373 | Colin Camerer on the Neuroscience of Economic Thinking

0:00:02.0 Sean Carroll: Hello, everyone. Welcome to the Mindscape podcast. I'm your host, Sean Carroll. In physics, you know, we have this distinction between fundamental physics and all the rest of physics. Fundamental physics is the stuff deep down, the quantum field theory, the general relativity, or perhaps even string theory or whatever, whereas all the rest is the emergent levels of the world, whether it's condensed matter physics or geophysics or biophysics or what have you. The analogous distinction in economics is between microeconomics and macroeconomics. When you think about the inflation rate or unemployment or the manufacturing sector or finance, you're thinking about macroeconomics. Microeconomics is the foundational part of the field that asks in very general terms how people go through the day making economic decisions. It's really about how people decide more generally to do one thing rather than another, simply indexed by spending money or something like that. So of course, this question of how people go about making decisions is a tough one.

0:01:12.1 SC: So there's a big debate about the foundations of economics, just as there's big debates about the foundations of physics, for that matter. For a long time, the simplest thing to do was to simply imagine that people were perfectly rational, more or less. No one ever thought that, right? No one ever actually thought, going back to Adam Smith, that people were perfectly rational all the time. But there's a simple assumption you can start making. Imagine that people have preferences and there's some probabilities and they have a utility function, and then you can derive consequences from that for economic behavior. But in recent decades, or at least let's say during my lifetime, so not so recent decades, people have begun to take more seriously the idea that maybe we should describe the decision-making process that real economic agents go through in somewhat more realistic terms. Today's guest, Colin Camerer, is an economics professor at Caltech, and he's been one of the leaders in this field for a long time. Sometimes the field is called behavioral economics, so economics that takes into account how people actually behave, amazingly enough.

0:02:22.3 SC: Colin has been a pioneer, the inventor basically of behavioral game theory. So thinking about game theory, which is another place where you could simplify things by imagining that people are perfectly rational, but instead checking out how people actually behave and what their limitations are imposed by the fact that some people make mistakes, our energy and stick-to-itiveness is not infinite when it comes to playing these games, etc. In more recent decades, Colin has also pioneered neuroeconomics. After all, if you want to ask how people really behave in these economic situations, maybe you want to know what's going on in their brains when they're making these decisions. So he is literally doing neuroscientific experiments, seeing what circuits are being activated, what modules are relevant when people are making these hard economic decisions. And it's, on the one hand, a field where they've discovered a lot of really cool stuff. On the other hand, it seems still a long way to go in terms of future discoveries to be made. So that makes it perfect kind of Mindscape material. Let's go.

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0:03:45.5 SC: Colin Camerer, welcome to the Mindscape podcast.

0:03:48.0 Colin Camerer: Glad to be here.

0:03:49.7 SC: I think I wanted to start by mentioning that a lot of people have this stereotype of economists, classical economists, anyway, as people who think that everyone is perfectly rational and they just maximize their utility. And we're going to move beyond that. But even before we move beyond that in your work, how accurate is or was that stereotype?

0:04:13.7 CC: I think it's... I think of it as a limiting case, like you might think about in physical science or natural, in the sense that there may be people with extraordinary experience, like a wine expert who has tasted a lot of wine and is immune to herd behavior and who can... The core of utility maximization is being able to rank every single thing, what's called completeness of preferences. So I think there are people who can do that. But it isn't... And maybe even there's probably a little bit like everything, a little bit of genetic predisposition to be highly rational in this sense of having a utility number for all these goods and bundles of goods too. So you make trade-offs between safety and a smoke alarm in your house that's annoying because it goes off too often and so forth. So trade-offs, besides utility maximization, trade-off is really the trickiest part because it assumes that people can judge two things that may not be very sensually in some ways commensurable. So I think it's a limiting case that occurs.

0:05:13.9 SC: But do you think that economists like to pretend that everyone is like that?

0:05:19.0 CC: I think there's an important distinction between the first few chapters of the book and what's taught and what's in the journals and what's actually believed. So I think of the model of utility maximization as a benchmark of rationality as sort of a starting point, like maybe Newtonian mechanics, where objects aren't moving too fast and so approximations work okay. And in a way it's a model of learning, or it could be a model of the endpoint of learning, as I was suggesting with wine experts. But it's not an endpoint for everyone and for all people at all different ages and with all different experiences and so forth. And it leaves a lot out.

0:05:59.2 SC: Yeah. So probably a lot of people took an economics class and got through the first few chapters of the book and they think that that's all that economics is.

0:06:07.5 CC: Right. Right. And it is true that in analyzing lots of real-world problems, that's always sort of the default way of thinking.

0:06:15.4 SC: Yeah.

0:06:16.7 CC: So for example, I was reading a paper by Chad Jones about how do you price... How much should we be spending on existential AI risk to limit it? And so he does a very typical economist study, which is to think of a value of a life and this probability and there's two things we care about: we like to have good AI, but we don't want to get wiped off the face of the planet. And you make this... And that's a pretty good starting point because how else would you begin?

0:06:44.7 SC: I guess so. I don't know. I mean, you've dived right into an area where I don't have strong opinions other than I'm not convinced by anyone's attempt to multiply really, really tiny numbers by really, really big numbers, right? This tiny probability by this huge utility. I think that that's... I'm kind of at sea as to what to do in those situations. Do you have a better view?

0:07:08.0 CC: No, I think... I mean, Jones is very, very cognizant about that. And I think the best analysis really are look at sensitivity analysis. In fact, in his paper, he has a... Like ablation they call it in AI, you know, if you change this assumption one at a time or in bundles, how much does it move around? And it may be that the right analysis right now moves around so much that it is almost worthless. I don't know. But anyway, that's one of the most exotic examples I could think of that was top of mind of where the economics mindset gets you started.

0:07:41.1 SC: Right. It's a good one. So by economics mindset, is that almost identical with kind of using game theory to model how people would go through these strategic interactions?

0:07:52.6 CC: Yes. So except for... There's a little bit of a historical Venn diagram. So game theory is really a branch of applied math originally. So John von Neumann worked on it, von Neumann-Morgenstern was an economist in their famous book that introduced game theory or made a gigantic leap forward, and a lot of the early work and continues to be done by people in applied math. But it's also... You can think of it as a candidate for how people actually behave. And not just people, but genes, organisms, nation states, et cetera.

0:08:20.1 SC: Yeah.

0:08:21.4 CC: I think it's in a way it's the closest thing we have to a unifying theory of social science which is used in lots of different places because the levels can be quite different. And so game theory kind of got co-opted or adopted or it became a project in economics. So now when most microeconomics is taught, you learn about supply and demand, equilibration in markets, collusion, monopoly, you know, blah, blah, blah, blah. And all of those things assume there's lots and lots of firms, so no one firm has to think too much about what the others would do, or the same on consumer side, it's called price taking. But in game theory, suppose you have three firms, then all of a sudden it makes sense for each of them to think about what the other's thinking, the other's thinking about each other, and so forth. So game theory, most of the important advances have been done in economics or by combinations of mathematicians who get interested in fundamental questions which have obvious application to economics. Like Bob Aumann, for example, is a PhD in Math, but he won the Nobel Prize in Economics for proving some important theorems about foundational questions.

0:09:30.7 SC: I think I didn't quite realize what you just said, but it makes perfect sense if I can rephrase it. You know, there was important work done in economics by Smith and Ricardo and Marx and whatever long before game theory came on the scene. And if I take what you just said, it sounds like what game theory adds to that is the ability to model the other agents acting with you.

0:09:54.8 CC: Correct, correct. And then it actually becomes quite rich because, for example, if you play a one-shot game like the so-called Prisoner's Dilemma, we might expect a lot of defection.

0:10:04.0 SC: Yeah.

0:10:04.6 CC: You know, people not being cooperative, like kids in class are all using AI because they don't want to lose out. But if it's a repeated game and people know the history and know that there's more periods coming, like two companies in a small town, then the results really change. In fact, it's so well known, it's called a folk theorem, that you can get cooperation which you wouldn't normally get mathematically in a one situation if you play repeatedly. So in parallel, lots more interesting mathematical questions arise, like can anything be a kind of equilibrium? Is there ways to pick out subsets in certain kinds? And so it really was a... I don't know how much mathematicians sitting in math departments contributed along the way, but certainly the foundational questions were often people who are quite mathematically sophisticated, and some like Aumann really did come from pure math backgrounds, or my colleague Omer Tamuz here at Caltech is in math and economics both.

0:11:06.6 SC: And I'm gonna apologize ahead of time because you've done so many interesting things, we're gonna breeze through them in a high-level way, and we're happy to pause and dig in at any moment. But in some sense, your starting point years ago was, you know, can we do better than thinking that people are purely rational, utility-maximizing game theorists? Can we actually think about how people actually think? Is that fair?

0:11:32.4 CC: Correct. Yes. More or less the mission statement that I use, which is very close to one from Dick Thaler, who won a Nobel Prize for pioneering so much of this work and is a co-author of mine, is we're trying to incorporate natural limits on computation, willpower, and self-interest to improve economic predictions and make better policy recommendations. And Dick, Richard Thaler, wouldn't use the word "natural". I kind of inserted that in there because I want people to think of an invitation to think about biological underpinnings and mechanisms and neuroscience as well as essentially psychology, sociology, and other adjacent disciplines.

