societies that are/were unambiguously matriarchal. Source.
• Experiments show that women value temporal flexibility relatively more than men, and men value income growth relatively more than women. This is the most powerful explanation of the wage gap. Source.
• Right after college, wages are pretty similar between men and women, and the wage gap appears as time passes, indicating that ‘innate’ differences aren’t hugely at play (including bargaining ability and temperament).
• 75% of the wage gap is due to differences within occupations, and only 25% across occupations.
• Among the top-paying occupations (salary ≥ \$60K), the within-occupation corrected pay gaps are biggest where there’s lots of self-employment (explained by self-employment being more demanding).
• Symphony orchestras introduced blind auditions in the ‘90s, which served as a natural experiment that found significant gender bias against women. Source.
• The analysis found that in a blind audition for preliminary rounds, the same woman was 9.3% more likely to be hired (from 19.3% to 28.6%), and the same man is 2.3% less likely to be hired.
• For final rounds, the same woman was 14.8% more likely to be hired in a blind audition (from 8.7% to 23.5%).
• Introduction of blind auditions also caused an explosion of female auditions.
• The rate of false reporting for sexual assault is in the range of 2-8%. Source.
• Estimates of the prevalence rate of campus sexual assault in the US vary hugely, from .61% to 27% of female students, depending on survey definitions and methodology. Source.
• The percentage of trans men that report lifetime suicide attempts is 46%, trans women is 42%, LGB adults is 10-20%, and among the overall US population is 4.6%. Source.
• Suicide attempt rates are lower (by about 9%) among trans women that are perceived by others as women, but are the same among trans men.

# Other

• “The IAT is a noisy, unreliable measure that correlates far too weakly with any real-world outcomes to be used to predict individuals’ behavior.” Source.
• Many early studies on IAT as a predictor of discriminatory behavior had serious methodological problems, including falsification of data by an “overzealous undergraduate”.
• IAT has a test-retest reliability of .55 on a scale from 0 to 1.
• Meta-analyses of the IAT-behavior link show that race IAT scores are weak predictors of discriminatory behavior.
• IAT tests done on fictional races that are identified as one oppressed and the other privileged show “implicit bias” against the oppressed group.
• More noise in the data predictably biases the IAT score downwards
• When people hear stereotyping is normal, they may do more of it. Source.
• The “few antibias trainings that have been proven to change people’s behavior” look at bias as a habit that can be broken. The Prejudice and Intergroup relations lab at UW Madison has had promising results with these type of trainings. Source.

Some takeaways: A lot of the concerns of the social justice movement are clearly very valid and rooted in real issues of societal inequalities that have been handed down to us by previous generations. That said, however, there is a good degree of subtlety required in the analysis of race and gender issues that is missing in the mainstream social justice movement.

The oft-cited 23% gender gap is misleading to say the least, and the actual percentage due to discrimination is unclear but something less than 7%. The focus the Black Lives Matter movement puts on racially biased police shootings is unjustified, and the focus would be better placed on disparate sentencing and drug arrests. And more generally, the overall trends in racial inequality in the United States look extremely positive in virtually every dimension.

It also looks like current methods at identifying and intervening on things like implicit bias and stereotyping leave a lot to be desired. This has some serious implications for questions about actual practical solutions to issues of racism and sexism… even if we acknowledge their existence and seriousness, this does not mean that we should jump on board with any plausible-sounding diversity training program. The question of how to solve these issues is highly nontrivial and deserves a lot of careful attention.

# The Scourge of Our Time

Human life must be respected and protected absolutely from the moment of conception. From the first moment of his existence, a human being must be recognized as having the rights of a person – among which is the inviolable right of every innocent being to life.

Since it must be treated from conception as a person, the embryo must be defended in its integrity, cared for, and healed, as far as possible, like any other human being.

Catechism of the Catholic Church, #2270, 2274

In this paper, Toby Ord advances a strong reductio ad absurdum of the standard pro-life position that life begins at conception. I’ve heard versions of this argument before, but hadn’t seen it laid out so clearly.

