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
We want policy-related info institutions to resist manipulation, that is, to resist attempts to influence policy via distorted participation. Speculative markets do well here because they deal well with “noise trading,” that is, trading for reasons other than info about common asset values. When other traders can’t predict noise trading exactly, they compensate for its expected average by an opposite average trade, and compensate for its expected variation by trading more, and by working harder to find relevant info. Theory says that if trader risk-aversion is mild, and if more effort gives more info, then increased noise trading increases price accuracy. And in fact, the most accurate real speculative markets tend to be those with the most noise trading.
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?
IQ is an increasingly controversial topic these days. I find that when it comes up, different people seem to be extremely confident in wildly different beliefs about the nature of IQ as a measure of intelligence.
Part of this has to do with education. This paper analyzed the top 29 most used introductory psychology textbooks and “found that 79.3% of textbooks contained inaccurate statements and 79.3% had logical fallacies in their sections about intelligence.” 
This is pretty insane, and sounds kinda like something you’d hear from an Alex Jones-style conspiracy theorist. But if you look at what the world’s experts on human intelligence say about public opinion on intelligence, they’re all in agreement: misinformation about IQ is everywhere. It’s gotten to the point where world-famous respected psychologists like Steven Pinker are being blasted as racists in articles in mainstream news outlets for citing basic points of consensus in the scientific literature.
The reasons for this are pretty clear… people are worried about nasty social and political implications of true facts about IQ. There are worthwhile points to be made about morally hazardous beliefs and the possibility that some truths should not be publicly known. At the same time, the quantification and study of human intelligence is absurdly important. The difference between us and the rest of the animal world, the types of possible futures that are open to us as a civilization, the ability to understand the structure of the universe and manipulate it to our ends; these are the types of things that the subject of human intelligence touches on. In short, intelligence is how we accomplish anything as a civilization, and the prospect of missing out on ways to reliably intervene and enhance it because we avoided or covered up research that revealed some inconvenient truths seems really bad to me.
Overall, I lean towards thinking that the misinformation is so great, and the truth so important, that it’s worthwhile to attempt to clear things up. So! The purpose of this post is just to sort through some of the mess and come up with a concise and referenced list of some of the most important things we know about IQ and intelligence.
The most replicated finding in all of psychology is that good performance on virtually all cognitively demanding tasks is positively correlated. The name for whatever cognitive faculty causes this correlation is “general intelligence”, or g.
A definition of intelligence from 52 prominent intelligence researchers: 
Intelligence is a very general capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test‑taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—‘catching on’, ‘making sense’ of things, or ‘figuring out’ what to do. Intelligence, so defined, can be measured, and intelligence tests measure it well.
IQ tests are among the most reliable and valid of all psychological tests and assessments. 
They are designed to test general intelligence, and not character or personality.
Modern IQ tests have a standard error of measurement of about 3 points.
The distribution of IQs in a population nicely fits a Bell curve.
IQ is defined in such a way as to make the population mean exactly 100, and the standard deviation 15.
People with high IQs tend to be healthier, wealthier, live longer, and have more successful careers. 
IQ is highly predictive of educational aptitude and job performance. 
Longitudinal studies have shown that IQ “is a causal influence on future achievement measures whereas achievement measures do not substantially influence future IQ scores.” 
Average adult combined IQs associated with real-life accomplishments by various tests
MDs, JDs, and PhDs
1–3 years of college
Clerical and sales workers
High school graduates, skilled workers (e.g., electricians, cabinetmakers)
1–3 years of high school (completed 9–11 years of school)
This doesn’t mean that 30-year-old you is no smarter than 10-year-old you. It means that if you test the IQ of a bunch of children, and then later test them as adults, the rank order will remain roughly the same. A smarter-than-average 10 year old becomes a smarter-than-average 30 year old.