0:12:14.8 SC: Is there a way... At the risk of oversimplifying, is there a way of giving a pithy answer to the question, how rational are people? I mean, how much does the model fail of thinking of them as perfect reasoners?

0:12:27.5 CC: Yeah, I was at the University of Chicago 100th anniversary of their PhD program, of which I'm a graduate, and somebody asked that. And I think I said 75% rational.

0:12:39.1 SC: I love that answer. [laughter]

0:12:41.8 CC: Well, I was trying to be not too evasive, you know, and I didn't want to say "it depends", which is the correct answer, actually.

0:12:47.6 SC: Yeah, of course.

0:12:49.4 CC: And again, I think there are interesting boundary cases. So there are cases where you have kind of extreme rationality. Like the wine experts are probably, kind of, if they rank two wines, they're gonna make the same ranking every time, doesn't matter where they are. So they're impervious to what we call context or situations. And there are other cases where you can move people around quite a bit. I'll give a quick example that might be a fun one. So my collaborator Eric Johnson, who's at Columbia, did a study where he asked people, "How likely do you think you'll live until 75?" And so people give a number like, you know, 90%. "How likely do you think it is that you'd be dead by 75?" They don't say 10%. Right? So the two numbers, you know, unless there's some weird thing happening right at your 75th birthday, right? The two numbers should add up to 100%.

0:13:41.8 SC: That's pretty basic.

0:13:43.0 CC: Yes. And it's an example of something which Eric and many people have studied, which is a sort of a kind of acquiescence bias. Like if I ask about living long, I think I'm probably gonna live long. If I ask about dying, I think a little bit more about dying.

0:13:55.0 SC: I see.

0:13:55.9 CC: And so you get attention, maybe attention, memory, you know, some inner process is sort of perturbing the numbers. It's not like we have a hard... Unless you're an actuary, right, or an accountant, it's not like we have a hard number in mind which we actually believe and then can retrieve.

0:14:12.2 SC: And we're gonna get into neuroscience at some point, but it sounds like this is rich for psychology as well. I mean, do you find like traditional psychology very useful in your work?

0:14:22.5 CC: Oh, yeah, yeah, yeah. In fact, what I just described as an example is called a framing effect.

0:14:27.5 SC: Yeah, yeah.

0:14:28.0 CC: Which is a term that Tversky and Kahneman coined to denote when the same thing we know is consequentially the same is just slightly... The language of it is just slightly changed, and it can have surprisingly large consequences in some cases. And they produced a whole string of interesting framing effects. If you call a salad dressing 10% fat, you sell a lot fewer than if it's 90% fat-free. So there are many... Lots of examples of these framing effects. And obviously advertisers and political consultants and everybody are in this business too.

0:15:02.0 SC: And another aspect, or maybe it's related, I mean, you're the expert, you tell me, but people are... How good are people at sort of thinking many levels through the game? Like, "I'm gonna do this, they're gonna do that, therefore I should do this," or do they kind of get tired of all that?

0:15:17.9 CC: Yeah, so most of the data we have is from lab experiments where we can design a game to kind of carefully titrate that. But there's some field data too. Let me walk through the lab experiments because the basic one is very simple. It's called a two-thirds of the average game, or we call it Keynesian beauty contest, as Keynes had an idea about the stock market that was similar. The idea is everyone picks a number from 0 to 100 simultaneously without talking. You write down your number, we're gonna compute the average, and whoever's closest to the average times two-thirds is gonna win a fixed prize. All right? So what people often start out thinking, "Well, if other people are just... I don't know what they're doing, so let's say the average of everyone else will be 50, I'll pick 33."

0:16:01.5 SC: Right.

0:16:02.5 CC: That's... We call that one step of thinking. But other people think, "You know what, other people are gonna pick 33, I'm gonna pick 22." That's two steps of thinking, right? And what we typically see in a distribution, in fact, there's many, many replications of this experiment in the lab and also in a number of newspapers have a contest where you kind of write in and they give some big prize. And so the New York Times, for example, we have data from 50,000 people doing this, and it looks a lot like students all over the world. The original data in Germany, we have data from Singapore, et cetera. And typically what you see is more 33s than 22s and not that many two-thirds of 22s, which is around 15. So it kind of peters out.

0:16:47.8 SC: Yeah, and no ones.

0:16:50.5 CC: No ones, unless you do it with an audience of game theorists, and even then, you don't get that many ones and zeros. You'll get fives and tens. So, you know, highly educated, but there's two pieces to this too, which I'm showing, which is it's not just that they're highly educated about game theory. They're in a group of other people who they know are highly educated about game theory.

0:17:11.5 SC: Yeah.

0:17:12.0 CC: Right? And so it's the, we call common knowledge or their sort of shared knowledge of background. And interestingly, there's some experiments with chess players, for example. You might think that chess players can do more strategic thinking, but they look very much like regular folks.

0:17:27.5 SC: Oh, okay.

0:17:28.0 CC: It doesn't seem to be... Depth of thinking seems to be somewhat domain specific, which is true of a lot of psychological things. There's very limited what we call transfer of learning, that it's not a very general skill, at least so far as we can tell.

0:17:41.2 SC: And I can imagine explaining that result by saying people only think it through one or two levels.

0:17:46.2 CC: Yes.

0:17:47.1 SC: There's another explanation that people think that other people are irrational, so they're just gonna foresee that. And can you disentangle those?

0:17:55.3 CC: Yes. Yeah, yeah. You can. So there's a few other games that disentangle them, and you can also manipulate who they're playing. So nobody's done this exact study, but I'm pretty confident what would happen if you did. If you had a group of very elite game theorists and you said, "Okay, we're gonna run this on Prolific or do a contest on an app, and you're gonna play those people," a lot of the game theorists who would choose zeros or ones among themselves will choose 22 or 15 or some number. In the New York Times, I picked 19, and the winner at the time was 20.

0:18:29.1 SC: Ah, pretty good. All right. You're well trained at human irrationality.

0:18:33.3 CC: Yes. Exactly.

0:18:34.2 SC: That's something to be an expert at. And this goes into what I think is called the cognitive hierarchy model. Is that a thing?

0:18:41.0 CC: Correct. Correct.

0:18:41.8 SC: Tell us about that.

0:18:42.5 CC: Yeah, yeah. So there's sort of two flavors. Sometimes it's called level-k. The cognitive hierarchy model just says there's some percentage of level zeros who are either choosing randomly or they may be doing something salient, like a, something... But the main thing is they're not thinking very strategically. So in Rosemarie Nagel's data, she was the first person to do this in her thesis, which I have in my office, she actually asked people afterwards, "Why did you pick the numbers you picked?" And so one person picked 18, and they were close to winning, much like the New York Times was 20. And the person said, "I picked 18 because I'm 18 years old." Right? So that's not three levels of strategic thinking, right?

0:19:22.5 SC: Yeah, it's not.

0:19:24.0 CC: And we only found out because Rosemarie asked them. That's what we call level zero, like picking a lucky number. Sometimes it's the middle. In some games it might be... There's another interesting game called 11-20 game. Sorry, the game theorists are not great at marketing. And in 11-20, you pick an integer from 11 to 20. And what's neat about it is you earn whatever number you picked. So there's a tendency to pick 20.

0:19:54.5 SC: Yeah.

0:19:55.0 CC: Right? But if you undercut the other person by one number, you get a big bonus of 20.

0:19:59.9 SC: I see.

0:20:01.0 CC: So if I think you're gonna pick 20, I will pick 19. And if I think you're gonna pick 19, I'll pick 18. But it doesn't go all the way down to 11 because if we both pick 11, nobody gets a bonus. So if I think you're gonna pick 11, I'm gonna pick 20.

0:20:15.1 SC: Interesting.

0:20:15.6 CC: If I can't get the bonus, I'll just get the highest. So you get what's called a mixed equilibrium, kind of like a cycle. And there the focal number or the salient number we think that level zeros pick 20. So it's as if they have a model of the game, but they neglect the fact that they could get more depending on what the other person does. They act like they're kind of playing by themselves. So it's as if they have a model, but they truncate out, "Oh, your payoff may depend on other people. Well, I don't know what they're gonna do, I'll just, yeah, forget about that." And anyway, so that game is another way to try to pin down, you know, number of levels, and usually it's something like two or three. The only exception, which is a little bit interesting, is, and I'm in a little debate with this with George Loewenstein, who's a behavioral economist at Carnegie, is language. So if you think about the iterated structure of language, depending what you call a level, you can get things like, "I know that you know that your wife didn't feed the cat and therefore you need to text your daughter," blah, blah, blah. You know, if you write it down, you could write it as games with a lot of levels in it. But I think the reason people might seem to be doing a lot of reasoning is because of just learning and familiarity or what cognitive scientists call schemas. You know, like we have sort of scripts for the way things work, and if it doesn't work that way, like a tree, you know, you have a branch, plan B and plan C. So it's possible that you can get higher levels, but I think it's pretty rare and maybe a specialized skill, or it may be in certain domains where this very high-level strategic thinking works.

