Our perceptions of some of the things we experience are deeply inaccurate. 🧵
Case 1: The vast majority of restaurants get few visits and go out of business quickly. This seems surprising because the typical restaurant you experience is busy and long-lived.
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The gap between reality and perception happens because few people experience any given unpopular, short-lived restaurant. Precisely because it is unpopular and short-lived!
The brilliant @CFCamerer, who gave this example, notes that it's not just curious but consequential.
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We, including aspiring restauranteurs, undersample unsuccessful restaurants so badly that it can make the restaurant business intuitively feel easy.
So too many people start restaurants who should have done other things instead.
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Maybe this abundance of bad restaurants would help others avoid the same mistake? No: the selection bias, and ignorance of it, are so powerful that it doesn't.
As the brilliant @4misceldah pointed out in the same conversation, once you do a little math you see that our misperception gets worse the more variance there is in restaurant popularity.
Well, there are many things that make popular restaurants more popular.
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We hear about good ones from others who have been there. We go with friends. We like to conform and do what's fashionable.
All these effects make the "rich get richer" and create a small number of very popular, profitable restaurants and the most extreme selection bias.
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Case 2: music.
Restaurants, like animals, have a natural maximum scale: one can only get so big, limiting the effects above. Not so with music.
The typical song you hear has tens of millions of listens. The typical song made has hundreds of listeners at most.
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As @CFCamerer, who just happened to run a record label, also points out, if you make an effort to sample SHOWS you find many with a dozen or two listeners. That shows just how popularity (and quality) biased our sampling processes are.
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One amusing corollary is that if you ever hear "this is a world premiere" for a piece of classical music and don't have very strong reasons to expect something amazing, you should immediately run the other way.
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Most classical pieces that are ever performed are performed ONCE and are not very good.
Our usual sampling process usually protects us from experiencing this part of the distribution. But if you’re told that this protection is somehow off, a Bayesian updates a lot!
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(This makes for a good little problem with Bayes' rule.)
The fact that many of us find implications of sampling biases surprising and counterintiutive suggests there's important behavioral economics to be done about them: even professionals find it hard to adjust for them.
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Who knows, though. Most sampling biases you hear about are much more interesting than the typical sampling bias.
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For what it's worth, though, I'd never thought properly about either the restaurant or music examples until @CFCamerer's and @4misceldah's comments - right-tail gems hidden in comment threads.
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PS/ Important caveat. As Chris points out here, restaurants are comparable to other service businesses. I think this does not upset the conventional wisdom that most small entrepreneurs are overoptimistic about the profits of their venture, but certainly
Excited to watch this talk by @renee_bowen_lyn : a model of echo chambers in social networks and how they take way less "behavioral error" than you might have thought to get started.
Behind the scenes there's a sort of puzzle based on a "naive martingale intuition": if there's abundant data and you understand the information process you're seeing, then a Bayesian should converge to accurate beliefs.
Here are some important statements that come up in economics:
"Nice estimators are consistent even in complicated models."
"Nice financial markets are informationally efficient."
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"Nice markets have price equilibria."
"Nice games have Nash equilibria."
The way these ideas are taught to Ph.D. economists in any field, even in core courses, involve very explicitly and extensively ideas extending ones in basic analysis.
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In particular, those ideas are: convergence (in fairly big spaces), integration and probability/martingales, continuity and fixed points.
Though you could get across aspects of these ideas at a high school level, econ grad school doesn't do them that way.
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One amusing feature of math classes at the master's level or above is that they almost take pride in not motivating the subject in external terms. For example, here's a page from a canonical textbook in algebraic geometry.
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This does a good job of reminding me of how Lecture 1 in such classes often felt, which is roughly, "The motivation for this class is fuck you. Let k be an arbitrary algebraically closed field. Now..."
Which was not a problem when I had my own motivations!
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At some point it stopped being enough. Incidentally, I don't think my economics courses were much better in the way of giving some great external motivation: I just found a cycle of self-reinforcing curiosity that kept me happily studying that subject.
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Was grateful to share at an #ASSA2021 session today a bit on what I've learned in teaching an undergraduate course on the Economics of Networks.
A short thread to serve as a focal point for any follow-up conversation.
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What networks is about (very rough and probably somewhat idiosyncratic description)
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I taught several variants of an undergraduate elective on this exciting and growing area. It was at the applied math/econ/CS intersection -- sometimes cross-listed, sometimes just economics but open to (and taken by) applied math, CS, other students.
This thread gives my own gloss and expansion of some points Doctor et al. raise.
Peters and co think there is a hidden assumption of economic theory: specifically, they think expected utility theory secretly assumes a mathematical property called ergodicity.
This is false.
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Expected utility theory makes 4 assumptions, which are stated precisely and concisely in every graduate textbook. Ergodicity is not among them.
EU is not the kind of theory that can hide assumptions: it is like Newtonian mechanics, not like Freudian analysis.
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