# Good enough is perfect, perfect is bad

Jan 8 JDN 2459953

Not too long ago, I read the book How to Keep House While Drowning by KC Davis, which I highly recommend. It offers a great deal of useful and practical advice, especially for someone neurodivergent and depressed living through an interminable pandemic (which I am, but honestly, odds are, you may be too). And to say it is a quick and easy read is actually an unfair understatement; it is explicitly designed to be readable in short bursts by people with ADHD, and it has a level of accessibility that most other books don’t even aspire to and I honestly hadn’t realized was possible. (The extreme contrast between this and academic papers is particularly apparent to me.)

One piece of advice that really stuck with me was this: Good enough is perfect.

At first, it sounded like nonsense; no, perfect is perfect, good enough is just good enough. But in fact there is a deep sense in which it is absolutely true.

Indeed, let me make it a bit stronger: Good enough is perfect; perfect is bad.

I doubt Davis thought of it in these terms, but this is a concise, elegant statement of the principles of bounded rationality. Sometimes it can be optimal not to optimize.

Suppose that you are trying to optimize something, but you have limited computational resources in which to do so. This is actually not a lot for you to suppose—it’s literally true of basically everyone basically every moment of every day.

But let’s make it a bit more concrete, and say that you need to find the solution to the following math problem: “What is the product of 2419 times 1137?” (Pretend you don’t have a calculator, as it would trivialize the exercise. I thought about using a problem you couldn’t do with a standard calculator, but I realized that would also make it much weirder and more obscure for my readers.)

Now, suppose that there are some quick, simple ways to get reasonably close to the correct answer, and some slow, difficult ways to actually get the answer precisely.

In this particular problem, the former is to approximate: What’s 2500 times 1000? 2,500,000. So it’s probably about 2,500,000.

Or we could approximate a bit more closely: Say 2400 times 1100, that’s about 100 times 100 times 24 times 11, which is 2 times 12 times 11 (times 10,000), which is 2 times (110 plus 22), which is 2 times 132 (times 10,000), which is 2,640,000.

Or, we could actually go through all the steps to do the full multiplication (remember I’m assuming you have no calculator), multiply, carry the 1s, add all four sums, re-check everything and probably fix it because you messed up somewhere; and then eventually you will get: 2,750,403.

So, our really fast method was only off by about 10%. Our moderately-fast method was only off by 4%. And both of them were a lot faster than getting the exact answer by hand.

Which of these methods you’d actually want to use depends on the context and the tools at hand. If you had a calculator, sure, get the exact answer. Even if you didn’t, but you were balancing the budget for a corporation, I’m pretty sure they’d care about that extra \$110,403. (Then again, they might not care about the \$403 or at least the \$3.) But just as an intellectual exercise, you really didn’t need to do anything; the optimal choice may have been to take my word for it. Or, if you were at all curious, you might be better off choosing the quick approximation rather than the precise answer. Since nothing of any real significance hinged on getting that answer, it may be simply a waste of your time to bother finding it.

This is of course a contrived example. But it’s not so far from many choices we make in real life.

Yes, if you are making a big choice—which job to take, what city to move to, whether to get married, which car or house to buy—you should get a precise answer. In fact, I make spreadsheets with formal utility calculations whenever I make a big choice, and I haven’t regretted it yet. (Did I really make a spreadsheet for getting married? You’re damn right I did; there were a lot of big financial decisions to make there—taxes, insurance, the wedding itself! I didn’t decide whom to marry that way, of course; but we always had the option of staying unmarried.)

But most of the choices we make from day to day are small choices: What should I have for lunch today? Should I vacuum the carpet now? What time should I go to bed? In the aggregate they may all add up to important things—but each one of them really won’t matter that much. If you were to construct a formal model to optimize your decision of everything to do each day, you’d spend your whole day doing nothing but constructing formal models. Perfect is bad.

In fact, even for big decisions, you can’t really get a perfect answer. There are just too many unknowns. Sometimes you can spend more effort gathering additional information—but that’s costly too, and sometimes the information you would most want simply isn’t available. (You can look up the weather in a city, visit it, ask people about it—but you can’t really know what it’s like to live there until you do.) Even those spreadsheet models I use to make big decisions contain error bars and robustness checks, and if, even after investing a lot of effort trying to get precise results, I still find two or more choices just can’t be clearly distinguished to within a good margin of error, I go with my gut. And that seems to have been the best choice for me to make. Good enough is perfect.