0:21:59.5 SC: And you said the chess players don't have any special benefits elsewhere. Is there some idea that other than domains where we're special experts in, each person has a certain level thinker? Like, is it universal among different kind of problems that they would face?

0:22:20.1 CC: Partly, but not quite. Yeah, yeah. And we do know... Here's a few things related to that, which is we know that when you play a couple of these simple games, if you do it the way a psychometrician does it, which is not just play one but five or six different games, like you play the 11-20 game and then you play the two-thirds of the average game, and then there are several others that have a similar structure, and you kind of infer what level do they seem to be using, those are correlated with working memory, like how many digits can people remember, with education. So we have some data comparing Caltech students with Pasadena City College, which is a mile away. So controlling for geography, Caltech students, as you well know, are extremely good analytically. And they're also very homogeneous. Everyone is, there's no poets, right?

0:23:07.7 SC: Yeah [laughter]

0:23:08.6 CC: And drama majors. So they're really good, and they typically, the median math SAT is 800. We basically choose on verbal scores because there's so many, they're all good at standard STEM. And they were about... And these are old data, they were about one-half level extra thinking compared to the PCC, which was a little less than we thought. So there's some differences in groups and things like that.

0:23:38.2 SC: Have you... I mean, this is gonna be a provocative question, have you compared Caltech to Princeton and Stanford and Chicago students?

0:23:47.0 CC: No, but I'll have two answers to that question, the provocative question. And I'm not gonna conjecture what the truth would be, but you could guess what I would guess, and maybe your readers can. I was on sabbatical last year, thank you very much, Provost, at Harvard in the fall and MIT in the spring.

0:24:05.2 SC: Good.

0:24:06.0 CC: And I think the difference is the Harvard students are not necessarily selected on IQ.

0:24:12.8 SC: They're not. I was a Harvard grad student. I found that out.

0:24:16.2 CC: Exactly, exactly. They do have a lot of "In 10 years, I may be in the top 30 under 30 in Wired magazine."

0:24:26.9 SC: They're gonna rule the world. That's their selection.

0:24:29.1 CC: Yeah. "The youngest congressperson in my state ever." And you would think... A little strategic thinking wouldn't hurt in those cases, but it's not what they're necessarily gonna gravitate toward being good at.

0:24:44.8 SC: So you're gonna put the Caltech students up there.

0:24:47.4 CC: Yes, yes. There is... I'll tell you a related thing since you mentioned that, and I do have data on this or somebody else has data. So there's something called the cognitive reflection test, which is basically brain teasers. And one of them is the bat and the ball problem, which is more famous, but I'm gonna use a trickier cool one that's called the algae pond problem. The idea is there's a little bit of algae in a pond. The amount doubles every day. It covers the pond in 48 days. On what day is the pond half covered?

0:25:20.8 SC: [laughter] Okay.

0:25:23.1 CC: And the answer is not 24. If the algae was growing linearly, 24 would be the correct answer. So it's the right answer to a question that's almost the right question. Right?

0:25:35.2 SC: Right.

0:25:36.4 CC: It's what we call level... What Danny Kahneman, who coined this term, called System 1 thinking. It's like a snap judgment. And the right answer is 47, because if it doubles every day, it has to be half of full one day before 48, which is 47. And so Shane Frederick, who concocted this, he's a professor at Yale, collected a bunch of data from lots of schools, including Harvard and, not Princeton, I can't remember everything that's on the list. And also he went to the Boston Common and just collected answers there.

0:26:14.0 SC: Yeah.

0:26:14.8 CC: And the Boston Common is a big park in the middle of Boston, which is historically very important. I went to a political rally there. It's really super cool. And the Harvard students were a little bit below Boston Common. So on the cognitive reflection test, there's three questions. They're all a little different, but they have the same structure, like brain teaser structure. Right? Like there's an obvious wrong answer and then a subtler right answer. And so the scale goes from zero to three. The MIT students, I think, were 2.28 or something like that. We did some at Claremont, they were around maybe 1.6. And Harvard was a little bit below Boston Common. But I asked Shane about this and he said, "Oh, no, no, no, but the Boston Common people, the day we went was an orchestra or something, and they may have been above average Bostonians. The Boston Common goers are not a representative sample of Bostonians." Like, okay, okay.

0:27:11.4 SC: He did not want to accept the result. Okay.

0:27:15.1 CC: Anyway, anyway.

0:27:16.3 SC: So the program then is, okay, people are not fully rational, but you don't want to just say they're not fully rational. You sort of want to dig into what it is they are doing, right?

0:27:28.0 CC: Correct. Yes.

0:27:28.4 SC: I mean, there is a process going on in people's brains that maybe even is optimizing something, but it's other than being a perfect reasoner.

0:27:36.2 CC: Correct, correct. And from the get-go in behavioral economics, I started in the '80s, the people were always confronted with, "What's your theory? What's a better theory?" And that's exactly the right scientific question. So there is a kind of fringe of people early in the field of behavioral economics who were, I call them hecklers. It's like, "Well, my cousin bought the wrong insurance plan. Therefore, we've disproved everyone being rational all the time." Well, like, great. What's the replacement theory? Right? And the replacement theory has been essentially a series of sort of boutique theories revolving around different kinds of computational challenges. So cognitive hierarchy is one theory in game theory, which has gotten quite a bit of traction, including in linguistics, which is a cool area, and a field called adversarial control, which is basically, you know, two fighter jets are trying to patrol and scare off another enemy fighter jet. Like, how does that... What are the trajectories they take? And so people have applied level-k type of modeling to that. So that's been pretty fruitful. And... Oh, let me briefly mention the field data. So, we also have a data with experiment with field data, not an experiment, it's a actual thing that happened. So one of the implications of this level-k cognitive hierarchy type thinking is that people will not fully infer information from somebody else's actions. For example, from 2000 to 2009, we have a sample of 600 big-screen wide-release movies in the US and other countries. And in those days, it was rare, but occasionally, but if you see a movie poster or an ad for the movies, it used to be the old days, you'll remember, some of your readers who are young won't, it used to be that Friday reviews, sometimes on TV and the newspaper, were a big deal. It kind of guided what people... And so the studios would send movie screeners to the critics, maybe on Monday or Tuesday. They said, "Send us your review, even if it's bad. We may take a blurb out and it will say, 'Stunning, a magnificent cinematic achievement,' or 'Interesting, unusual.'" If it says one word, that's bad.

0:29:55.6 SC: Yes.

[laughter]

0:29:56.8 CC: Right. Like, "Nothing you've ever seen before." Right? Anyway, and so it turns out about 10% of movies were cold open, which means they were not shown to critics. And so it turns out the cold open movies are the worst ones, because critics later review them when they do come out and they get much lower ratings on what's called Metacritic, which is a nice aggregation of critic reviews. And so we took an aspect of this model that says some percentage of the moviegoers think that if there's no review, that doesn't mean anything. That means it's an average movie. But they're wrong, because the less average movies... So the fact that there's no review is bad news. In this case, no news is bad news. But psychologically it's a little tricky. It's kind of like Sherlock Holmes, the dog that didn't bark. Right? Like, if I didn't see a review, "Eh, you know, maybe it got lost in the mail or they didn't have room in the paper," blah, blah. But if you step back and think about it a little bit. And so the prediction we made was that these cold open movies, given their later critic rating, would do unusually well because you're kind of pulling in people who didn't realize that no review is a bad sign. And you can see that in the data. There's about a 15% increase in box office gross from this kind of sucker effect.

0:31:18.7 SC: Because I did figure out... I once did a project, we never published it, with Mark Wise, my colleague at Caltech, where we ranked how much money Hollywood movies make versus their budget.

0:31:31.4 CC: Oh, yeah, yeah. Yeah.

0:31:32.9 SC: And what we found was that Hollywood was actually extraordinarily rational as an industry. So two things. One is the money you make per dollar is the same whether you make a $100 million movie or $1 million movie. But the dispersion is also the same.

0:31:54.8 CC: Oh, interestingly.

0:31:55.7 SC: Because in the $1 million movies, there's a lot more dispersion, but there's also a lot more movies, so the average is the same. But they know how much money they're gonna make from a $100 million movie most of the time.

0:32:04.9 CC: Yeah, yeah. Typically, often a couple of days or a week before opening, you'll hear, "We expect it to open this much." And so it's some combination of analytics, and then they do screenings and they have people fill these cards, and usually they're fairly accurate that way.

0:32:21.6 SC: So they know which movies are gonna do badly and they just cold open those. They don't want anybody to know.

0:32:26.6 CC: Exactly. Yeah. Yeah. Yeah.

0:32:28.6 SC: Okay.

0:32:30.6 CC: And the paper's fun because there's some... A famous one is Pluto Nash by Eddie Murphy. And Eddie Murphy was a big star at that time. And so this is where the studios often get in trouble. It was kind of like, "Well, you made us a lot of money, you know, maybe you'll lose us some money. That's all forgiven." Anyway, another one was called Killers with Katherine Heigl and Ashton Kutcher, which...

0:32:56.7 SC: That one I don't know.

0:32:58.5 CC: Well, because, right, it didn't do very well.

0:33:00.3 SC: Probably for the best. Yeah.