After your mid-20s, crystallized intelligence plateaus and fluid intelligence starts declining. Obligatory terrifying graph: (source)
High IQ is correlated with more gray matter in the brain, larger frontal lobes, and a thicker cortex. 
“There is a constant cascade of information being processed in the entire brain, but intelligence seems related to an efficient use of relatively few structures, where the more gray matter the better.” 
“Estimates of how much of the total variance in general intelligence can be attributed to genetic influences range from 30 to 80%.” 
Twin studies show the same results; there are substantial genetic influences on human intelligence. 
The genetic component of IQ is highly polygenic, and no specific genes have been robustly associated with human intelligence. The best we’ve found so far is a single gene that accounts for 0.1% of the variance in IQ. 
Many genes have been weakly associated with IQ. “40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals” is accounted for by genetic differences. 
Scientists can predict your IQ by looking only at your genes (not perfectly, but significantly better than random). 
This study analyzed 549,692 base pairs and found a R = .11 mean correlation between their predictions and the actual fluid intelligence of over 3500 unrelated adults. 
You might be wondering at this point what all the controversy regarding IQ is about. Why are so many people eager to dismiss IQ as a valid measure of intelligence? Well, we now dive straight into the heart of the controversy: intergroup variation in IQ.
It’s worth noting that, as Scott Alexander puts it: society is fixed, while biology is mutable. This fear we have that if biology factors into the underperformance of some groups, then such difference are intrinsically unalterable, makes little sense. We can do things to modify biology just as we can do things to modify society, and in fact the first is often mucheasier to do and more effective than the easier.
Anyway, prelude aside, we dive into the controversy.
Group differences in IQ
Yes, there are racial differences in IQ, both globally and within the United States. This has been studied to death, and is a universal consensus; you won’t find a single paper in a reputable psychology journal denying the numerical differences. 
Within the United States, there is a long-standing 1 SD (15 to 18 point) IQ difference between African Americans and White Americans. 
The tests in which these differences are most pronounced are those that most closely correspond to g, like Raven’s Progressive Matrices.  This test also is free of culturally-loaded knowledge, and only requires being able to solve visual pattern-recognition puzzles like these ones:
Controlling for the way the tests are formulated and administered does not affect this difference. 
IQ scores predict success equally accurately regardless of race or social class. This provides some evidence that the test is not culturally biased as a predictor.  
Internationally, the lowest average IQs are found in sub-Saharan Africa and the highest average IQs are found in East Asia. The variations span a range of three standard deviations (45 IQ points). 
Malawi has an estimated average IQ of 60.
Singapore and Hong Kong have estimated IQs around 108.
A large survey published in one of the top psychology journals polled over 250 experts on IQ and international intelligence differences. 
On possible causes of cross-national differences in cognitive ability: “Genes were rated as the most important cause (17%), followed by educational quality (11.44%), health (10.88%), and educational quantity (10.20%).”
“Around 90% of experts believed that genes had at least some influence on cross-national differences in cognitive ability.”
Men and women have equal average IQs.
But: “most IQ tests are constructed so that there are no overall score differences between females and males.” 
They do this by removing items that show significant sex differences. So, for instance, men have a 1 SD (15 point) advantage on visual-spatial tasks over women. Thus mental rotation tests have been removed, in order to reduce the perception of bias. 
Males also do better on proportional and mechanical reasoning and mathematics, while females do better on verbal tests. 
Hormones are thought to play a role in sex differences in cognitive abilities. 
Females that are exposed to male hormones in utero have higher spatiotemporal reasoning scores than females that are not. 
The same thing is seen with men that have higher testosterone levels, and older males given testosterone. 
There is also some evidence of men having a higher IQ variance than women, but this seems to be disputed. If true, it would indicate more men at the very bottom and the very top of the IQ scale (helping to explain sex disparities in high-IQ professions). 
In the developed world, average IQ has been increasing by 2 to 3 points per decade since 1930. This is called the Flynn effect.
