5
Freud’s Dream
Games and the brain

The intention is to furnish a psychology that shall be a natural science: that is, to represent psychical processes as quantitatively determinate states of specifiable material particles, thus making those processes perspicuous and free from contradiction.

—Sigmund Freud, Project for a Scientific Psychology, 1895

Sigmund Freud really wanted to understand the brain.

He studied medicine and specialized in neurology. He planned to decipher the code linking the brain’s physical processes to the mysteries of the mind. In 1895, he outlined a project for “a scientific psychology,” in which mental states and human behavior could be explained materialistically, in terms of the physical interaction of nerve cells in the brain. But Freud found the brain science of the late 19th century too immature to link cranial chemistry to thought and behavior. So he skipped the brain and went straight to the mind, analyzing dreams for clues to the unconscious memories that manipulate mental life.

Others never even dreamed of achieving the “brain physics” that Freud envisioned. Many simply regarded the brain as off limits, declaring it to be a “black box” inaccessible to scientific scrutiny. These “behaviorists” decreed that psychology should stick to observing behavior, studying stimulus and response.



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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature 5 Freud’s Dream Games and the brain The intention is to furnish a psychology that shall be a natural science: that is, to represent psychical processes as quantitatively determinate states of specifiable material particles, thus making those processes perspicuous and free from contradiction. —Sigmund Freud, Project for a Scientific Psychology, 1895 Sigmund Freud really wanted to understand the brain. He studied medicine and specialized in neurology. He planned to decipher the code linking the brain’s physical processes to the mysteries of the mind. In 1895, he outlined a project for “a scientific psychology,” in which mental states and human behavior could be explained materialistically, in terms of the physical interaction of nerve cells in the brain. But Freud found the brain science of the late 19th century too immature to link cranial chemistry to thought and behavior. So he skipped the brain and went straight to the mind, analyzing dreams for clues to the unconscious memories that manipulate mental life. Others never even dreamed of achieving the “brain physics” that Freud envisioned. Many simply regarded the brain as off limits, declaring it to be a “black box” inaccessible to scientific scrutiny. These “behaviorists” decreed that psychology should stick to observing behavior, studying stimulus and response.

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature As the 20th century progressed, both Freudianism and behaviorism faded. The black box concealing the brain turned translucent as molecular medicine revealed some of its inner workings. Nowadays the brain is almost transparent, thanks to a variety of scanning technologies that produce images of the brain in action. And so the infant neuroscience that Freud abandoned over a century ago has now matured, nearly to the point of fulfilling his original intention. Freud could not have dreamed about merging neuroscience with economics, though, for he died before the rise of game theory. And even though they regarded game theory as a window into human behavior, game theory’s originators themselves did not imagine that their math would someday advance the cause of brain science. The original game theorists would not have predicted that game theory could someday partner with neuroscience, or that such a partnership would facilitate game theory’s quest to conquer economics.1 But in the late 1990s, game theory turned out to be just the right math for bringing neuroscience and economics together, in a new hybrid field known as neuroeconomics. BRAINS AND ECONOMICS One of the appealing features of game theory is the way it reflects so many aspects of real life. To win a game, or survive in the jungle, or succeed in business, you need to know how to play your cards. You have to be clever about choosing whether to draw or stand pat, bet or pass, or possibly bid nillo. You have to know when to hold ’em and know when to fold ’em. And usually you have to think fast. Winners excel at making smart snap judgments. In the jungle, you don’t have time to calculate, using game theory or otherwise, the relative merits of fighting or fleeing, hiding or seeking. Animals know this. They constantly face many competing choices from a long list of possible behaviors, as neuroscientists Gregory Berns and Read Montague have observed (in language