0:33:01.6 CC: Yes. 21 on Metacritic.

0:33:04.4 SC: So you mentioned Kahneman and Tversky and System 1 and System 2. How does your picture here fit into that? Is it a compatible or is it a modification of that?

0:33:17.0 CC: Yeah, I think... And by the way, I'm a little bit of a System 1/System 2 skeptic in the following sense. So, when Kahneman wrote his Nobel lecture about this, and there's lots of other people who've studied it, he made a list of traits that System 1 has, associative, fast, effortless. And System 2 is parallel, serial, slow, deliberative, extrinsic, like I can tell you what I'm thinking. In the snap judgment case, maybe I can't tell you. And that's kind of pre-science. We don't want a neural system to be defined by a list of words. Right? We want to see it in the brain. Like the brain evidence is very good that there's something called the default mode network, which is what you're thinking when you're not doing anything active, the salience network. There's a number of things, like language areas we know quite a lot about. And I remember asking Danny once, early, maybe five years after System 1 and System 2 was sort of in the air, "Have you presented this to neuroscientists? And has anyone found anything that looks like System 1/System 2 dissociation?" And he said... He had a very distinctive accent, he said, "I did. I thought I was giving them a wonderful gift that they would all want to go study." And part of it was neuroscientists don't believe in that much reductionism.

0:34:41.0 SC: Yeah.

0:34:43.0 CC: There's an ongoing debate, you may know some bits of it, about modularity from Fodor and Cosmides and Tooby once said we think there's 78 different modules that are adapted modules to challenges in the environment of evolutionary adaptation, the EEA. And you know, there's lots of schemes for deciding like how many distinct parts of the brain there are. And so I think the neuroscientists felt like two is too small of a number.

0:35:07.0 SC: I see. Okay.

0:35:10.9 CC: And so it never got much traction. I mean, I wish people would work at it because if it is two, that would be wild. And it may well be, for example, that System 1 is available in infants and System 2 shows up in puberty or when you're 25, you know.

0:35:26.3 SC: Some people never. Yeah. [laughter]

0:35:29.6 CC: Yeah, yeah, you took the words out of my mouth.

0:35:33.9 SC: But that's a perfect segue because you are an economist who does neuroscience. And I mean, let's just ask the sort of career path question here. Was that an obvious thing to start doing as a professor of economics?

0:35:53.2 CC: No. No.

0:35:54.1 SC: I figured.

0:35:55.4 CC: Yeah. So the pattern it sort of fit was I like novelty and I'm a bit of a dilettante, although... Maybe I want to be more than a dilettante in the sense that what I'm proudest facts about my career is that I've published in a lot of different journals, sometimes on my own, but also with typically with a tutor, a senior collaborator who is my sensei.

0:36:24.7 SC: That's the way to do it.

0:36:25.8 CC: And can tell me what I need to know. And so the first thing I worked on after graduate school that was a little bit ahead of its time was experimental economics. So the idea of doing an experiment came very late to economics. In fact, up to 1985 and maybe still, Samuelson and Nordhaus's famous book that a lot of people taught from said, "Sadly, like astronomy and geology, we can't do experiments. Like, we can't make artificial earthquakes and create new planets in economics." And even at that point, there were hundreds of experiments already published, so I don't know what they were thinking. It was like a hallucination, the human form of hallucination or denial. Anyway, the idea came quite late, and so I got interested in that because Charlie Plott, my colleague at Caltech, who's a major figure, really one of the main founders in this area, taught a course at University of Chicago, and I learned about experimental economics. No one was doing it. I like doing things that no one else was doing. I don't really like competition, frankly.

0:37:26.6 SC: Yeah. Me too. I'm with you.

0:37:29.1 CC: Yeah. One way is to do something no one else does or to combine, synthesize things that are unusually unusual mixtures. And then I got interested in behavioral economics, and that was also controversial at the beginning and took a lot of... There were constant like battling with referees. It was kind of healthy because if you have scientific enemies can be very good for you. They read your papers very carefully. If you make a mistake, they will tell you about it. If they think there's a... They probably are willing to have a conversation that says, "What evidence would convince you?" You know, like, "Well, people do these games for $10. If you did it for $200, I'd be convinced." So then you go do it for $200. Or you do what we did and many people, you go to Vietnam, which is highly literate and poor at the time, 2010, and you do it there for the equivalent of 10 US dollars, which for them might be two days' wage. Right? So as Thaler likes to say, the easiest way to win an argument is to run another experiment or run another regression. So we were always in grit mode, like, "We're gonna prove it to you, prove it to you." Okay, here comes neuroscience. So in 1997, George Loewenstein and I were together at the Institute for Advanced... Sorry, not the Institute, that's Princeton. At Stanford, there's something called the Center for Advanced Study in the Behavioral Sciences, CASBS, C-A-S-B-S, that began in the '50s. And it's an amazing institution. It's a great place to go. Ironically, they are always begging me, "Do you know anyone in economics or neuroscience who wants to come?" Because most of the neuroscientists have big ongoing labs and they feel like they can't take a year away, even though I think their trainees probably wouldn't mind, to be honest, neuroscientists out there. And there's flights from San Francisco very frequently. Anyway, I was at this CASBS, and George Loewenstein and I were there, and also Drazen Prelec, who's a brilliant psychologist who contributed to behavioral economics, and his wife is a neuroscientist. And so George had gotten interested in neuroscience a little bit because he was good friends with John Cohen, a major cognitive neuroscientist who also kind of joined us in neuroeconomics with some of his projects. And so between Danica Prelec, John Cohen, and other people we knew, George and I organized a meeting in Pittsburgh that was basically, you know, "Our friends are gonna meet your friends." And so there was a few economists, none of whom were doing neuroscience. They were just like, "I'd like to hear what it is, and how do you study things, and what are the questions in economics like utility maximization? How would you try to study that thing?" And it was quite an amazing conference, except most of the economists were dead silent. So I think they were listening, number one.

0:40:19.7 SC: Okay.

0:40:20.8 CC: And they didn't know what to ask. Right? In economics, there's a little bit of a proud tradition, with some famous exceptions, of it's considered low status to say, "I don't really understand this."

0:40:49.0 SC: Oh, that's too bad.

0:40:50.4 CC: "Or how would this... I'm interested in common knowledge in games. How would this help me study that?" Occasionally people will ask, but anyway, they were just quiet. And by the way, in that group was Peter Diamond, Al Roth, Paul Romer, and I may have left somebody out, all three of whom later won Nobel Prizes. Now, did we have a causal effect? [laughter] I'm just saying it's a small sample. You know, we don't have much statistical power. And actually, no, none of them pretty much did anything. Actually, Paul Romer got quite interested in some aspect of it, and he wrote a small paper on emotion. And so he's the one who kind of came closest, but then he got distracted by lots of other things that were more important for him to do, frankly. So they were kind of like sympathizers, you know, "Tell us." And then there was kind of a lag, and then there's another starter conference at Princeton, which I missed, and Angus Deaton was there, later Nobel laureate. And Angus, again, is very far from a neuroscientist. He's interested in demand systems. You know, how do you fit together how much products people like and whether they substitute products? And he had done some beautiful work on that. But he basically said, "You know, I came to this conference because I don't think we really understand the source of demand. Like, I have all these data, but why do people like vanilla and not ice cream? Does it change over time? Does it depend on who is around them?"

0:42:21.4 SC: That's a great question.

0:42:22.1 CC: "Can you guys help us understand that?" And, you know, if you taught a course on demand theory, there's not a lot of neuroeconomics papers I would put on there right now, but we would have a research agenda, for example. And we could say something about a few things that have been studied, like self-control. Like, when do people buy tempting goods rather than healthy goods? We have some data suggesting dorsolateral prefrontal cortex interacting with another region, but it depends on how much your brain is sort of encoding or representing health value.

0:42:33.5 SC: Okay.

0:42:34.1 CC: So there's some bits of cogneuro that speak to things in economics, but it's just a very thin web of tissue. It's not a nice, dense plan. Anyway, so in the course of that, I moved to Caltech in '94. So this conference in '97 was a little bit in, and I started to ask around about neuroscience people I knew. One was John Allman, who turns out is one of the world's experts on cross-species comparison of primates and an incredibly wise person. So he was kind of my tutor. And then we got a brain scanner in 2003, and we got a kind of a volume discount. So there was one scanner to study monkeys, which was vertical, because the monkeys don't like to lie down and they're squirmy, so you have to lift them up into it like a kind of hairdresser. You know?

0:43:24.5 SC: Okay.

0:43:24.9 CC: And there's another one for small animals, which have tiny brains, so you need a big seven Tesla. And then we thought, well, we might as well get a human scanner. But as you may well appreciate from knowing Caltech, in biology, now called BBE, Biology and Biological Engineering, very few people are interested in humans.

0:43:43.4 SC: Yeah.

0:43:43.9 CC: Because we're too complicated a biological system.

0:43:47.2 SC: Too messy. Yeah, of course.