I think that being gifted as a child trained me to be dangerously perfectionist as an adult. (Many of you may find this familiar.) When it came to solving math problems, or answering quizzes, perfection really was an attainable goal a lot of the time.

As I got older and progressed further in my education, maybe getting every answer right was no longer feasible; but I still could get the best possible grade, and did, in most of my undergraduate classes and all of my graduate classes. To be clear, I’m not trying to brag here; if anything, I’m a little embarrassed. What it mainly shows is that I had learned the wrong priorities. In fact, one of the main reasons why I didn’t get a 4.0 average in undergrad is that I spent a lot more time back then writing novels and nonfiction books, which to this day I still consider my most important accomplishments and grieve that I’ve not (yet?) been able to get them commercially published. I did my best work when I wasn’t trying to be perfect. Good enough is perfect; perfect is bad.

Now here I am on the other side of the academic system, trying to carve out a career, and suddenly, there is no perfection. When my exam is being graded by someone else, there is a way to get the most points. When I’m the one grading the exams, there is no “correct answer” anymore. There is no one scoring me to see if I did the grading the “right way”—and so, no way to be sure I did it right.

Actually, here at Edinburgh, there are other instructors who moderate grades and often require me to revise them, which feels a bit like “getting it wrong”; but it’s really more like we had different ideas of what the grade curve should look like (not to mention US versus UK grading norms). There is no longer an objectively correct answer the way there is for, say, the derivative of x^3, the capital of France, or the definition of comparative advantage. (Or, one question I got wrong on an undergrad exam because I had zoned out of that lecture to write a book on my laptop: Whether cocaine is a dopamine reuptake inhibitor. It is. And the fact that I still remember that because I got it wrong over a decade ago tells you a lot about me.)

And then when it comes to research, it’s even worse: What even constitutes “good” research, let alone “perfect” research? What would be most scientifically rigorous isn’t what journals would be most likely to publish—and without much bigger grants, I can afford neither. I find myself longing for the research paper that will be so spectacular that top journals have to publish it, removing all risk of rejection and failure—in other words, perfect.

Yet such a paper plainly does not exist. Even if I were to do something that would win me a Nobel or a Fields Medal (this is, shall we say, unlikely), it probably wouldn’t be recognized as such immediately—a typical Nobel isn’t awarded until 20 or 30 years after the work that spawned it, and while Fields Medals are faster, they’re by no means instant or guaranteed. In fact, a lot of ground-breaking, paradigm-shifting research was originally relegated to minor journals because the top journals considered it too radical to publish.

Or I could try to do something trendy—feed into DSGE or GTFO—and try to get published that way. But I know my heart wouldn’t be in it, and so I’d be miserable the whole time. In fact, because it is neither my passion nor my expertise, I probably wouldn’t even do as good a job as someone who really buys into the core assumptions. I already have trouble speaking frequentist sometimes: Are we allowed to say “almost significant” for p = 0.06? Maximizing the likelihood is still kosher, right? Just so long as I don’t impose a prior? But speaking DSGE fluently and sincerely? I’d have an easier time speaking in Latin.

What I know—on some level at least—I ought to be doing is finding the research that I think is most worthwhile, given the resources I have available, and then getting it published wherever I can. Or, in fact, I should probably constrain a little by what I know about journals: I should do the most worthwhile research that is feasible for me and has a serious chance of getting published in a peer-reviewed journal. It’s sad that those two things aren’t the same, but they clearly aren’t. This constraint binds, and its Lagrange multiplier is measured in humanity’s future.

But one thing is very clear: By trying to find the perfect paper, I have floundered and, for the last year and a half, not written any papers at all. The right choice would surely have been to write something.

Because good enough is perfect, and perfect is bad.

# The sausage of statistics being made

Nov 11 JDN 2458434

“Laws, like sausages, cease to inspire respect in proportion as we know how they are made.”

Statistics are a bit like laws and sausages. There are a lot of things in statistical practice that don’t align with statistical theory. The most obvious examples are the fact that many results in statistics are asymptotic: they only strictly apply for infinitely large samples, and in any finite sample they will be some sort of approximation (we often don’t even know how good an approximation).

But the problem runs deeper than this: The whole idea of a p-value was originally supposed to be used to assess one single hypothesis that is the only one you test in your entire study.

That’s frankly a ludicrous expectation: Why would you write a whole paper just to test one parameter?