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature rather more colloquial than what you usually find in a neuroscience journal). “Do I chase this new prey or do I continue nibbling on my last kill?” Berns and Montague wrote in Neuron. “Do I run from the possible predator that I see in the bushes or the one that I hear? Do I chase that potential mate or do I wait around for something better?”2 Presumably, animals don’t deliberate such decisions consciously, at least not for very long. Hesitation is bad for their health. And even if animals could think complexly and had time to do so, there’s no obvious way for them to compare all their needs for food, safety, and sex. Yet somehow animal brains add up all the factors and compute a course of action that enhances the odds of survival. And humans differ little from other animals in that regard. Brains have evolved a way to compare and choose among behaviors, apparently using some “common currency” for valuing one choice over others. In other words, not only do people have money on the brain, they have the neural equivalent of money operating within the brain. Just as money replaced the barter system— providing a common currency for comparing various goods and services—nerve cell circuitry evolved to translate diverse behavioral choices into the common currency of brain chemistry. When you think about it, it makes a lot of sense. But neuroscientists began to figure it all out only when they joined forces with economists inspired by game theory. Game theory, after all, was the key to quantifying the fuzzy notion of economic utility. Von Neumann and Morgenstern showed how utility could be rigorously defined and derived logically from simple axioms, but still thought of utility in terms of money. Economists continued to consider people to be “rational” actors who would make behavioral choices that maximized their money or the monetary value of their purchases. Putting game theory into experimental action, though, showed that people don’t always do that. Money—gasp—turned out not to be everything, after all. And people turned out not to be utterly rational, but pretty darn emotional. Imagine that.

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature GAMES AND EMOTIONS You might think (and some people do) that game theory therefore becomes irrelevant to the real world of human social interaction, because people are not rational seekers of maximum utility, as game theory allegedly assumes. But while game theory is often described in that way, it’s not quite the right picture. Game theory actually only tells you what people would do if they were “rationally” maximizing their utility. That makes game theory the ideal instrument for identifying deviations from that notion of rationality, and many game theorists are happy with that. There is, however, another interpretation of what’s going on. Perhaps people really do maximize their utility—but utility is not really based on dollars and cents, at least not exclusively. And maybe “emotional” and “rational” are not mutually exclusive descriptions of human behavior. Is it really so irrational to behave in a way that makes you feel good, even if it costs you money? After all, the root notion of utility was really based on happiness, which is surely an emotional notion. Actually, most economists have long recognized that people are emotional. But when your goal is describing economics scientifically—and mathematically—acknowledging emotions poses a real problem, as Colin Camerer explained to me. “One of the things mainstream economists have said is, well, rationality is mathematically precise,” he said. “There’s one way to be rational. But there are a lot of ways to not be rational. So they’ve often used that as an excuse—anything can happen if people aren’t perfectly rational.” And if anything can happen, there’s no hope of finding a mathematical handle on the situation. “Economists have been a little defeatist about this—if you give up rationality, we’ll never be able to have anything precise.” This argument seems very much like the strategy of looking for lost keys only under the lamp post, because you couldn’t see them if they were anywhere else. If there’s only one sort of behavior (rational) that you can describe with your math, then that’s the behavior you will assume is correct. But Camerer and other

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature behavioral economists would rather first figure out what behavior is actually like. “Our view is to say, let’s find scientists who have been thinking about how brains actually work … and ask them for some help,” Camerer said. “It might be that even though, mathematically, there are lots of possible alternative models, the psychologists say, ‘oh, it’s this one.’”3 Of course, there was a time—as in Freud’s day—when psychologists couldn’t have provided very reliable answers to the questions about brain processes underlying human behavior. But with the rise of modern neuroscience, that situation has changed. Human emotions, for instance, are no longer as much of a mystery as they used to be. Scientists can now peer inside the brain to observe what’s going on when people experience contempt and disgust, fear or anger, empathy and love. Not to mention getting high on drugs. The driving forces of human decision making can now be traced to signals traveling between specific brain regions. Consequently human behavior, economic and otherwise, can now be analyzed in terms other than the economist’s “rational” and monetary notion of utility. In fact, it now seems likely that the brain measures utility not with dollars, but with dopamine. And that’s just one of the insights that the new discipline of neuroeconomics is providing into human economic behavior. ECONOMICS AND THE BRAIN I had encountered a few papers on neuroeconomics, but really didn’t get the big picture until 2003, when I visited Read Montague’s laboratory, at the Baylor College of Medicine in Houston. His “Human Neuro-imaging Laboratory” is a cutting-edge model of advanced technology in the service of science, with 100 or so computers, walls lined with plasma screen monitors, and state-of-the-art brain scanning machines. Montague explains it all with the speed of a Pentium processor, emphasizing the power of this new science to grasp human behavior in a precise way. “We’re quantifying the mind and human experience,” he said. “We’re turning feelings into numbers.”4