The average IQ in the US in 1932, as measured by a 1997 IQ test, would be around 80. People with IQ 80 and below correspond to the bottom 9% of the 1997 population. 
Some studies have found that the Flynn effect seems to be waning in the developing world, and beginning in the developing world. 
A large survey of experts found that most attribute the Flynn effect to “better health and nutrition, more and better education and rising standards of living.” 
The Flynn effect is not limited to IQ tests, but is also found in memory tests, object naming, and other commonly used neuropsychological tests. 
Many studies indicate that the black-white IQ gap in the United States is closing. 
Can IQ be increased?
There are not any known interventions to reliably cause long term increases (although decreasing it is easy).
Essentially, you can do a handful of things to ensure that your child’s IQ is not low (give them access to education, provide them good nutrition, prevent iodine deficiency, etc), but you can’t do much beyond these.
Educational intervention programs have fairly unanimously failed to show long-term increases in IQ in the developed world. 
The best prekindergarten programs have a substantial short-term effect on IQ, but this effect fades by late elementary school.
Several large-scale longitudinal studies have found that children with higher IQ are more likely to have used illegal drugs by middle age. This association is stronger for women than men. 
This actually makes some sense, given that IQ is positively correlated with Openness (in the Big Five personality traits breakdown).
The average intelligence of Marines has been significantly declining since 1980. 
“The US military has minimum enlistment standards at about the IQ 85 level. There have been two experiments with lowering this to 80 but in both cases these men could not master soldiering well enough to justify their costs.” (from Wiki)
This is fairly terrifying when you consider that 10% of the US population has an IQ of 80 or below; evidently, this enormous segment of humanity has an extremely limited capacity to do useful work for society.
Researchers used to think that IQ declined significantly starting around age 20. Subsequently this was found to be mostly a product of the Flynn effect: as average IQ increases, the normed IQ value inflates, so a constant IQ looks like it decreases. (from Wiki)
The popular idea that listening to classical music increases IQ has not been borne out by research. (Wiki)
There’s evidence that intelligence is part of the explanation for differential health outcomes across socioeconomic class.
“…Health workers can diagnose and treat incubating problems, such as high blood pressure or diabetes, but only when people seek preventive screening and follow treatment regimens. Many do not. In fact, perhaps a third of all prescription medications are taken in a manner that jeopardizes the patient’s health. Non-adherence to prescribed treatment regimens doubles the risk of death among heart patients (Gallagher, Viscoli, & Horwitz, 1993). For better or worse, people are substantially their own primary health care providers.” “For instance, one study (Williams et al., 1995) found that, overall, 26% of the outpatients at two urban hospitals were unable to determine from an appointment slip when their next appointment was scheduled, and 42% did not understand directions for taking medicine on an empty stomach. The percentages specifically among outpatients with inadequate literacy were worse: 40% and 65%, respectively. In comparison, the percentages were 5% and 24% among outpatients with adequate literacy. In another study (Williams, Baker, Parker, & Nurss, 1998), many insulin-dependent diabetics did not understand fundamental facts for maintaining daily control of their disease: Among those classified as having inadequate literacy, about half did not know the signs of very low or very high blood sugar, and 60% did not know the corrective actions they needed to take if their blood sugar was too low or too high. Among diabetics, intelligence at time of diagnosis correlates significantly (.36) with diabetes knowledge measured 1 year later (Taylor, Frier, et al., 2003).” 
IQ differences might be able to account for a significant portion of global income inequality.
“… in a conventional Ramsey model, between one-fourth and one-half of income differences across countries can be explained by a single factor: The steady-state effect of large, persistent differences in national average IQ on worker productivity. These differences in cognitive ability – which are well-supported in the psychology literature – are likely to be malleable through better nutrition, better education, and better health care in the world’s poorest countries. A simple calibration exercise in the spirit of Bils and Klenow (AER, 2000) and Castro (Rev. Ec. Dyn., 2005) is conducted. According to the model, a move from the bottom decile of the global IQ distribution to the top decile will cause steady-state living standards to rise by between 75 and 350 percent. I provide evidence that little of IQ-productivity relationship is likely to be due to reverse causality.” 