This is why I don’t actually think this so-called multiple comparisons problem is a problem with researchers doing too many hypothesis tests; I think it’s a problem with statisticians being fundamentally unreasonable about what statistics is useful for. We have to do multiple comparisons, so you should be telling us how to do it correctly.

Statisticians have this beautiful pure mathematics that generates all these lovely asymptotic results… and then they stop, as if they were done. But we aren’t dealing with infinite or even “sufficiently large” samples; we need to know what happens when your sample is 100, not when your sample is 10^29. We can’t assume that our variables are independently identically distributed; we don’t know their distribution, and we’re pretty sure they’re going to be somewhat dependent.

Even in an experimental context where we can randomly and independently assign some treatments, we can’t do that with lots of variables that are likely to matter, like age, gender, nationality, or field of study. And applied econometricians are in an even tighter bind; they often can’t randomize anything. They have to rely upon “instrumental variables” that they hope are “close enough to randomized” relative to whatever they want to study.

In practice what we tend to do is… fudge it. We use the formal statistical methods, and then we step back and apply a series of informal norms to see if the result actually makes sense to us. This is why almost no psychologists were actually convinced by Daryl Bem’s precognition experiments, despite his standard experimental methodology and perfect p < 0.05 results; he couldn’t pass any of the informal tests, particularly the most basic one of not violating any known fundamental laws of physics. We knew he had somehow cherry-picked the data, even before looking at it; nothing else was possible.

This is actually part of where the “hierarchy of sciences” notion is useful: One of the norms is that you’re not allowed to break the rules of the sciences above you, but you can break the rules of the sciences below you. So psychology has to obey physics, but physics doesn’t have to obey psychology. I think this is also part of why there’s so much enmity between economists and anthropologists; really we should be on the same level, cognizant of each other’s rules, but economists want to be above anthropologists so we can ignore culture, and anthropologists want to be above economists so they can ignore incentives.

Another informal norm is the “robustness check”, in which the researcher runs a dozen different regressions approaching the same basic question from different angles. “What if we control for this? What if we interact those two variables? What if we use a different instrument?” In terms of statistical theory, this doesn’t actually make a lot of sense; the probability distributions f(y|x) of y conditional on x and f(y|x, z) of y conditional on x and z are not the same thing, and wouldn’t in general be closely tied, depending on the distribution f(x|z) of x conditional on z. But in practice, most real-world phenomena are going to continue to show up even as you run a bunch of different regressions, and so we can be more confident that something is a real phenomenon insofar as that happens. If an effect drops out when you switch out a couple of control variables, it may have been a statistical artifact. But if it keeps appearing no matter what you do to try to make it go away, then it’s probably a real thing.

Because of the powerful career incentives toward publication and the strange obsession among journals with a p-value less than 0.05, another norm has emerged: Don’t actually trust p-values that are close to 0.05. The vast majority of the time, a p-value of 0.047 was the result of publication bias. Now if you see a p-value of 0.001, maybe then you can trust it—but you’re still relying on a lot of assumptions even then. I’ve seen some researchers argue that because of this, we should tighten our standards for publication to something like p < 0.01, but that’s missing the point; what we need to do is stop publishing based on p-values. If you tighten the threshold, you’re just going to get more rejected papers and then the few papers that do get published will now have even smaller p-values that are still utterly meaningless.

These informal norms protect us from the worst outcomes of bad research. But they are almost certainly not optimal. It’s all very vague and informal, and different researchers will often disagree vehemently over whether a given interpretation is valid. What we need are formal methods for solving these problems, so that we can have the objectivity and replicability that formal methods provide. Right now, our existing formal tools simply are not up to that task.

There are some things we may never be able to formalize: If we had a formal algorithm for coming up with good ideas, the AIs would already rule the world, and this would be either Terminator or The Culture depending on whether we designed the AIs correctly. But I think we should at least be able to formalize the basic question of “Is this statement likely to be true?” that is the fundamental motivation behind statistical hypothesis testing.

I think the answer is likely to be in a broad sense Bayesian, but Bayesians still have a lot of work left to do in order to give us really flexible, reliable statistical methods we can actually apply to the messy world of real data. In particular, tell us how to choose priors please! Prior selection is a fundamental make-or-break problem in Bayesian inference that has nonetheless been greatly neglected by most Bayesian statisticians. So, what do we do? We fall back on informal norms: Try maximum likelihood, which is like using a very flat prior. Try a normally-distributed prior. See if you can construct a prior from past data. If all those give the same thing, that’s a “robustness check” (see previous informal norm).