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature Montague began his scientific life in mathematics and biophysics, but foresight warned him that physics was not the wave of the future. While dabbling in a quantum chemistry project, his thoughts turned to the brain. Why not put math to use in comprehending cognition as well as the cosmos? He began to work on computational modeling of brain processes, and then proceeded to peer deep into real brains, exploiting a technology provided by physics to revolutionize psychology. Brain scanners are so familiar today that it’s hard to remember that a generation or so ago many scientists still considered the brain to be forever inscrutable. The behaviorist psychology of the early 20th century, proselytized by B. F. Skinner, had left its imprint on general beliefs about brain and behavior. Brains could not be observed in action, so only the behaviors that the brain produced mattered to science, the behaviorists contended. It turned out to be a misguided notion of both science and the brain. By the 1970s, imaginative new technologies had begun to make the brain transparent to clever neurovoyeurs. Radioactive atoms could be attached to critical molecules, allowing their activity to be observed in living brains, providing clues to what brains were doing while animals were behaving. Later methods dispensed with the radioactivity, using magnetic fields to jostle molecules in the blood that flowed through brain tissue. Ultimately this method, known as magnetic resonance imaging, or MRI, became widely used in medicine to “see” beneath the skin. And a variant of MRI technology was adopted by researchers in neuroscience to watch brains in action.5 “It can make a movie of the dynamic blood flow changes in every region of your brain,” Montague said. And blood flow has been shown to be tightly linked to neural activity—active neurons need nourishment, so that’s where the blood goes. You can watch how patterns of activity change in different parts of the brain as its owner performs various behaviors. Consequently, the old limits on which aspects of the brain could be studied and understood had dissolved, Montague explained, as a new wave of neuroscientists embraced the imaging

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature tools. “There’s a kind of sea change of belief in what you can and can’t explain,” he said. “People put people into scanners like this and do every manner of cognitive task, literally from having sex to thinking about the word sailboat. The experiments are working beautifully. I think the sky’s the limit.”6 A new scientific discipline to exploit these technological abilities seems to have emerged almost out of nowhere. The term neuroeconomics itself apparently first appeared in 2002.7 Before that, people like Montague had been referring to their studies as “neural economics.” In any event, the first attention-getting published paper in the new genre appeared in 1999, reporting a study by Paul Glimcher and Michael Platt of the Center for Neural Science at New York University. Glimcher and Platt had measured nerve-cell activity in the brains of monkeys performing a decision-making task. The results supported the notion that nervous activity reflects choice-making factors—that is, something like utility— that economists had already identified. Monkeys, of course, are not obsessed with money, but they do really enjoy getting squirts of fruit juice and can be fairly easily trained to perform all sorts of tasks for a juice-squirt reward. In the Platt-Glimcher experiment, all a monkey was required to do was switch its gaze from a cross on a screen to one of two lights. Looking at a light earned a squirt of juice. Looking at one of the lights, though, earned a bigger squirt than looking at the other. It didn’t take the monkey long to figure that out. (If I’m going to maximize my utility, the monkey obviously thought, I should look at the light on the right.) If the experimenters changed the high-reward squirt to the other light, the monkey caught on right away and preferred the new high-reward light. None of that was very surprising—similar experiments had been done before. But in this case, Platt and Glimcher also recorded the activity of a nerve cell in a region of the monkey brain that processes visual input and is involved in directing eye movement. (If you must know, the cell was in the lateral intraparietal cortex, or LIP.)