Exposure to lead hampers cognitive development and lowers IQ. You can calculate the economic boost the US received as a result of the dramatic reduction in children’s exposure to lead since the 1970s and the resulting increase in IQs.
“The base-case estimate of $213 billion in economic benefit for each cohort is based on conservative assumptions about both the effect of IQ on earnings and the effect of lead on IQ.” 
Yes. $213 billion.
In a 113-country analysis, IQ has been found to positively affect all main measures of institutional quality.
“The results show that average IQ positively affects all the measures of institutional quality considered in our study, namely government efficiency, regulatory quality, rule of law, political stability and voice and accountability. The positive effect of intelligence is robust to controlling for other determinants of institutional quality.” 
High IQ people cooperate more in repeated prisoner’s experiments; 5% to 8% more cooperation per 100 point increase in SAT score (7 pt IQ increase). 
The second paper also shows more patience and higher savings rates for higher IQ. 
Embryo selection is a possible way to enhance the IQ of future generations, and is already technologically feasible.
“Biomedical research into human stem cell-derived gametes may enable iterated embryo selection (IES) in vitro, compressing multiple generations of selection into a few years or less.” 
Average IQ gain
1 in 2
1 in 10
1 in 100
1 in 1000
There is a ridiculous amount of research out there on IQ, and you can easily reach any conclusion you want by just finding some studies that agree with you. I’ve tried to stick to relying on large meta-analyses, papers of historical significance, large surveys of experts, and summaries by experts of consensus views.
(This post is a summary of the main things I found while diving into the economics literature on income inequality. Will try to condense my findings as much as possible, but there’s a lot to talk about. TL;DR at the end for lazy folk)
First, a note on terminology
Before getting into the published research on this topic, I started by surveying articles from popular news sources. I was curious to ultimately compare the standard media presentation to what I’d find in the scientific literature.
A large portion of what I read consisted of debates about the meanings of terms – one person says that capitalism is a lightly regulated market with a social safety net, another says any social safety net is socialism and therefore not capitalism, another says that a free market with any form of government regulation is corporatism, not capitalism, and they all yell at each other about terms and don’t get anything done.
By contrast, the terminology used in the economics and public policy literature was consistent, straightforward, and clear. I’ll define the controversial terms right here at the start to avoid confusion. These definitions are in line with the way that the terms are used in the literature.
Economic freedom: A combination of factors including limited regulation of businesses, protected rights to own private property, trade freedom, and small government.
(Preliminary post – am planning to write this all up more digestibly in a future post)
Free markets and income inequality
Capital in the 21st Century (Piketty)
When the rate of return on capital is greater than the rate of economic growth (as tends to occur in a free market given time), this leads to a concentration of wealth.
Wage Inequality: A Story of Policy Choices (Mishel, Scmitt, Shierholz)
Income inequality is the result of erosion of the minimum wage value, decreased union power, industrial deregulation, traid policy, failure to use fiscal spending to stimulate the economy, bad monetary policy by the Fed, and rent-seeking behaviors from CEOs.
Controversies about the Rise of American Inequality: A Survey(Gordon, Dew-Becker)
Rising inequality is due to a low minimum wage, the decline in unionization, audience magnification, generous stock options, and unregulated corporate wage practices, not imports, immigration, or a lower labor share of income.
Declining Labor and Capital Shares (Barkai)
Capital shares have declined faster than labor shares in the last 30 years, and the decline of labor shares is due entirely to an increase in markups, which decreases output and consumer welfare.
Why Hasn’t Democracy Slowed Rising Inequality? (Bonica, McCarty, Poole, Rosenthal)
Democracy hasn’t slowed the rise in inequality because of a political acceptance of free-market capitalism, immigration and a low turnout of poor voters, rising real income and wealth making social insurance less attractive, money influencing politics, and distortion of democracy through gerrymandering.