Informal norms are also inherently harder to teach and learn. I’ve seen a lot of other grad students flail wildly at statistics, not because they don’t know what a p-value means (though maybe that’s also sometimes true), but because they don’t really quite grok the informal underpinnings of good statistical inference. This can be very hard to explain to someone: They feel like they followed all the rules correctly, but you are saying their results are wrong, and now you can’t explain why.

In fact, some of the informal norms that are in wide use are clearly detrimental. In economics, norms have emerged that certain types of models are better simply because they are “more standard”, such as the dynamic stochastic general equilibrium models that can basically be fit to everything and have never actually usefully predicted anything. In fact, the best ones just predict what we already knew from Keynesian models. But without a formal norm for testing the validity of models, it’s been “DSGE or GTFO”. At present, it is considered “nonstandard” (read: “bad”) not to assume that your agents are either a single unitary “representative agent” or a continuum of infinitely-many agents—modeling the actual fact of finitely-many agents is just not done. Yet it’s hard for me to imagine any formal criterion that wouldn’t at least give you some points for correctly including the fact that there is more than one but less than infinity people in the world (obviously your model could still be bad in other ways).

I don’t know what these new statistical methods would look like. Maybe it’s as simple as formally justifying some of the norms we already use; maybe it’s as complicated as taking a fundamentally new approach to statistical inference. But we have to start somewhere.

# “DSGE or GTFO”: Macroeconomics took a wrong turn somewhere

Dec 31, JDN 2458119

The state of macro is good,” wrote Oliver Blanchard—in August 2008. This is rather like the turkey who is so pleased with how the farmer has been feeding him lately, the day before Thanksgiving.

It’s not easy to say exactly where macroeconomics went wrong, but I think Paul Romer is right when he makes the analogy between DSGE (dynamic stochastic general equilbrium) models and string theory. They are mathematically complex and difficult to understand, and people can make their careers by being the only ones who grasp them; therefore they must be right! Nevermind if they have no empirical support whatsoever.

To be fair, DSGE models are at least a little better than string theory; they can at least be fit to real-world data, which is better than string theory can say. But being fit to data and actually predicting data are fundamentally different things, and DSGE models typically forecast no better than far simpler models without their bold assumptions. You don’t need to assume all this stuff about a “representative agent” maximizing a well-defined utility function, or an Euler equation (that doesn’t even fit the data), or this ever-proliferating list of “random shocks” that end up taking up all the degrees of freedom your model was supposed to explain. Just regressing the variables on a few years of previous values of each other (a “vector autoregression” or VAR) generally gives you an equally-good forecast. The fact that these models can be made to fit the data well if you add enough degrees of freedom doesn’t actually make them good models. As Von Neumann warned us, with enough free parameters, you can fit an elephant.

But really what bothers me is not the DSGE but the GTFO (“get the [expletive] out”); it’s not that DSGE models are used, but that it’s almost impossible to get published as a macroeconomic theorist using anything else. Defenders of DSGE typically don’t even argue anymore that it is good; they argue that there are no credible alternatives. They characterize their opponents as “dilettantes” who aren’t opposing DSGE because we disagree with it; no, it must be because we don’t understand it. (Also, regarding that post, I’d just like to note that I now officially satisfy the Athreya Axiom of Absolute Arrogance: I have passed my qualifying exams in a top-50 economics PhD program. Yet my enmity toward DSGE has, if anything, only intensified.)

Of course, that argument only makes sense if you haven’t been actively suppressing all attempts to formulate an alternative, which is precisely what DSGE macroeconomists have been doing for the last two or three decades. And yet despite this suppression, there are alternatives emerging, particularly from the empirical side. There are now empirical approaches to macroeconomics that don’t use DSGE models. Regression discontinuity methods and other “natural experiment” designs—not to mention actual experiments—are quickly rising in popularity as economists realize that these methods allow us to actually empirically test our models instead of just adding more and more mathematical complexity to them.

But there still seems to be a lingering attitude that there is no other way to do macro theory. This is very frustrating for me personally, because deep down I think what I would like to do as a career is macro theory: By temperament I have always viewed the world through a very abstract, theoretical lens, and the issues I care most about—particularly inequality, development, and unemployment—are all fundamentally “macro” issues. I left physics when I realized I would be expected to do string theory. I don’t want to leave economics now that I’m expected to do DSGE. But I also definitely don’t want to do DSGE.