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature Now here’s the tricky part of the experiment. The lights on the screen were positioned so that only one of them was in the field of view accessible to the nerve cell being monitored. When the accessible light appeared, that nerve cell fired electrical impulses, as nerve cells do when stimulated. That nerve cell also boosted its activity as the monkey’s eyes moved to gaze at that light. No surprises there. But if that light happened to be the “high reward” light, the nerve cell fired its signals much more vigorously than when viewing the “low reward” light. To an old-school neurophysiologist, that would be surprising. For the actual visual stimulus was precisely the same in either case—a light comes on, and the eyes move to look at it. Somehow the neuron linked to that visual stimulus “knew” which light was the Big Gulp of juice dispensers. The monkey’s choice of looking toward the high-reward light (that is, the utility-maximizing choice) reflected a specific change in activity by a nerve cell in a specific region of the brain.8 Of course, that experiment was just a start, but it opened a lot of scientists’ eyes to the possibility of understanding economic decision making by looking inside the brain. The next year, neuroeconomics pioneers met in Princeton for the first major conference on the topic. Montague recalls the skepticism expressed by one of the economists attending, who saw no reason to believe that brain chemicals had anything to do with economics. “I said that is just complete poppycock,” Montague recalled. “If your brain doesn’t generate economic behavior, what kind of ghost horses do you believe in?” Even worse, the economist didn’t even think his remarks were particularly provocative. “I was stunned by that,” said Montague. “I might still be stunned by that.”9 Gradually, though, the idea of merging neuroscience and economics caught on, though perhaps more rapidly in neuroscience than economics. A special issue of Neuron, published in October 2002, included a passel of papers on human decision making, many of them exploring the new insights offered by neural economic studies. Montague and Berns’s paper in that issue argued that the

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature chemical dopamine was the brain’s currency for gauging the relative payoffs of potential behaviors. The paper noted various lines of evidence supporting the idea that a circuit of activity linking two parts of the brain—one at the front, behind the forehead, and another deep in the brain’s middle—helps govern choice making by producing more or less dopamine. Dopamine levels predict the likely reward associated with different choices, the evidence indicated. Dopamine had long been known as the brain’s chief pleasure molecule, linked to behavior that produces pleasant feelings. But it’s not merely pleasure that drives dopamine production. Actually, the brain’s dopamine currency seems tuned to the expectation of pleasure (or reward of some sort). Some of the brain’s dopamine-producing nerve cells are programmed to monitor the difference between expected and actual reward, Montague and Berns showed. If a choice produces precisely the predicted reward, the dopamine cells maintain a constant level of activity. When pleasure exceeds expectations, the cells squirt out dopamine like crazy. If the reward disappoints, dopamine production is curtailed. This monitoring system also takes timing into account—if dinner is delayed, dopamine is diminished. When the anticipated rewards aren’t realized, the dopamine monitoring system tells the brain to change its behavior. In this way the expectation of reward can guide a brain’s decisions. A critical point, noted by Montague and Berns, is that all brains are not alike. One person’s dream reward might be another’s horrific nightmare. Some people make a risky choice only when expecting a huge reward; others gamble for the fun of it. Part of the promise of neuroeconomics is its ability to identify such individual differences with brain scanning. In one experiment described by Montague and Berns, people chose either A or B on a computer screen and then watched a bar on the screen to see whether their choice earned a reward. (The bar recorded accumulated reward “points” as the game progressed.) As the game went on, the computer adjusted the rewards, based on the player’s choices. At first, choosing A raised the bar more, but