Billionaire Bonanza (Collins, Hoxie)
The people at the top are crazy rich and we should tax them.
Economic Freedom of the World: 2017 Annual Report (Gwartney, Lawson, Hall)
Economic freedom is strongly correlated with rapid growth, higher average income per capita, lower poverty rates, higher income amount/share for the poorest 10%, higher life expectancy, more civil liberties and political rights, more gender equality, greater happiness, and better access to electricity, gas, and water supplies.
The great Chinese inequality turnaround (Kanbur, Wang, Zhang)
Drop in Chinese inequality is due to tightening of rural labor markets from migration, government investment in infrastructure in the rural sector, minimum wage policies, and social programs.
(Some speculative rambling about stuff I’ve been thinking about recently.)
There’s a fallacy that I have committed hundreds of times, and that I have only really recently internalized as a fallacy. Perhaps it is not a fallacy, but a confused pattern of thought. In any case, I’ll call it “the incomprehensibility of the complex.”
Here’s the context in which I would make the mistake:
Somebody brings up some political or economic question, say “Should we have left Iraq?” or “Should we raise the minimum wage?”
This sparks a fierce debate. Somebody says that removing the troops left the region defenseless against takeover by extremist groups, or that extra wages given to workers go back into the economy and stimulate the economy. Another objects that our troops were ultimately the source of the instability, or cite the broken-window fallacy.
And I would think: “The world is crazily complicated. Physicists can barely understand complex atoms. Now scale that complexity up to interactions between hundreds of millions of humans, each one a system of a hundred trillion trillion atoms. This should put into perspective the proper degree of epistemic humility we should hold when discussing the minimum wage.”
Basically: If we can’t understand atoms, then we sure as hell can’t understand economic systems or international relations.
Observing that this is a bad argument is not too profound or interesting.
What’s interesting to me is the fact that this is a bad argument. That is, the fact that we can scale up the complexity of the system we are studying by a factor of 10^30, squint our eyes, and then get to work at creating fantastically simple and accurate models of the system. This is absolutely insane, and tells us something about the type of universe that we live in.
Recently I watched a lecture on Marginal Revolution University about gun buyback programs and slave redemption policies. The gist of it is this:
Starting in 1993, some humanitarian groups got in their head that they could save Sudanese slaves by buying them from their owners and then freeing them. This maybe sounds like a good idea, until you learn about supply and demand curves.
In truth, what the slave redeemers ended up doing was increasing demand for slaves, resulting in new slaves being captured and tens of thousands of dollars ending up in the hands of slave-owners. Fresh revenue funded weapons purchases, further enabling slave traders to raid villages and capture new slaves.
A similar thing can happen with gun buyback programs. These programs involve the buying of guns in large quantities from gun owners in order to melt them down, the thought being that this will get the guns off of the street. The effect of this?
Well, the gun producers thank their new customers for the money and start manufacturing more guns to supply their larger customer base. In some cases violent crime rates jumped, and a study measuring if these programs actually decrease violent crime rates overall found no statistically significant effects.
Now, I’m ashamed to say that these programs actually initially seemed like fine ideas to me. This is really a statement of my failure to have internalized how supply and demand curves work. In my defense, this is not always a totally horrible policy idea. When demand is much more elastic than supply, the price of the good will jump and many of the original buyers will be priced out of the market. In other words, if the producers have a harder time scaling up their operations than the consumers have buying less of the good, then the world will actually end up freer of slaves/guns.
But that is not how these markets actually work. Demand for guns is in fact less elastic than supply of guns, so the gun nuts are barely affected and the ungun-nuts are handing over free money to the gun manufacturers.
And one more example from Marginal Revolution. Sorry, but we’re on the topic of unintuitive basic econ and it’s just too good to leave out.