0:43:48.4 CC: Yeah, too messy. Right. If you want to study nicotine addiction, like Henry Lester, study rodents. If you want to study sleep and development, you want an organism that's transparent when they're born, so you study a zebrafish larvae with David Prober. Right. And so humans are like, "Man, there's too much going on." So we had this kind of Field of Dreams scanner that no one was using, and the staff people were really terrific. And a couple of us, actually Peter Bossaerts was one, Steve Quartz was another, said, "I have some ideas about what we could do with a scanner." And so people started to work on that. And some of it was a very Caltech formula, as you might recognize. So Peter started out as a finance professor, and he got interested in risk and reward. And he was like, "Well, what is risk and reward in the brain?" Like, fundamental thing in economics. And he was just good at it. And he, like me, was very adventurous. He was like, "I don't care if I can't publish these papers. I have tenure. That's what tenure's for."

0:44:42.0 SC: Yep.

0:44:42.8 CC: Steve Quartz was a wet philosopher. What that means is he had a PhD in philosophy. He did a postdoc at the Salk Institute at La Jolla, where Terry Sejnowski was and Francis Crick. And Steve was there at the same time as Read Montague and Peter Dayan. And so Montague and Dayan and Wolfram Schultz wrote a very famous paper in 1997 about dopamine, and that dopamine neurons were firing in midbrain as a reward signal, reward learning signal. Anyway, so Steve was around this kind of computational neuroscientific. And so he came to Caltech. He's still teaching philosophy. Like, he would teach a course on, like, free will. You know? [laughter] But he was interested and he liked to dabble. So he and Peter wrote some papers, I wrote some papers with them, and we kind of got a start. And Caltech, by the way, was quite supportive. Partly they had this free scanner. They just wanted to get something out of it, right? So for a few years it was free to use the scanner. It's normally quite expensive. They were basically subsidizing a startup. And they were pretty charmed by it. So I was able to do it, and I had good collaborators around.

0:46:00.0 SC: Yeah, I mean, that sounds great. The one thing I wanted to note, when you were talking about organizing the interdisciplinary conference, there's a piece recently by Paul Bloom, who's a former Mindscape guest...

0:46:10.2 CC: Yeah, yeah.

0:46:11.2 SC: He was trashing interdisciplinary conferences, not because the idea is bad, but because you would invite the best people from Area 1 and the best people from Area 2. But he said those aren't who you should invite. You should invite the people from Area 1 who care about Area 2, and those might not be the ones you've already identified as the best.

0:46:53.2 CC: Yes. Yes. No, I totally agree with him in the sense that it's very easy to put on a mediocre interdisciplinary conference that's a waste of time, that nobody would want to come back for part two. And it's exactly what he's put his finger on, which is you need people who are good at what they do, like really good, willing to learn language and communicate across languages. Like the word rational is used seven different ways. And so that takes some... You have to be in kind of student mode. Like you have to toggle from expert to student. And then these are people for whom this could really be a priority. Like if this meeting is fun, I want to go every year, or a collaboration comes out of it, or we write a joint grant for those funders that really like these kinds of mixtures, like John Templeton and sometimes DARPA or whomever.

0:47:19.6 SC: I like the idea of student mode because it's depressing how many professors don't want to get into student mode. Like they figured... They've paid that price already, they don't want to do that anymore. But it's kind of a shame. But okay, so this is a very broad question, but we can narrow in. What do we learn by looking at the brain? Is it something like, "Oh, when you're making a decision, this part lights up," or can we actually start identifying some of those modules you mentioned earlier?

0:47:47.4 CC: Yeah, so first of all, let me knock down a couple of kind of myths or things that people often say.

0:47:52.3 SC: Good.

0:47:53.1 CC: So one is that very few things are highly localized to one part of the brain. Right? So we do think about certain regions, like amygdala or DLPFC I mentioned, but typically we're looking for kind of a circuit. You know?

0:48:11.4 SC: Mm-hmm.

0:48:12.1 CC: And so that's number one. There's some exceptions, like language is largely left lateralized for...

0:48:16.8 SC: So that's left side of the brain.

0:48:18.5 CC: Left side of the brain. Yes, exactly, exactly. And so there's some cases like that. But even then, the brain has a fair amount of plasticity. So if you have damage in an area, or with animals we can sometimes lesion the animals, the people and animals can often, like the brain will reorganize itself to have some functionality in a different part of the brain. There's a very famous paper on this on ferrets' optic nerves and auditory, I believe, where you kind of rewire. And there's even, you know, there's people who have hemispherectomies where they remove an entire hemisphere and they can do a lot of things. So the brain has a certain resilience. It depends when it happens. If it happens early in life, it's better, and so on, and so on. But so one distinction I think is important is... Oh, so let me give you one concrete example that I think builds on what we were talking about. So when it comes to strategic thinking, the terms that I prefer is mentalizing. Another term people use is theory of mind, which is the idea that I have a... It's not my favorite term, but you know, that's why I like mentalizing. Theory of mind is the idea that I have some kind of theory or personal representation of what's in your mind.

0:49:36.7 SC: Yes.

0:49:37.7 CC: Okay. All right. And there's a lot of philosophical nitpicking about... Which is interesting actually, about how it works, but that doesn't help guide us too much on the neuroscientific discovery. It's been a lively debate, and lively debates are usually good. But there is pretty good evidence on the mentalizing circuit.

0:49:58.5 SC: Okay.

0:49:58.6 CC: So part of it is in what's called right temporoparietal junction. So there's four main lobes of the brain, temporal, parietal is motor, occipital cortex in the back, and then frontal. And so temporoparietal junction is sort of a region where the temporal and the parietal lobes tend to meet. There's part of the mentalizing circuit is dorsomedial prefrontal cortex. Dorsal is on the top, medial in the middle. So that's, you know, an area like a big quarter on the top of your forehead, an inch above your eyebrows. There's some regions in temporal lobe and posterior cingulate. And so if we do what's called a localizer, like if I show people a bunch of pictures, like my colleague Ralph Adolphs does, and I said, "What is this person doing with their arm?" You would say reaching. Right? And I could give a similar picture and say, "Why is this person moving their arm that way?" So the how question is not about the contents of the person's mind. The why question is about why are they doing it.

0:51:05.6 SC: Yeah.

0:51:06.6 CC: Or you could show a person who's crying and said, "What is this person's face doing?" It's crying. Then you ask, "Why are they crying?" Okay. And so if you run this why-how localizer and you look, what are the areas of the brain that are more active in why than how? And just to help your listeners too, the reason we do both is because whenever you put something on the screen and you have people press a button, tons of stuff is going on in the brain.

0:51:29.8 SC: Everything goes crazy. Yeah, right.

0:51:32.0 CC: Yeah. And so we want to have two things that are very, very similar in terms of what buttons you press, what you see on the screen, the luminosity, the emotions. So they literally would use the same pictures that permit either a how-why judgment. And then you subtract the how activity from the why, and what's left over we think is mentalizing.

0:51:53.4 SC: Okay. And... Yeah.

0:51:54.9 CC: And there's a couple of studies, but not very many, including by us. There's one nice study on the two-thirds of the average game, and people who do two steps of thinking in two-thirds of the average have more activity in mentalizing circuitry.

0:52:11.1 SC: Oh, okay.

0:52:12.1 CC: Right? And I wish there was... For some reason, including my laziness, I wish there was a lot more studies on that. There's a few, and they all kind of point to this idea that more strategic thinking, as evidenced by these kind of extra levels, like 22s or 18 or 19 in the 11-20 game, those extra steps of thinking are recruiting this mentalizing.

0:52:33.5 SC: And how well can we... I mean, so sorry, let me back up a little bit. Given that you're able to do at least some connection between that way of thinking and something happening in the brain, what kind of story can we tell about why we think that way, why we're not better at multi-level thinking? Does it come down to energy usage or just accidents of evolution or satisficing? I'm not quite sure.

0:53:00.1 CC: Energy usage is always a good candidate, right? You know, because the brain runs on 40 watts and what evolution is generally doing is thrifting, you know, it's trying to sort of offload to displacement approximations, offload things. There's a couple of papers on this. I don't know if the fossil record or anything, I should ask John Allman, right? Actually, he's often in at Santa Fe. You should ask him.

0:53:26.8 SC: I'll ask him, yeah.

0:53:29.5 CC: Whether there was some kind of rate limit that kept human brain from developing more steps of thinking. Probably it's something like there's a bit of a kind of mental arms race where if I could do more thinking, I can figure out if you're fooling me and I can fool you. But unless you're a very social species in which that's highly advantageous, you know, you just wouldn't get enough evolutionary pressure to generate more. It's a very good question. I wish... If I had five PhD students writing theses, we would...

0:54:04.1 SC: One of them would...

0:54:05.4 CC: Tell you in three years.

0:54:05.7 SC: Okay, good. Well, you know, there might be people out there listening who can then apply to grad school at Caltech and do that.

0:54:11.5 CC: Yes.

0:54:12.1 SC: One of the obvious ways to me that people fall a little short of being perfectly rational is when we're trying to compare what we want right now in the moment to what might benefit us down the road, right? Planning for the future. Is that something that you can see in a distinction in the brain or is it...

0:54:33.8 CC: Yes. Yes.

0:54:34.6 SC: Okay.