Fortunately with economics I have a backup plan: I can always be an “applied micreconomist” (rather the opposite of a theoretical macroeconomist I suppose), directly attached to the data in the form of empirical analyses or even direct, randomized controlled experiments. And there certainly is plenty of work to be done along the lines of Akerlof and Roth and Shiller and Kahneman and Thaler in cognitive and behavioral economics, which is also generally considered applied micro. I was never going to be an experimental physicist, but I can be an experimental economist. And I do get to use at least some theory: In particular, there’s an awful lot of game theory in experimental economics these days. Some of the most exciting stuff is actually in showing how human beings don’t behave the way classical game theory predicts (particularly in the Ultimatum Game and the Prisoner’s Dilemma), and trying to extend game theory into something that would fit our actual behavior. Cognitive science suggests that the result is going to end up looking quite different from game theory as we know it, and with my cognitive science background I may be particularly well-positioned to lead that charge.

Still, I don’t think I’ll be entirely satisfied if I can’t somehow bring my career back around to macroeconomic issues, and particularly the great elephant in the room of all economics, which is inequality. Underlying everything from Marxism to Trumpism, from the surging rents in Silicon Valley and the crushing poverty of Burkina Faso, to the Great Recession itself, is inequality. It is, in my view, the central question of economics: Who gets what, and why?

That is a fundamentally macro question, but you can’t even talk about that issue in DSGE as we know it; a “representative agent” inherently smooths over all inequality in the economy as though total GDP were all that mattered. A fundamentally new approach to macroeconomics is needed. Hopefully I can be part of that, but from my current position I don’t feel much empowered to fight this status quo. Maybe I need to spend at least a few more years doing something else, making a name for myself, and then I’ll be able to come back to this fight with a stronger position.

In the meantime, I guess there’s plenty of work to be done on cognitive biases and deviations from game theory.

# Toward an economics of social norms

Sep 17, JDN 2457649

It is typical in economics to assume that prices are set by perfect competition in markets with perfect information. This is obviously ridiculous, so many economists do go further and start looking into possible distortions of the market, such as externalities and monopolies. But almost always the assumption is still that human beings are neoclassical rational agents, what I call “infinite identical psychopaths”, selfish profit-maximizers with endless intelligence and zero empathy.

What happens when we recognize that human beings are not like this, but in fact are empathetic, social creatures, who care about one another and work toward the interests of (what they perceive to be) their tribe? How are prices really set? What actually decides what is made and sold? What does economics become once you understand sociology? (The good news is that experiments are now being done to find out.)

Presumably some degree of market competition is involved, and no small amount of externalities and monopolies. But one of the very strongest forces involved in setting prices in the real world is almost completely ignored, and that is social norms.

Social norms are tremendously powerful. They will drive us to bear torture, fight and die on battlefields, even detonate ourselves as suicide bombs. When we talk about “religion” or “ideology” motivating people to do things, really what we are talking about is social norms. While some weaker norms can be overridden, no amount of economic incentive can ever override a social norm at its full power. Moreover, most of our behavior in daily life is driven by social norms: How to dress, what to eat, where to live. Even the fundamental structure of our lives is written by social norms: Go to school, get a job, get married, raise a family.

Even academic economists, who imagine themselves one part purveyor of ultimate wisdom and one part perfectly rational agent, are clearly strongly driven by social norms—what problems are “interesting”, which researchers are “renowned”, what approaches are “sensible”, what statistical methods are “appropriate”. If economists were perfectly rational, dynamic stochastic general equilibrium models would be in the dustbin of history (because, like string theory, they have yet to lead to a single useful empirical prediction), research journals would not be filled with endless streams of irrelevant but impressive equations (I recently read one that basically spent half a page of calculus re-deriving the concept of GDP—and computer-generated gibberish has been published, because its math looked so impressive), and instead of frequentist p-values (and often misinterpreted at that), all the statistics would be written in the form of Bayesian logodds.

Indeed, in light of all this, I often like to say that to a first approximation, all human behavior is social norms.

