Neuroscience, game theory, monkeys - Colin Camerer

[Applause] I'm going to talk about the strategizing brain. We're going to use an unusual combination of tools from Game Theory and Neuroscience to understand how people interact socially when value is on the line.

So, game theory is a branch of originally Applied Mathematics, used mostly in economics, political science, and a little bit in biology, that gives us a mathematical taxonomy of social life. It predicts what people are likely to do and believe others will do in cases where everyone's actions affect everyone else. That's a lot of things: competition, cooperation, bargaining, games like hide-and-seek and poker.

Here's a simple game to get us started: everyone chooses a number from zero to 100. We're going to compute the average of those numbers, and whoever's close to two-thirds of the average wins a fixed prize. So, you want to be a little bit below the average number but not too far below, and everyone else wants to be a little bit below the average number as well.

Think about what you might pick as you're thinking. This is a toy model of something like selling in the stock market during a rising market, right? You don't want to sell too early because you miss out on profits, but you don't want to wait too late, when everyone else sells, triggering a crash. You want to be a little bit ahead of the competition, but not too far ahead.

Okay, here's two theories about how people might think about this, then we'll see some data. Some of these will sound familiar because you probably are thinking that way. I'm using my brain theory to see, okay, a lot of people say, "I really don’t know what people are going to pick, so I think the average will be 50." They're not being really strategic at all, and I'll pick two-thirds of 50—that's 33.

That's the start. Other people, who are a little more sophisticated, using more working memory, say, "I think people will pick 33 because they're going to pick a response to 50, and so I'll pick 22," which is two-thirds of 33. They're doing one extra step of thinking—two steps; that's better.

Of course, in principle, you could do three, four, or more, but it starts to get very difficult. Just like in language and other domains, we know that it's hard for people to parse very complex sentences with a kind of recursive structure. This is called the cognitive hierarchy theory, by the way, something I've worked on and a few other people, and indicates some kind of hierarchy along with some assumptions about how many people stop at different steps and how the steps of thinking are affected by lots of interesting variables and variance, as we'll see in a minute.

A very different theory, a much more popular one and an older one, due largely to John Nash of A Beautiful Mind fame, is what's called equilibrium analysis. So if you've ever taken a game theory course at any level, you will have learned a little bit about this. Equilibrium is a mathematical state in which everybody has figured out exactly what everyone else will do, and it's a very useful concept, but behaviorally it may not exactly explain what people do the first time they play these types of economic games or in situations in the outside world.

In this case, the equilibrium makes a very bold prediction: everyone wants to be below everyone else; therefore they'll play zero. Let's see what happens. This experiment has been done many, many times. Some of the earliest ones were done in the '90s by me, Ros, Marine, Nagel, and others.

This is a beautiful data set of 9,000 people who wrote into three newspapers and magazines that had a contest. The contest said, "Send in your numbers, and whoever is close to two-thirds of the average will win a big prize." As you can see, there's so much data here; you can see the spikes very visibly. There's a spike at 33—those are people doing one step. There is another spike visible at 22, and notice, by the way, that most people pick numbers right around that; they don't necessarily pick exactly 33 and 22, there's something a little bit noisy around it, but you can see those spikes in the data.

There's another group of people who seem to have a firm grip on equilibrium analysis because they're picking zero or one, but they lose, right? Because picking a number that low is actually a bad choice if other people aren't doing the equilibrium analysis as well. So they're smart but poor.

Okay, where are these things happening in the brain? One study by Corel and Nagle gives a really sharp interesting answer. So they had people play this game while they're being scanned in fMRI, and in two conditions: in some trials, they're told you’re playing another person who's playing right now, and we're going to match up your behavior at the end and pay you if you win. In the other trials, they're told you're playing a computer; they're just choosing randomly.

So what you see here is a subtraction of areas in which there's more brain activity when you're playing people compared to playing the computer. You see activity in some regions we've seen today: medial prefrontal cortex, dorsomedial, however, up here, ventromedial prefrontal cortex, anterior cingulate—an area that's involved in lots of types of conflict resolution, like if you're playing Simon says—and also the right and left temporal parietal junction. These are all areas which are fairly reliably known to be part of what's called a theory of mind circuit or mentalizing circuit. That is, it's a circuit that's used to imagine what other people might do.