Here’s the argument:

1. The majority (~62%) of embryos die within a few weeks of conception (mostly from failure to implant in the lining of the uterus wall). A mother of three children could be expected to also have had five spontaneous abortions.
2. The Catholic Church promotes the premise that an embryo at conception has the same moral worth as a developed human. On this view, more than 60% of the world population dies in their first month of life, making this a more deadly condition than anything else in human history. Saving even 5% of embryos would save more lives than a cure for cancer.

3. Given the 200 million lives per year at stake, those that think life begins at conception should be directing massive amounts of resources towards ending spontaneous abortion and see it as the Scourge of our time.

Here are two graphs of the US survival curve: first, as we ordinarily see it, and second, as the pro-lifer is obligated to see it:

This is of course a really hard bullet for the pro-life camp to bite. If you’re like me, you see spontaneous abortions as morally neutral. Most of the time they happen before a pregnancy has been detected, leaving the mother unaware that anything even happened. It’s hard then to make a distinction between the enormous amount of spontaneous abortions naturally occurring and the comparatively minuscule number of intentional abortions.

I have previously had mixed feelings about abortion (after all, if our moral decision making ultimately comes down to trying to maximize some complicated expected value, it will likely be blind to whether is a real living being or just a “potential” living being), but this argument pretty much clinches the deal for me.

I’ve recently come across some pretty surprising statistics regarding guns and violence, so I’ve decided to compile some of them here. I might update this if I run across more interesting things in the future.

• Guns probably save many more lives than they end. Source (CDC and the National Research Council) and source (1995 criminology paper).
• There are an estimated 500,000 to 3,000,000 defensive gun uses per year, and only about 300,000 violent gun crimes per year.
• Defensive uses of guns in the US save around 162,000 lives per year (based off self-report), while overall non-suicide gun deaths only result in 11,000 deaths per year. Estimates of lives saved don’t include any military service, police work, or work as a security guard.
• Defensive gun use reliably reduces injury rates among gun-using crime victims.

• 1994 imposition of five-day waiting periods for firearms didn’t reduce the overall suicide rate. Source (paper in AMA journal).

• Homicides have been on the decline for years, and guns aren’t nearly as dangerous as we think. Source (Freakonomics podcast).
• There have been an average of 2 mass shootings and 16.5 fatalities a year from mass shootings (excluding gang shootings and armed robberies).
• Any particular handgun in the US will kill somebody about once every 10,000 years.
• A given swimming pool is 100 times more likely to lead to the death of a child than a particular gun is to lead to the death of a child.
• Gun buyback programs are horribly ineffective – typically saving an estimated .0001 lives.

• The “more likely to have your gun used against you” meme is super misleading; it refers to the increased chance of suicide in the home for men with guns, not intruders wielding your gun against you. Source for one of the original findings.

Side note: Upon reflection, I’m super suspicious of the 162,000 lives/year saved number. Obviously measuring the counterfactual “would you have died if not for X?” is hard, but the number seems impossibly large when you think about the current murder rate… it corresponds to almost an extra 50 per 100,000 where the current homicide rate is 4.9 per 100,000. The cited study looks at self-reported potential fatality, which seems quite plausibly skewed upwards (if people tend to exaggerate the lethality of their encounters).

# Cults, tribes, states, and markets

The general problem solved by Civilization is how to get a bunch of people with different goals, each partial to themselves, to live together in peace and build a happy society instead of all just killing each other. It’s easy to forget just how incredibly hard of a problem this is. The lesson of game theory is that even two people whose interests don’t align can end up in shitty suboptimal Nash equilibria where they’re both worse off, by each behaving apparently perfectly rationally. Generalize this to twenty people, or a thousand people, or 300 million people, and you start to get a sense of how surprising it is that civilization exists on the scale that it does at all.