How does this affect buying and selling? Well, first of all, there are some things we refuse to buy and sell, or at least that most of us refuse to buy and sell, and who use social pressure, public humilitation, or even the force of law to prevent. You’re not supposed to sell children. You’re not supposed to sell your vote. You’re not even supposed to sell sexual favors (though every society has always had a large segment of people who do, and more recently people are becoming more open to the idea of at least decriminalizing it). If we were neoclassical rational agents, we would have no such qualms; if we want something and someone is willing to sell it to us, we’ll buy it. But as actual human beings with emotions and social norms, we recognize that there is something fundamentally different about selling your vote as opposed to selling a shirt or a television. It’s not always immediately obvious where to draw the line, which is why sex work can be such a complicated issue (You can’t get paid to have sex… unless someone is filming it?). Different societies may do it differently: Part of the challenge of fighting corruption in Third World countries is that much of what we call corruption—and which actually is harmful to long-run economic development—isn’t perceived as “corruption” by the people involved in it, just as social custom (“Of course I’d hire my cousin! What kind of cousin would I be if I didn’t?”). Yet despite all that, almost everyone agrees that there is a line to be drawn. So there are whole markets that theoretically could exist, but don’t, or only exist as tiny black markets most people never participate in, because we consider selling those things morally wrong. Recently a whole subfield of cognitive economics has emerged studying these repugnant markets.

Even if a transaction is not considered so repugnant as to be unacceptable, there are also other classes of goods that are in some sense unsavory; something you really shouldn’t buy, but you’re not a monster for doing so. These are often called sin goods, and they have always included drugs, alcohol, and gambling—and I do mean always, as every human civilization has had these things—they include prostitution where it is legal, and as social norms change they are now beginning to include oil and coal as well (which can only be good for the future of Earth’s climate). Sin goods are systematically more expensive than they should be for their marginal cost, because most people are unwilling to participate in selling them. As a result, the financial returns for producing sin goods are systematically higher. Actually, this could partially explain why Wall Street banks are so profitable; when the banking system is corrupt as it is—and you’re not imagining that; laundering money for terroriststhen banking becomes a sin good, and good people don’t want to participate in it. Or perhaps the effect runs the other way around: Banking has been viewed as sinful for centuries (in Medieval times, usury was punished much the same way as witchcraft), and as a result only the sort of person who doesn’t care about social and moral norms becomes a banker—and so the banking system becomes horrifically corrupt. Is this a reason for good people to force ourselves to become bankers? Or is there another way—perhaps credit unions?

There are other ways that social norms drive prices as well. We have a concept ofa “fair wage”, which is quite distinct from the economic concept of a “market-clearing wage”. When people ask whether someone’s wage is fair, they don’t look at supply and demand and try to determine whether there are too many or too few people offering that service. They ask themselves what the labor is worth—what value has it added—and how hard that person has worked to do it—what cost it bore. Now, these aren’t totally unrelated to supply and demand (people are less likely to supply harder work, people are more likely to demand higher value), so it’s conceivable that these heuristics could lead us to more or less achieve the market-clearing wage most of the time. But there are also some systematic distortions to consider.

Perhaps the most important way fairness matters in economics is necessities: Basic requirements for human life such as food, housing, and medicine. The structure of our society also makes transportation, education, and Internet access increasingly necessary for basic functioning. From the perspective of an economist, it is a bit paradoxical how angry people get when the price of something important (such as healthcare) is increased: If it’s extremely valuable, shouldn’t you be willing to pay more? Why does it bother you less when something like a Lamborghini or a Rolex rises in price, something that almost certainly wasn’t even worth its previous price? You’re going to buy the necessities anyway, right? Well, as far as most economists are concerned, that’s all that matters—what gets bought and sold. But of course as a human being I do understand why people get angry about these things, and it is because they have to buy them anyway. When someone like Martin Shkreli raises the prices on basic goods, we feel exploited. There’s even a way to make this economically formal: When demand is highly inelastic, we are rightly very sensitive to the possibility of a monopoly, because monopolies under inelastic demand can extract huge profits and cause similarly huge amounts of damage to the welfare of their customers. That isn’t quite how most people would put it, but I think that has something to do with the ultimate reason we evolved that heuristic: It’s dangerous to let someone else control your basic necessities, because that gives them enormous power to exploit you. If they control things that aren’t as important to you, that doesn’t matter so much, because you can always do without if you must. So a norm that keeps businesses from overcharging on necessities is very important—and probably not as strong anymore as it should be.