This is one of the first studies to see this tied into game theory. What happens with these one-step types? We classify people by what they picked, and then we look at the difference between playing humans versus playing computers, which brain areas are different active. On the top, you see the one-step players; there's almost no difference. The reason is they're treating other people like a computer, and the brain is too.

The bottom players see all the activity in dorsomedial PFC, so we know that those two-step players are doing something differently. Now, if you were to step back and say what can we do with this information, you might be able to look at brain activity and say this person is going to be a good poker player or this person's socially naive, and we might also be able to study things like the development of adolescent brains once we have an idea of where this circuitry exists.

Okay, get ready. I'm G to say I'm saving you some brain activity because you don't need to use your hair detector cells. You should use those cells to think carefully about this game. This is a bargaining game: two players who are being scanned using EEG electrodes are going to bargain over one to six. If they can do it in 10 seconds, they're going to actually earn that money. If 10 seconds goes by and they haven't made a deal, they get nothing—that's kind of a mistake together.

The twist is that one player on the left is informed about how much on each trial there is. They play lots of trials with different amounts each time—in this case, they know there's $4. The uninformed player doesn’t know, but they know that the informed player knows, so the uninformed player's challenge is to say, "Is this guy really being fair, or are they giving me a very low offer in order to get me to think that there’s only one or two dollars available to split?" In which case, they might reject it and not come to a deal.

So there's some tension here between trying to get the most money and trying to go the other player into giving you more. The way they bargain is to point on a number line that goes from zero to $6, and they're bargaining over how much the uninformed player gets, and the informed player is going to get the rest. So this is like a management-labor negotiation in which the workers don’t know how much profit the privately held company has, right? And they want to maybe hold out for more money, but the company might want to create the impression that there's very little to split. "I'm giving you the most that I can."

First, some behavior: a bunch of the subject pairs, they play face-to-face. We have some other data where they play across computers; that's an interesting difference as you might imagine. But a bunch of the face-to-face pairs agree to divide the money evenly every single time. Boring! It’s just not interesting. Early, it's good for them; they make a lot of money. But we’re interested in can we say something about when disagreements occur versus when they don’t occur?

So this is the other group of subjects who often disagree. They have a chance of bickering and disagreeing and end up with less money. They might be eligible to be on Real Housewives—the TV show. Okay, you see on the left when the amount to divide is one, two, or three, they disagree about half the time, and when the amount is four, five, six, they agree quite often.

This turns out to be something that's predicted by a very complicated type of game theory. You should come to graduate school at Caltech and learn about it. It's a little too complicated to explain right now, but the theory tells you that this shape kind of should occur; your intuition might tell you that too.

Now I'm going to show you the results from the EEG recording. Very complicated! The right brain schematic is the uninformed person, and the left is the informed. Remember that we scanned both brains at the same time, so we can ask about time-synced activity in similar or different areas simultaneously. Just like if you wanted to study a conversation, and you were scanning two people talking to each other, you'd expect common activity in language regions when they’re actually kind of listening and communicating.

So the arrows connect regions that are active at the same time, and the direction of the arrows flows from the region that’s active first in time, and the arrowhead goes to the region that's active later. So in this case, if you look carefully, most of the arrows flow from right to left. That is, it looks as if the uninformed brain activity is happening kind of first, and then it’s followed by activity in the informed brain.

By the way, these are trials where deals were made. This is from the first two seconds. We haven't finished analyzing this data, so we're still peeing in, but the hope is that we should be able to say something in the first couple of seconds about whether they'll make a deal or not, which could be very useful in thinking about avoiding litigation and ugly divorces and things like that—those are all cases in which a lot of value is lost by delay and strikes.

Here’s the case where the disagreements occur: you can see it looks different than the one before. There's a lot more arrows; that means that the brains are kind of synced up more closely in terms of simultaneous activity, and the arrows flow clearly from left to right. That is, the informed brain seems to be kind of deciding we're probably not going to be able to make a deal here, and then later there's activity in the uninformed brain.