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature choosing A too often made B a better bet. When A’s payoffs dropped, some players noticed right away and quickly switched to choosing B more often. But others stuck with A, gambling that it would return to its previous high-payoff rate. It appeared that some brains are more inclined to take risks than others—some players play conservatively; others are risk-takers. (Actually, Montague said, more accurate labels for the two types of players would be “matchers” and “optimizers.” “I call them conservative and risky because you can make good jokes about that,” he said.) To me, it sounds more like they should be called “switchers” and “stickers.” But the labels don’t really matter. The most intriguing result from this experiment is the revelations from the brain scans. Sure enough, patterns of brain activity differed in the two groups, particularly in a small clump of brain cells called the nucleus accumbens. It’s a brain region implicated in drug addiction, and it’s more active in the “risk-taking” game players (the stickers). The neatest thing, though, is that you can tell who the risk takers and play-it-safers are from their brain scans just after the very beginning of the game, even while their behaviors are still identical. This is the sort of evidence that destroys the old behaviorist position that behavior is the only thing that matters (or that you can know). Early in the game, two players can behave identically, making exactly the same choices. Yet by looking into their brains you can see differences that allow you to predict how they will play later, when the payoff rate changes. “The people that ended up on average being risky are different from these people right away—nobody even jumps categories,” Montague told me. Even more intriguing, there appears to be a genetic difference between the two groups as well. So neuroeconomics thus offers economists a tool they had not possessed before, giving hope that by getting inside people’s heads, science might really be on the road to finding the Code of Nature that governs human behavior.

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature WHOM DO YOU TRUST? An important advance along that road came in 2003 with the publication of a paper in the journal Science by researchers at Princeton University. In a study by Alan Sanfey and colleagues, participants in an experiment played the ultimatum game, one of the favorites of behavioral game theorists. It’s kind of like a TV game show contest in which you are given a lot of money, but you have to share your windfall with a stranger. Suppose you get $100. You then offer the stranger part of the money and keep the rest— unless the stranger refuses your offer. Then you have to give all the money back, and nobody wins anything. In theory, the stranger should take any offer, no matter how small, in order to get something rather than nothing. Therefore, a game theorist might conclude, you should offer a low amount— $10, say, or even $1—so that you will then walk away with the most money possible. But in practice, most strangers reject low offers. If you offer $10, for instance, you’re much more likely to walk away with zero than $90, as the stranger will probably reject your offer just to punish you, even at personal expense. Consequently people typically share more generously—offering 40 to 50 percent of the prize, say—in anticipation of an angry rejection of an unfair offer. So this is another case where naive game theory, in assuming that everybody will maximize their money, makes an incorrect prediction, as many economic experiments had already established. The Princeton study went further, though, by scanning the brains of the strangers who were considering whether to accept the offer from the other participant. In this case, the prize was only $10— science doesn’t have budgets like Who Wants to Be a Millionaire?— but the principle was the same. If the first player offered only $1 or $2, the offer was usually rejected. But not always. And you could tell who was likely to accept or reject a low offer by watching what went on inside their brains. Stronger brain activity in the front part of a brain region known as the insula (an area known to be associated with negative

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature emotions, such as anger and disgust) was common in players who were more likely to reject low offers. Another brain structure—the anterior cingulate cortex—also showed increased activity in those who rejected unfair offers. That region is known to be involved in monitoring conflict—in this case, the conflict between the choice of punishing a cheapskate or turning away money. “Unfair treatment … can lead people to sacrifice sometimes considerable financial gain in order to punish their partner for the slight,” Sanfey and his collaborators reported in Science.10 In a commentary on that paper, Colin Camerer noted that it showed how the tenets of basic game theory do not always hold— people do not always act totally in their own self-interest (that is, maximizing their money), and all the players in a “game” therefore are not always trying to do the best they can do, as assumed in the underlying basis for a Nash equilibrium. But behavioral game theory, Camerer noted, can relax these assumptions and still learn a lot about human behavior. The neuroeconomics enterprise, in other words, is a powerful tool for developing behavioral game theory insights into how real people make choices. Montague’s subjects at Baylor, for instance, play similar behavioral games that reveal the quirks of human economic behavior. In one such game—a task for testing trust—Player 1 is given $20 and is allowed to keep some of it and put the rest in a virtual pot, where the amount is then tripled. If Player 1 keeps $10 and donates $10, the sum in the pot becomes $30. Player 2 then gets to split the pot with Player 1—or take it all. “If you split it 15-15, then in a sense you’ve repaid the trust,” said Montague. But if you take $29 and leave $1, Player 1 is not likely to offer much in the next round of the game. At any point in the game, one player or the other could decide to keep all the money, so the logical move is to take it all as soon as possible, before the other player does. But in fact, players typically trust each other not to be so selfish—although some are more trusting, and some more selfish, than others. Traditional economists were not surprised at the results of such games. In the 1980s, game theory had fueled the rise of “experi-