Another very important way that fairness and markets can be misaligned is talent: What if something is just easier for one person than another? If you achieve the same goal with half the work, should you be rewarded more for being more efficient, or less because you bore less cost? Neoclassical economics doesn’t concern itself with such questions, asking only if supply and demand reached equilibrium. But we as human beings do care about such things; we want to know what wage a person deserves, not just what wage they would receive in a competitive market.

Could we be wrong to do that? Might it be better if we just let the market do its work? In some cases I think that may actually be true. Part of why CEO pay is rising so fast despite being uncorrelated with corporate profitability or even negatively correlated is that CEOs have convinced us (or convinced their boards of directors) that this is fair, that they deserve more stock options. They even convince them that their pay is based on performance, by using highly distorted measures of performance. If boards thought more like economic rational agents, when a CEO asked for more pay they’d ask: “What other company gave you a higher offer?” and if the CEO didn’t have an answer, they’d laugh and refuse the raise. Because in purely economic terms, that is all a salary does: it keeps you from quitting to work somewhere else. The competitive mechanism of the market is supposed to then ensure that your wage aligns with your marginal cost and marginal productivity purely due to that.

On the other hand, there are many groups of people who simply aren’t doing very well in the market: Women, racial minorities, people with disabilities. There are a lot of reasons for this, some of which might go away if markets were made more competitive—the classic argument that competitive markets reward companies that don’t discriminate—but many clearly wouldn’t. Indeed, that argument was never as strong as it at first appears; in a society where social norms are strongly in favor of bigotry, it can be completely economically rational to participate in bigotry to avoid being penalized. When Chick-Fil-A was revealed to have donated to anti-LGBT political groups, many people tried to boycott—but their sales actually increased from the publicity. Honestly it’s a bit baffling that they promised not to donate to such causes anymore; it was apparently a profitable business decision to be revealed as supporters of bigotry. And even when discrimination does hurt economic performance, companies are run by human beings, and they are still quite capable of discriminating regardless. Indeed, the best evidence we have that discrimination is inefficient comes from… businesses that persist in discriminating despite the fact that it is inefficient.

But okay, suppose we actually did manage to make everyone compensated according to their marginal productivity. (Or rather, what Rawls derided: “From each according to his marginal productivity, to each according to his threat advantage.”) The market would then clear and be highly efficient. Would that actually be a good thing? I’m not so sure.

A lot of people are highly unproductive through no fault of their own—particularly children and people with disabilities. Much of this is not discrimination; it’s just that they aren’t as good at providing services. Should we simply leave them to fend for themselves? Then there’s the key point about what marginal means in this case—it means “given what everyone else is doing”. But that means that you can be made obsolete by someone else’s actions, and in this era of rapid technological advancement, jobs become obsolete faster than ever. Unlike a lot of people, I recognize that it makes no sense to keep people working at jobs that can be automated—the machines are better. But still, what do we do with the people whose jobs have been eliminated? Do we treat them as worthless? When automated buses become affordable—and they will; I give it 20 years—do we throw the human bus drivers under them?

One way out is of course a basic income: Let the market wage be what it will, and then use the basic income to provide for what human beings deserve irrespective of their market productivity. I definitely support a basic income, of course, and this does solve the most serious problems like children and quadriplegics starving in the streets.

But as I read more of the arguments by people who favor a job guarantee instead of a basic income, I begin to understand better why they are uncomfortable with the idea: It doesn’t seem fair. A basic income breaks once and for all the link between “a fair day’s work” and “a fair day’s wage”. It runs counter to this very deep-seated intuition most people have that money is what you earn—and thereby deserve—by working, and only by working. That is an extremely powerful social norm, and breaking it will be very difficult; so it’s worth asking: Should we even try to break it? Is there a way to achieve a system where markets are both efficient and fair?

I’m honestly not sure; but I do know that we could make substantial progress from where we currently stand. Most billionaire wealth is pure rent in the economic sense: It’s received by corruption and market distortion, not by efficient market competition. Most poverty is due to failures of institutions, not lack of productivity of workers. As George Monblot famously wrote, “If wealth was the inevitable result of hard work and enterprise, every woman in Africa would be a millionaire.” Most of the income disparity between White men and others is due to discrimination, not actual skill—and what skill differences there are are largely the result of differences in education and upbringing anyway. So if we do in fact correct these huge inefficiencies, we will also be moving toward fairness at the same time. But still that nagging thought remains: When all that is done, will there come a day where we must decide whether we would rather have an efficient economy or a just society? And if it does, will we decide the right way?