0:54:35.9 CC: Yeah, that's a great topic. So in psychology, it's often called delay discounting. How much do you discount or underweight things that you get with a delay? We often call it time discounting. And interestingly, animal learning, biology, psych, and economics have cohered on a fairly standard set of definitions and tools for measuring it. And a very typical one is called smaller sooner, longer later. So the idea is you could have $10 now or in a month I'll send you $15.

0:55:10.1 SC: Yep.

0:55:11.4 CC: And people say, "Well, I want 10." So then we'll usually calibrate that. You can do this in a Bayesian optimal way. So it's like, "Well, how about 10 or 18?" "Okay, I still want 10." "10 or 20." Right? So you can titrate this and then figure out what's an implicit interest rate. So we call it a discount factor, but it's exactly like an interest rate. And there's quite good evidence about how that works in the brain. And again, dorsolateral prefrontal cortex is quite important. So there's something called prospective thinking, which is basically when people are thinking about the future. And the prospective thinking system is fairly well understood. One of the early people who worked on it, by the way, was Demis Hassabis, who's now at DeepMind.

0:55:57.7 SC: Yeah.

0:55:58.0 CC: Nobel laureate. So a number of people in his circle were interested in this. And so we have a study related to this, which is the food study I alluded to briefly before, which is people see pictures of food and there are things like apples, yogurt, Snickers bars... And they rate them on health and taste. And each individual rates them. We don't take some rate because, you know, people may... Some people love tofu, some don't. And then we ask them to choose between goods. And what happens is when they're choosing the tempting one, there's more activity... Sorry, when they're resisting the tempting one, right? So they see tofu and Snickers, and when they choose tofu... And these are people, by the way, who we recruited who said they're trying to diet. So they're people who we know kind of have everyday self-control struggles. We didn't super carefully titrate, you know, what that means. And this is pre-GLP-1, so that's going to scrub all these actually... I don't know if it's going to screw up the studies, but it becomes a really interesting thing to study separately.

0:57:04.2 SC: New thing to study, yeah.

0:57:05.1 CC: Right, exactly. Anyway, and so you ask people about these healthy foods and then more tempting foods that they like better. And when they're able to resist temptation, there's activity in DLPFC. So we think of that... I think of it as... It's also involved in working memory and other lots of other major tasks, what's called executive function. So I think of that as a place that reminds you now about future consequences, like health.

0:57:29.1 SC: Right. Right.

0:57:33.1 CC: Like health. And there's another really beautiful study on this topic. So one thing people have studied is called EFT, episodic future tagging. And the original study is so beautiful. So what they did is they took a calendar and they asked subjects, "What are some things that are happening in the next couple months?" Like, "Oh, I have my PhD defense," "Oh, I'm going to Santa Fe," "I'm on Sean's podcast." And they had them rate... "I'm going to go to the dentist." Are they good things or bad things? So how vivid is it when you think about it, and is it good or bad? And then what they did was they did the SSLL experiment. You could have $10 now or $15 in a month. But that's the control treatment. In the episodic treatment, they said, "You can have $10 now or $15 when you're going to be in Sean Carroll's podcast in a month." So the time is fixed, but again, there's a kind of framing difference where you've injected this vividness, this salience. And then people are more patient.

0:58:41.3 SC: [laughter] Yeah, that makes sense.

0:58:41.7 CC: Yeah, the idea is that the episodic imaginability through prospective thinking kind of pulls... It's as if it pulls the future forward mentally, and it changes the discount rate.

0:58:53.1 SC: Is it that or is it... I mean, in part, when you say something like, "I'll give you $10 today or $20 in the future," part of me just doesn't believe you, right?

0:59:03.8 CC: Oh, okay, yeah, yeah.

0:59:04.8 SC: Like, you know, maybe you're faking or maybe you'll forget or you'll get poor. Whereas if we tie it to an event, then your reliability goes up a little bit.

0:59:12.8 CC: It could be. It could be. And actually, this thing about credibility of future rewards a lot of people have tackled. And my colleague here, Charlie Sprenger, who's a real terrific experimental economist, the way he did it was to give people post-dated checks when they left. So if you pick $10 today...

0:59:30.1 SC: Brilliant.

0:59:30.8 CC: You get a check 24th of September. And if you pick one month, it's October 24th. It's a little sus because sometimes banks will cash post-dated checks and what have you, but anyway, they were really cognizant about this. And another study by Paul Glimcher, who's a terrific neuroeconomist, what they did was they had people scan three or four sessions. And so by the time they did the second session, they had gotten their money from the first session.

0:59:58.7 SC: Okay, so they're believable.

0:59:59.1 CC: So that was a way to build up credibility.

1:00:00.8 SC: Yeah, okay, good.

1:00:02.5 CC: Yeah. Yeah, yeah. But it is a... This is kind of like the best way to win an argument is to run another regression or run another control experiment. So people have been very careful about this credibility thing. But it may be the episodicness somehow adds some vividness or some something. I mean, we need to ask the brain more.

1:00:23.6 SC: It does sound like this might tie into... I know you're doing recent work on burstiness in activity. Like, if we're doing something, we'll keep doing it for a while and then it'll just go away.

1:00:29.7 CC: Yeah. Yeah. Yeah.

1:00:35.7 SC: So what have you found out there?

1:00:38.3 CC: Okay, yeah. So that's a great segue. Thanks, Sean. So burstiness is... Well, let me tell you a story. So we have some gym data, and we have some data on about 1,500 days of gym attendance. These are people who joined the gym and then we have four years of data or so, sometimes less, sometimes more. And it's a lot of people, like 20,000 people. And so we were interested in whether you can predict when they go to the gym. Like do they form a habit of going regularly? And so my RA, Hong Ho, made a graph. I said, "For giving a talk purposes, make a graph of one person's calendar year of when they went to the gym every day." And so I looked at this graph and I thought, "Poisson non-homogeneity."

[laughter]

1:01:29.5 SC: Just what I would think of, of course.

1:01:31.0 CC: As one would. As one would.

1:01:33.0 SC: As one would.

1:01:33.5 CC: Yeah, yeah. And that's an extremely clumsy term for burstiness.

1:01:35.2 SC: Yeah.

1:01:39.4 CC: Okay. And so on the graph, what happens is the guy goes one day, the next day, next day, next day. So he's going one day apart three times. Then there's a seven-day gap. Then he goes the next day, one-day gap. Then he goes 13 days later. And so there's a lot of short gaps and a lot of long gaps. And it turns out that the kind of default model for thinking about these kind of stochastic processes is often a Poisson arrival process. And what Poisson non-homogeneity means is if the Poisson parameter... Poisson parameter, readers, is... A Poisson process is a gorgeous process invented by Mr. Poisson, which is French for fish. And it has a lovely, lovely structure, and it's characterized by one parameter. So there's a parameter tau, which is the mean of the distribution and its variance. So it's very easy to work with. The Gaussian, you have two parameters, and lots of other beautiful functions have two or three, but for some purposes, one is very handy. And we also use it in the cognitive hierarchy, by the way. So it looks like the distribution of levels in these games, like two-thirds of the average, happens to have a Poisson shape. It's the same Poisson, just completely different. It's just a good example of... I'm like a Platonic... Sometimes the same ideas show up powerfully in science in many different places. I hope so. Like God sometimes makes our life easier rather than harder. Anyway, so back to the calendar. So I found this beautiful book by a guy called Karsai that was about 200 pages on burstiness. And so burstiness is another name for these lots of smalls and lots of longs. And burstiness has a property that if you regress the percentage of one-day gaps against one day, the percentage of two against two days, against two, right, you regress the percentage of days of length tau against tau, but take the logs. Log percentage against log tau. And I did this on my computer after I got these data. I was so excited. It was 10 years ago. Claude couldn't help me. I basically counted by hand the size of the gaps and I made a plot. I was really excited. I'm discovering, I'm discovering. Here comes dopamine flowing. And I ran a regression. It was an R-squared of 0.97. And in social science, you only get that when you're regressing by mistake one thing against the same thing, or something that's really persistent like GDP. Quarterly GDP is last year's GDP times one plus some small perturbation.

1:04:22.6 SC: So sorry, just to interrupt, but for the listeners who might have missed the forest there for the trees, so you're saying that there's essentially a perfect kind of power law relation here in the frequency of the gaps between these events.

1:04:40.2 CC: Correct, correct. In the log frequency and the log length. Exactly. So if you... I mean, like I said, we just never... We never see this, you know.

1:04:48.9 SC: No.

1:04:49.3 CC: And so my view, especially after reading this Karsai book, which was just a gift, was, and again, this is not that common in economics, but was, if it's that lawful empirically, there must be a simple explanation.

1:05:07.4 SC: Yeah.

1:05:08.0 CC: And it turns out there's not just one simple explanation, there's a few.

1:05:11.8 SC: That's the problem with the simple relationship, yes.