Yes, history tells many thousands of tales about large-scale defecting (civil wars, corruption, oppressive treatment of minority populations, outbreaks of violence and lawlessness, disputes over the line of succession) and the anarchic chaos that results, but it’s easy to imagine it being way, way worse. People are complex things with complex desires, and when you put that many people together, you should expect some serious failures. Hell, even a world of selfless altruists with shared goals would still have a tough time solving coordination problems of this size. Nobody thinks that the average person is better than this, so what gives?

Part of the explanation comes from psychologists like Jonathan Haidt and Joshua Greene, who detail the process by which humans evolved a moral sense that involved things like tit-for-tat emotional responses and tribalistic impulses. This baseline level of desire to form cooperative equilibria with friends helps push the balance away from chaos towards civilization, but it can’t be the whole explanation. After all, history does not reveal a constant base-rate of cooperative capacity between different humans, but instead tells a story of increasingly large-scale and complex civilizations. We went from thousands of small tribes scattered across Africa and Asia, to chiefdoms of tens of thousands individuals all working together, to vast empires that were home to millions of humans, and to today’s complex balance of global forces that make up a cooperative web that we are all part of. And we did this in the space of some ten thousand years.

This is not the type of timescale over which we can reasonably expect that evolution drastically reshaped our brains. Our moral instincts (love of kin, loyalty to friends, deference to authority, altruistic tendencies) can help us explain the cooperation we saw in 6000 B.C.E. in a tribe of a few hundred individuals. But they aren’t as helpful when we’re talking about the global network of cooperation, in which lawfulness is ensured by groups of individuals thousands of miles away, in which virtually every product that we rely on in our day-to-day life is the result of a global supply chain that brings together thousands of individuals that have never even seen each other, and in which a large and growing proportion of the world have safe access to hospitals and schools and other fruits of cooperation.

The explanation for this immense growth of humanity’s cooperative capacity is the development of institutions. As time passed, different bands of humans tried out different ways of structuring their social order. Some ways of structuring society worked better and lived on to the next generations of humans, who made further experiments in civilizational engineering. I think there is a lot to be learned by looking at the products of this thousand-year-long selection process for designing stable cooperative structures and seeing what happened to work best. In a previous post I described the TIMN theory of social evolution, which can be thought of as a categorization of the most successful organizational strategies that we’ve invented across throughout history. The following categorization is inspired by this framing, but different in many places.

The State: Cooperation is enforced by a central authority who can punish defectors. This central authority employs vast networks of hierarchically descending authority and systems of bureaucracy to be able to reach out across huge populations and keep individuals from defecting, even if they are nowhere near the actual people in charge. “State” is technically too narrow of a term, as these types of structures are not limited to governments, but can include corporate governance by CEOs, religious organizations, and criminal organizations like the Medellin Cartel. Ronfeldt uses the term Institution for this instead, but that sounds too broad to me.

The Market: Cooperation is not enforced by anybody, but instead arises as a natural result of the self-interested behaviors of individuals that each stand to gain through an exchange of goods. Markets have some really nice properties that a structure like the State doesn’t have, such as natural tendencies for exchange rates to equilibrate towards those that maximize efficiency. They also are fantastically good at dealing with huge amounts of complex information that a single central authority would be unable to parse (for instance, a weather event occurs on one coast of the United States, affecting suppliers of certain products, who then adjust their prices to re-equilibrate, which then results in a cascade of changes in consumer behavior across other markets, which also then react, and eventually the “news” of the weather event has traveled to the other coast, adjusting prices so that the products are allocated efficiently). A beautiful feature of the Market structure is that you can get HUGE amounts of people to cooperate in order to produce incredibly innovative and valuable stuff, without this cooperation being explicitly enforced by threats of punishment for defecting. Of course, Markets also have numerous failings, and the nice properties I discussed only apply for certain types of goods (those that are excludable and rival). When the Market structure extends outside of this realm, you see catastrophic failures of organization, the scale of which pose genuine threats to the continued existence of human civilization.