Next, I'm going to introduce you to some relatives. They're hairy, smelly, fast, and strong. You might be thinking back to your last Thanksgiving—maybe if you had a chimpanzee with you. Charles Darwin and I, we broke off in the family tree from chimpanzees about five million years ago. They're still our closest genetic kin; we share 98.8% of the genes. We share more genes with them than zebras do with horses, and we're also their closest cousin. They have more genetic relation to us than to gorillas.

So how humans and chimpanzees behave differently might tell us a lot about brain evolution. So, this is an amazing memory test from the Primate Research Institute in Ngoya, Japan, where they’ve done a lot of this research. This goes back quite a way; they're interested in working memory. The chimp is going to see—watch carefully! They're going to see a 200-millisecond exposure— that's fast! That’s eight movie frames of numbers: 1, 2, 3, 4, 5. Then, they disappear, and they’re replaced by squares, and they have to press the squares that correspond to the numbers from low to high to get an apple reward.

Let’s see how they can do it! This is a young chimp; the young ones are better than the old ones, just like humans, and they’re highly experienced, so they’ve done this thousands and thousands of times. Obviously, there’s a big training effect, as you can imagine. You can see they’re very blasé and kind of effortless. Not only can they do it very well, they do it, you know, in a sort of lazy way.

Right? Who thinks you could beat the chimps? Wrong! We could try. We'll try—maybe we'll try.

Okay, so the next part of the study I'm going to zip through is based on an idea of Tetsuro Sawa. He had a bold idea that he called the cognitive trade-off hypothesis. We know chimps are faster and stronger; they’re also very obsessed with status. His thought was maybe they’ve preserved brain activities and they practiced them in development that are really important to them to negotiate status and to win, which is something like strategic thinking during competition.

So, we’re going to check that out by having the chimps actually play a game by touching up two touch screens. The chimps are actually interacting with each other through the computers; they’re to press left or right. One chimp is called a matcher; they win if they press left, left—like a seeker finding someone in hide-and-seek—or right right. The mismatch wants to mismatch; they want to press the opposite screen of the chimp, and the rewards are apple cube rewards.

So here’s how game theorists look at these data. This is a graph of the percentage of times the matcher picked right on the x-axis, and the percentage of times they picked right by the mismatch on the y-axis. Okay, so a point here is the behavior by a pair of players—one trying to match, one trying to mismatch. The neat square in the middle actually indicates NE, CH, and Q. Those are three different theories, Nash, equilibrium, and others tell you what the theory predicts is that they should match 50/50.

Because if you match—if you play left too much, for example, I can exploit that if I’m the mismatch by then playing right—and as you can see, the chimps, each chimp is one triangle, are kind of circled around, hovering around that prediction. Now, we move the payoffs. We're actually going to make the left/left payoff for the matcher a little bit higher. Now they get three apple cubes—game theoretically, that should actually make the mismatch behavior shift.

What happens is the mismatch will think, "Oh, this guy's going to go for the big reward, and so I'm going to go to the right to make sure he doesn't get it." Okay, and as you can see, their behavior moves up in the direction of this change in the Nash equilibrium. Finally, we change the payoffs one more time; now it’s four apple cubes, and their behavior again moves toward the Nash equilibrium.

It’s sprinkled around, but if you average the chimps out, they’re really, really close within 0.1. They’re actually closer than any species we've observed. What about humans? You think you’re smarter than a chimpanzee? Here’s two human groups in green and blue. They’re closer to 50/50; they’re not responding to payoffs as closely, and also if you study their learning in the game, they aren't as sensitive to previous rewards. The chimps are playing better than the humans—better in the sense of adhering to game theory.

These are two different groups of humans from Japan and Africa; they replicate quite nicely—none of them are close to where the chimps are.

Okay, so here are some things we learned today: people seem to do a limited amount of strategic thinking using theory of mind, with some prary evidence from bargaining that early warning signs in the brain might be used to predict whether there’ll be a bad disagreement that costs money, and chimps are better competitors than humans as judged by game theory. Thank you!

[Applause]