1:05:13.9 CC: Correct. Exactly, exactly, exactly. So we have these four data sets and there's a couple of beautiful models we found in the literature that actually go back to 1954. So let me give you a quick tutorial to your beloved readers or listeners on queuing theory. So queuing theory is a branch of applied math developed, think like a lot of boom in operations research around World War II or thereabouts, which says if people arrive for some kind of service, these are the words that people still use, and the arrival rate is stochastic, but it's governed by some law like Poisson arrival, and then the service is stochastic, governed by some kind of law, what are the statistical properties of how long people wait to get served? What's the mean? What's the median? If the arrival rate is faster than the service rate, it goes to infinity. There's a lot of interesting applied questions, like if you're at Vons and there's more than three people in line, there's four, what they do is they'll open up another server. And so you can study if you have a certain server open-up rule, like how does that work? Most of the queuing is simplified if you use first-in, first-out, which assumes somebody arrives and nobody gets to cut in line. But it turns out starting in the 50s and then later, Barabási was a famous person working on this, who people may have heard of. He's written a popular book and done a lot of interesting things. And it turns out if you tweak queuing theory to what's called priority queuing, you can get a power law.

1:06:49.9 SC: Okay.

1:06:50.5 CC: And so priority queuing means it's not first-in, first-out. So let's say there's a line and with some probability p, you don't serve the person who's next in line, you pick somebody else.

1:07:03.0 SC: Okay.

1:07:03.5 CC: And there's three or four very beautiful forms of this. And the different forms of priority generate power laws with different exact numerical slopes. Like Barabási's form, the Cobham form 1954 is minus one and a half, minus three halves. Barabási is minus one. There's a couple of others that have slightly different... I mean, and this is all published in like statistical physics journals. It's just beautiful. Like I was just... I kind of want to, like maybe I should have stuck with physics after I majored in physics for one term and then gave up because it was too hard. It was too hard and there was no people in it. But anyway, it was extremely beautiful work. You have to make these nice approximations and it's sort of knowing what to take out in some ways, right? Anyway, so there was all this literature on what to do. Meanwhile, we have a priority-type model, but we hadn't thought of it as priority queuing at all. It's called Neural Autopilot. And we developed this, as the name suggests, to create a simple mathematical econ-style model of habit that we might be able to see in the brain. In fact, it was inspired by things that people had been writing about the brain. And the idea is that the brain toggles between modes. When you're in optimization mode, you go to a store, you look at all the things in the store, you think about the prices. People don't really do this, again, I mean, this is where we started out with utility maximization, but they do some approximation. Or if they go to the store at the same time, like the wine expert, they may actually know what all the different pickles are in the Armenian store, which there's about 100 in LA.

1:08:40.0 CC: So that's regular shopping, but it's very expensive. You have to look at all this stuff, you have to compare the prices, you have to make trade-offs, right? Do I buy the big jar even though I'm not gonna eat that many pickles? In habit mode, you do something much simpler. What you do is you retrieve a memory, what did I pick last time? And then you pick up one number, which is the reward reliability, which is basically, was it pretty much the same as last time? So it's not the reward prediction error, which is a workhorse idea from Wolfram Schultz and others in theoretical neuroscience. It's the absolute reward prediction error. It's like the sign. Has it been pretty good? If that's high, you don't buy the same thing again. Because if it's high, it's like, well, I don't know if I'm getting something that's as good as last time, right? If it's low, you just buy the same thing. So think how simple it is. What did I pick? What was its reward reliability? In fact, those might be closely coupled together in associative memory, so it's a very... And so the idea is in habit mode, you just think of these two numbers. You're in and out. You can multitask, right? So you're pushing your shopping cart, you're on the phone, you're grabbing Italian wedding soup as your favorite soup. You grab Italian wedding soup and blah, blah, blah, right? You can have a conversation. So, multitasking is a hallmark of habit, right? Because you have this freed up executive function. That's the idea. Anyway, so we thought, well, let's take this Neural Autopilot model and see if it can fit some of these power laws. So for example, maybe when people are going to the gym, sometimes they're in habit mode, and if they go to the gym one day, they're just gonna go again and again and again. And other times they break that habit, and maybe they're in a habit for a different activity, like running or binge-watching or binge podcast listening or something, right?

1:10:37.4 SC: Right.

1:10:37.6 CC: So they're not in the gym habit. And so we're able to show statistically, it's horribly inelegant compared to the statistical physics, I'm ashamed to admit, but that's the limits of my talent, and we're able to show that the Neural Autopilot model will also generate a lot of streaks of ones and then some long streaks. And it generates a slope of the power laws. The slopes in the power laws we saw in our data were mostly like minus two.

1:11:06.4 SC: Okay.

1:11:07.2 CC: Which happens to be a little bit higher than most of the beautiful mathematical ones that generate steeper slopes. And so this was a... Again, I think this is what interdisciplinary is good for.

1:11:24.6 SC: Yeah.

1:11:25.3 CC: It's like if I hadn't seen that picture and known what Poisson non-homogeneity was, which I learned from Peter Bossaerts because it's an idea used in finance to think about insider trading, right? It's like, you know, the trades don't always arrive in this Poisson way. You may get a burst of trading and then a lull and then a burst of trading. And that's either because information comes in lumps, you know, or in, or trading is self-generating. Like if I see a bunch of trades, the price moves and that gets my attention. Anyway, so a bunch of people had been developing this in finance. Peter Bossaerts infects me, that plants the seed in my hippocampus. I see this picture, I think burstiness. I go look for a book. I find [1:12:06.6] ____. Thank you, [1:12:07.2] ____. And then I go and talk at my MIT-Harvard sabbatical, I talk to Xavier Gabaix, who is an extremely brilliant macro-finance behavioral economist. And he wrote a paper on power laws and derived why city sizes satisfy a power law, which is a very old application from the '50s from Herb Simon. And I said to Xavier, "This topic still interesting?" right? Because no one in economics seems to be working on it. Like, he wrote two great papers and then just stopped. And he said, "Yeah, I couldn't get..." I said, "When this is so lawful, there must be a beautiful simple process." He goes, "Yeah, yeah, yeah, yeah. Exactly." So we're the two champions of power laws study in economics. I mean, his work was much more serious and I just was kind of dabbling on the empirical side. Anyway, that's what that's about.

1:12:59.3 SC: I mean, the... I'm diving into this myself because I'm writing a couple of books that involve these things.

1:13:02.5 CC: Yeah, yeah.

1:13:05.7 SC: The power laws are... On the one hand, they're everywhere. On the other hand, they're surprisingly controversial because people argue like...

1:13:12.1 CC: It's not quite a power law.

1:13:14.5 SC: "Is it really a power law, or is it a log-normal? You know, what is your mechanism?" But I think for sure it starts you down a path that makes you learn things. So I'm all in favor of people noticing power laws here and there.

[overlapping conversation]

1:13:24.0 CC: Yes. And in fact, we've just to... Sorry to echo your point, we wrote a second paper. So Gabaix has the only paper I know of that actually uses statistical inference to test whether it's really a power law or something else.

1:13:38.5 SC: Oh yeah, okay.

1:13:39.8 CC: And there's a few ways you can do it. You can compare power law with alternative specifications like log-normal. But his test is... I mean, these are really good econometricians, right? So what they're thinking of is not just, "Wow, that picture looks pretty good.98 R-squared," but could we reject power-lawness without a specific alternative? So they had this really elegant test. It's very delicate. He never published it. And so we're the first people to actually test that. And indeed, they're not quite power law.

1:14:15.8 SC: Not quite, yeah, yeah.

[laughter]

1:14:17.1 CC: Let's just whisper. Readers have to back up and listen again.

1:14:19.9 SC: Right.

1:14:20.7 CC: Click the minus 15 seconds button. What it is, is the log-normal fits a little better.

1:14:27.1 SC: Okay. But that is... You know, despite the fact that it comes across as a little bit persnickety statistician talk, it might have important implications for the generative mechanism, right?

1:14:36.8 CC: Absolutely, absolutely. Yeah, yeah, yeah... And this is really nerdy, but let me just finish on that. So and if you graph them, like power law and log-normal, what the log-normal differs is that when the lengths are very short, like one or two day gaps in the gym, one or two, three day gaps in the gym, there's a little bit fewer of those short lengths than would be on the power law straight line.

1:14:49.9 SC: Yeah.

1:15:03.0 CC: And then the tail of extremely high lengths, like 30 days they haven't gone to the gym, just kind of falls apart. It's what's called exponential cutoff in the... It just starts to not look like a straight line because there's not much data down there.

1:15:14.8 SC: Not much data, right, yeah, of course.

1:15:16.3 CC: It just fans out. But the log-normality might... I mean, your point is... I'm so glad we... I sent you this paper last night and you read it or thought about it, or at least it sparked the thought, because in psychology there's something called refractory period, which is basically recharging. Right? So, you know, after you eat and you're full, you don't eat again. Sex, exercise, sleep, you know, all these things, you don't expect to see a lot of really small things close to each other, right? So for example, I don't think this is true of the gym, but it might be. For example, after you go to the gym one day, your muscles are a little sore and you can't go for two more days. So there's a deep biological reason why you can't have short lags. Right?

1:16:06.1 SC: Right.

1:16:06.5 CC: Or we have data, for example, on people checking their phones, you know, and you just don't... You can't check it minute after minute after minute unless you're...

1:16:14.8 SC: I hope not, yeah.

1:16:16.1 CC: Yeah, I mean, it's bad enough as it is, right? But I think your point is really well taken, which is the hard work of comparing these things that look very close together may tell us something about a deeper mechanism that's not in our habit idea, and it's not in priority queuing, but maybe there's a way to kind of splice that in or to find a domain in which you think the refractory period may, you know, may bend the curve in some interesting way.