The Tribe: Cooperation is achieved not through a central authority or through mutually beneficial exchange, but through strong kinship and friendship relations. Tribe-type structures spring up naturally all the time in extended families, groups of friends, or shared living situations. Strong loyalty intuitions and communitarian instincts can serve to functionally punish defectors through social exclusion from their tribe, giving it some immunity to invading defector strategies. But the primary mechanism through which cooperation is enforced is the part of our psychology that keeps us from lying to our friends or stealing from our partners, even when we think we can get away with it. The problem with this structure is that it scales really poorly. Our brains can only handle a few dozen real friendships at a time, and typically these relationships require regular contact to be maintained. Historically, this has meant that tribes can only survive for fairly small groups of people that are geographically close to each other, and this is pretty much the range of their effectiveness.

The Cult: The primary idea of this category is that cooperation does not arise from self-interested exchange or from punishment for defectors, but from shared sacred beliefs or values. These beliefs often shape their holders’ entire world-views and relate to intense feelings of meaning, purpose, reverence, and awe. They can be about political ideology, metaphysics, aesthetics, or anything else that carries with it sufficient value as to penetrate into and reshape a whole worldview. The world’s major religions are the most striking examples of this, having been one of the biggest shapers of human behavior throughout history. Different members of the same religion can pour countless hours into dedicated cooperative work, not because of any sense of kinship with one another, but because of a sense of shared purpose.

The Pope won’t throw you in jail if you stop going to church, and you don’t go to make an exchange of goods with your priest (except in some very metaphorical sense that I don’t find interesting). You go because you believe deeply in the importance of going. There are aspects of Science that remind me of the Cult structure, like the hours of unpaid and anonymous work that senior scientists put into reviewing the papers of their colleagues in the field in order to give guidance to journals, grant-funders, or the researchers themselves on the quality of the material. When I’ve asked why spend so much time on doing this when they are not getting paid or recognized for their work, the responses I’ve gotten make reference to the value of the peer-review process and the joy and importance of advancing the frontier of knowledge. This type of response clearly indicates the sense of Science as a Sacred Value that serves as a driving force in the behavior of many scientists.

A Cult is like a Tribe in many ways, but one that is not limited to small sizes. Cults can grow and become global behemoths, inspiring feelings of camaraderie between total strangers that have nothing in common besides shared worldview. While the term ‘Cult’ is typically derogatory, I don’t mean to use it in this sense here. Cults are incredibly powerful ways to get huge numbers of people to work together, despite there being no obvious reason why they should do so to anybody on the outside of their worldview. And not only do they inspire large-scale cooperative behavior, but they are powerful sources of meaning and purpose in our lives. This seems tremendously valuable and loaded with potential for developing a better future society. Think about the strength of something like Judaism, and how it persevered through thousands of years of repeated extermination attempts, diasporas, and religious factioning, all the while maintaining a strong sense of Jewish identity and fervent religious belief. Taking the perspective of an alien visiting the planet, it might be baffling to try to understand why this set of beliefs didn’t die out long ago, and what constituted the glue holding the Jewish people together.

I think that the Cult structure is really undervalued in the circles I hang out in, which tend to focus on the irrationality that is often associated with a Cult. This irrationality seems natural enough; a Cult forms around a deeply held belief or set of beliefs, and strong identification with beliefs leads to dogmatism and denial of evidence. I wonder if you could have a “Cult of Rationality”, in which the “sacred beliefs” include explicit dedication to open-mindedness and non-dogmatic thinking, or if this would be in some sense self-defeating. There’s also the memetic aspect of this, which is that not just any idea is apt to become a sacred belief. It might be that the type of person that is deeply invested in rationality is exactly the type that would typically scoff at the idea of a Cult of Rationality, for instance.

Broad strokes: Tribes play on our loyalty and kinship intuitions. States play on our respect for authority. Markets play on our self-interest. And Cults play on our sense of reverence, awe, and sacredness.