In 1990 the United States passed a policy that applied a tax on luxury goods like yachts. The idea, it seems, was, “The federal budget deficit is too high, and if we tax the rich on their fancy luxury goods, we can reduce the deficit without really hurting anybody.” Sounds good, yes?
But what actually happened was that as the price of yachts increased, rich people bought less, and thousands of laborers in the yacht industry lost their jobs. When all was said and done, the government ended up paying more in increased unemployment benefits than they gained in tax revenue from the policy! The government quickly wised up and repealed the tax a few years after it was put in place.
How to understand this? Easy! Draw a graph of supply and demand. Which one has a steeper slope? Well, rich people can fairly easily just spend their money differently if yacht prices increase. They care less about one less yacht than the workers that survive off of the wage they got making that yacht.
So the yacht-buyers will more easily leave the market than the yacht-producers, which means the demand for yachts is more elastic than the supply, which means that the producers are hurt more by the tax.
The point is, the model works! It makes weird-sounding and unintuitive predictions, and it turns out to be right. Literally just draw two lines and assess their relative slopes, and you can understand why a tax will sometimes burn consumers and other times burn producers. (You can also do better than the US government in 1990 apparently, but maybe this shouldn’t be surprising)
A simple model of our economy as a bunch of supply and demand curves with varying elasticities has enormous explanatory power. This is a breathtakingly simple model of a breathtakingly complex system. And it tells us something important about the world that it works at all.
Okay, enough fun with econ. All of this was just to say that I feel thoroughly rebutted in my old view that things like interactions of humans are too complex to be understood by anybody. So we have our mystery: how does simplicity arise out of complexity?
Here’s my attempt at an answer: simplicity arises when the universe is playing an optimization game with a simple target.
If every few seconds God scanned the universe, erased the least macroscopically circular shapes, and duplicated the rest, then you would quickly expect to be able the universe to consist of only circles. More to the point, it would quickly become possible to accurately model the universe as a bunch of circles of various sizes at various locations.
The clearest real world example of something like this is natural selection. Natural selection is a process that is optimizing biological systems for a simple target – reproductive fitness. It kills off variation and only lets those few forms that are able to reproduce successfully survive into the next generation.
In this sense, natural selection prunes down the complexity of the world, replacing the incomprehensible with the comprehensible. What was initially a high-entropy system, describable only at the level of fundamental physics, becomes a low-entropy system, describable by a few simple biological principles. Instead of having to describe the organism in full glorious detail at the level of quarks and electrons, we just need to explain how it won the optimization game of natural selection.
Gravity gives us another example of an optimization game our universe plays. Once you get enough mass in one place, gravity will crush it inward towards the center of mass, gradually inching diverse macroscopic shapes towards sphericity.
Which is why every large object you’ll see in the sky looks perfectly spherical. Any large objects that started off clunky and non-spherical were ruthlessly optimized into sphericity. (Actually they are oblate spheroids, but that’s because technically the optimization game they’re playing is gravity + angular momentum)
So why do supply and demand curves do a great job at predicting interactions between massive numbers of humans? The implied answer is that humans are the result of an optimization game that has made our behaviors simply describable in terms of supply and demand curves.
What exactly does this mean? Perhaps a trait that enhances reproductive fitness in organisms like us is the cognitive skill to make tradeoffs between different desires, and this gives rise to some type of universal comparison metric between very different goods. Now we can sensibly say things like “I want ice cream less than I want to enjoy a beautiful sunset. Except orange custard chocolate chip ice cream. I’d trade off the sunset for orange custard chocolate chip ice cream any day.”
Then somebody comes along with a bright idea called ‘money’, and suddenly we have a great generalization about human behavior: “Everybody wants more money.” From this, some basic notions like a downward-sloping demand curve, an upward-sloping supply curve, and a push towards equilibrium follow quite nicely. And we have a crazily simple high-level explanation of the crazily complex phenomenon of human interaction.