1:16:43.6 SC: And so the burstiness of our habits, is that something you can also tie into the neuroscience? You can sort of say, "Oh, it's this circuit in the brain that is responsible."

1:16:53.2 CC: We have not done that yet. I am not great on theoretical neuroscience. One of my skills I immodestly think is there's a lot of... I know a little bit about a lot of things, but there's some things I know nothing about, like consciousness, international taxation, and spike trains. But I know who knows.

1:17:15.5 SC: Right.

1:17:17.1 CC: And also the Neural Autopilot model as we kind of wrote it down, we've used it to explain some things like consumer choices, the gym, posting on social media, hasn't been like deeply tested in the way I would like from, in neuroscience. There are a lot of people who studied habit going back quite a few years. And I think the idea that the, that habit and more deliberative choice are fundamentally dissociated is well established. For example, in rodents, goal-directed... What we call goal-directed or deliberative or optimizing activity is in dorsomedial striatum, habit in dorsolateral striatum, and as well as, again, that's part of a circuit. It's not that habits are only there, right? That circuit connects to motor circuits and things. And there's some beautiful work by John O'Doherty, my colleague, and Bernard Balleine in Australia, where they actually... They lesion different parts of the brain. And you can either get rid of habits that rats have by lesioning the habit area, or you can get rid of goal-directed actions and make them more habitual by lesioning the other area. So the... You know, you can kind of turn on and off, at least in mice, you know, at least in rodents doing very simple, you know, pressing of buttons or swimming, they have these things where they swim through water and they climb onto a ramp, a platform. And so they're extremely simple tasks. They're a little bit like things humans do, like your ritual for making coffee in the morning, or you get in the car and you either, do you reach for the seatbelt first or do you turn the car on first? You know, there are sort of chained motor sequences that are similar, but a lot of how we're thinking about a habit is much more higher scale.

1:19:07.8 SC: I remember I walk to the office every day. My house is within walking distance. And one day I decided to walk a different route, and halfway through I realized I had no idea where I was.

1:19:18.6 CC: Yeah, exactly. It's like muscle memory, right?

1:19:22.4 SC: Didn't know what I was doing. Yeah.

1:19:23.1 CC: Procedural memory, muscle memory. Yeah, yeah. In fact, this is... I'm so glad you brought that up. So one bit of evidence for habits is what are called action slips. Right? So, for example, if you drive to work a certain way and then turns out the Olympics are in town, most people forget, they start driving to work and all of a sudden they're stuck. So that's called an action slip. And my favorite example of this I have on my slide deck, which is I was flying to Philadelphia to give a talk about habits, and the flight attendant said, "What would you like?" and gave me a snack, like a little cookie. "What would you like to drink?" I said, "I'd like a Sprite Zero with no ice. Sprite Zero, please, no ice." So she plops down ice, and then I put my napkin on top of the cookie and she said, "By the way, did you get your cookie?" So it was a double action slip, right, all within four seconds. I'm getting my ice and she forgot about the cookie.

1:19:53.7 SC: Autopilot.

1:20:23.1 CC: Yeah, right, because she's on autopilot.

1:20:25.6 SC: I mean, I guess this is... Maybe this is a good roundup final question here, even though it's completely unfair for you. Are there at least hypothetically ways we could go into the brain and change some of these things? Are there therapies, drugs, whatever? Can we become better at k-level thinking or less temporal discounting or whatever it would be? Is that a frontier?

1:20:55.3 CC: Yes. Oh, yeah, no, absolutely. I mean, if you think that's an unfair question, you should see my colleagues from the past 40 years.

[laughter]

1:21:02.8 SC: I can think of more unfair ones, but I'm trying to be nice.

1:21:04.7 CC: Yeah, that's not an unfair question. That's the idea of an unfair question, come on. Yes. So I'll give you a couple of examples. We worked on one study, but it wasn't really a knockout study, so I don't want to mention it, and it wasn't like a hard, conclusive result I would bet the farm on. So there's something called optogenetics, and one of my brilliant colleagues here, Viviana Gradinaru, works on that, and she was a student of Karl Deisseroth, who kind of invented it and is going to win a Nobel Prize in medicine any day now. And the idea is you can inject a virus into the brain, certain kinds of viruses that are light sensitive. And so in one experiment, there are cells in the brain that are encoding reward prediction errors. These are essentially the cells that they're in rats that Schultz, Dayan, and Montague discovered in 1997 in La Jolla. And so what happens is you train the rat to press a lever and it either gets a drink reward or not. And so you train it to press, and every time it gets a drink reward, you can see this little reward prediction error, like, "Oh, that was good. Oh, that was good. That was good." And so it learns that if you press the lever, you get something good. There's another lever that they press, and sometimes it gets salty water, which it hates. So it learns, "I'm not gonna press that." Then what you do is you optogenetically upregulate the reward prediction error neurons. I call this neural gaslighting. See what you think.

1:22:35.4 SC: I like that.

1:22:36.2 CC: You upregulate, so when it presses the lever and gets salty water, the brain neurons say, "That was great," because you've hijacked, you've convinced the brain that salty water is really good because you know where these reward prediction error neurons are.

1:22:52.5 SC: Yeah.

1:22:53.6 CC: And it doesn't get overridden by the sensory system. So you can trick the rat's brain into thinking it likes something that it really doesn't. And you can tell it doesn't because when you look at its face and so on, it drinks the salty water, it's like, "Brain, what were you doing telling me to drink that salty water? You're gaslighting me," right? I think I invented the term neural gaslighting, which is a good one.

1:23:15.9 SC: Which is a good one. Yeah.

1:23:16.8 CC: And here's another one that's a little less practical and interesting, but it just tells you something cool. So my academic granddaughter, Lucia Zhu, is a professor in Beijing, and she has a paper that exploits the fact that there are certain times when you're sleeping in which you can say things to people or play a sound, and people remember them and it will affect behavior.

1:23:20.6 SC: Okay.

1:23:47.6 CC: I can't remember the acronym for this. Sorry, listeners. Anyway, and so what she does is... But it has to be, and you have to really know a lot about sleep, and you have to do it in a sleep lab or with some equipment that knows when there's this sweet spot, because otherwise they'll wake up, or if they're in deep sleep, they won't encode it. And remember again, many of your readers will know, but when you're sleeping, the brain's not completely comatose.

1:23:59.8 SC: Right.

1:24:12.1 CC: There's neural replay, dreaming, obviously. Dreaming is like medicine for your brain. There's kind of defragging and interesting things happening. So the brain is doing something, it's just you're not conscious. Anyway, so what she does is she has people value different snack foods, similar to the study we talked about, Snickers, potato chips, yogurt. And then let's say they value a food for $3. Then she whispers the name of the food when they're sleeping. "Snickers. Snickers. Snickers. Snickers." And then they wake up and she says, "Did you hear anything when you were sleeping?" "Well, no, I was sleeping." But they value the foods that were treated, that were whispered about, and of course you control, so you whisper about all the different foods and compare when you weren't whispering, blah, blah, blah, they value them about 10% more. So I don't think this is a marketing technique. Well, who knows? Right? But it is an anecdotal answer to your question. Once we know something about circuitry, are there techniques to upregulate or train? And just to wind up, and also as you probably know, there's a lot of work on deep brain stimulation in Parkinson's. And there's a lot of interest in what's called transcranial magnetic stimulation, or TMS, which is low voltage current outside of the brain, but close, to treat depression and things like that. So lots of therapeutics are working along these lines. They just haven't been brought to economic decisions. I think there's a good chance if we worked on it, we could get people to be more patient. But it's one thing to do it in a short run of one experiment for one hour, and it's another thing to do it maybe at a sweet spot, like when children are three or four, and preferably you don't have to come in a lab and be in a scanner or have be whispering at night or any of those things. They're just proofs of concept, right?

1:26:21.2 SC: Make people more virtuous?

1:26:24.1 CC: Yes, my colleagues did that.

1:26:25.7 SC: Okay.

1:26:27.2 CC: So Alex Smith at Virginia Tech and Sheryl Ball used transcranial direct current stimulation, tDCS, to TPJ, the area we talked about for mentalizing. And they basically had people do these tasks where you have some money, you can keep some or give some to a charity. And then they vary the trade-offs.

1:26:37.7 SC: Yeah.

1:26:48.3 CC: Like, the charity gets very little, you get a lot; you get very little, charity gets a lot. And they test the rationality of how consistent they are and also whether they give more to the charity. And when you upregulate right temporal parietal junction, they're a little bit more generous and a little bit more rational. They're more consistent.

1:27:07.5 SC: You know what? I'm gonna call it a day there because that was an optimistic place.

1:27:13.5 CC: Yes, that's a knockout.

1:27:14.0 SC: Yeah, you scared me a little bit 10 minutes ago, but now you're making me think that there's an optimistic path forward in addition to all the wonderful science and economics and everything. So, Colin Camerer, thanks very much for being on the Mindscape podcast.

1:27:26.7 CC: That was a fantastic... My pleasure, Sean.

1:27:28.7 SC: Thanks.

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