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature mental economics” in which such deviations from pure self-interest showed up regularly. What’s new in neuroeconomics is eavesdropping on the players’ brains via the MRI scanners while the games are in progress. Montague’s lab is particularly well equipped for this sort of thing, with a pair of scanners, one each in two rooms separated by the scientists’ observing station. The scientists watch as computers record the brain activity of players deciding how to move or how to react to another player’s move. “You can see what went on in the behavior. You can back up and look at their intent to act badly or their intent to invest more,” Montague said. “It allows us to cross-correlate what’s going on in the two brains. I think it’s cool. I think it’s an obvious way to study social interactions.”11 Neuroeconomics does not always require scanning, though. Paul Zak, director of the Center for Neuroeconomics Studies at Claremont Graduate University in California, sometimes uses blood tests instead of brain scans. He can relate variant economic behaviors to levels of certain hormones. In one of Zak’s versions of the trust game, players communicate via computer. One player, given $10, offers some of it to another player, who is paid triple the amount offered. (So if Player 1 offers $5, Player 2 gets $15). Player 2 then can take it all, or give part of it back to Player 1. But in this version of the experiment, the game ends after just one round. There’s no incentive to earn trust so as to get more money the next time around. So standard game theory suggests that Player 2 would take all the money, having nothing to gain by giving some back. But Player 1, anticipating that move, should therefore offer none of the money to begin with. Nevertheless, many players defy naive game theory and show at least some trust that the other player will play fair. About half of the first-movers offer some money (suggesting that they are trusting souls), while three in four of the responders give some back (suggesting that they are trustworthy). Once again, the intriguing thing about such games is finding out what’s behind the differences in individual behavior. It turns out that among the trustworthy players, blood tests revealed higher

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature levels of oxytocin, a hormone linked to pleasure and happiness. Apparently the trusting gesture of the first player, by offering some money, elicits a positive hormonal response. “It tells us that people are very much responsive to their environment,” Zak told me when I visited him at Claremont. “People who got a positive signal had a nice positive happy hormone response, and their behavior reflects that.”12 Zak believes that the relationship between trust and oxytocin is central to understanding many of the world’s economic ills. Oxytocin is linked to happiness, and the countries where people report high levels of happiness are also countries where people report high degrees of trust. Trust levels, in turn, are a good indicator of a country’s economic well-being. “Trust is among the biggest things economists have ever found that are related to economic growth,” Zak said. HOMO NEUROECONOMICUS For all of its intriguing findings, neuroeconomics doesn’t excite everybody, like the economist who perplexed Montague by not caring about the brain. From the perspective of economists like that one, neuroeconomics probably doesn’t have much to offer. To them, it only matters what people do; it doesn’t matter which part of the brain is busy when they do it. Neuoreconomists, though, want more than a mere description of economic decision making. They want the Code of Nature, the scientific understanding of humanity sought by 18th-century thinkers such as David Hume and Adam Smith. “The more ambitious aim of neuroeconomics,” writes neuroeconomist Aldo Rustichini, “is going to be the attempt to complete the research program that the early classics (in particular Hume and Smith) set out in the first place: to provide a unified theory of human behavior.”13 Rustichini, of the University of Minnesota, points out that Adam Smith’s great works—Theory of Moral Sentiments and Wealth of Nations—were part of a grand plan to codify the nature of

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature human civilization, to explain how selfish individuals manage to cooperate sufficiently well to establish elaborate functioning societies. Smith’s basic answer was the existence of sympathy—the ability of one human to understand what another is feeling. Modern neuroscience has begun to show how sympathy works, by identifying “mirror neurons,” nerve cells in the brain that fire their signals both in performing an action and when viewing someone else performing that same action. Other neuroscientific studies have identified the neural basis of both individual behavioral propensities and collective and cooperative human behavior. Scientists scanning the brains of players participating in a repeated Prisoner’s Dilemma game, for instance, have identified regions in the brain that are active in players who prefer cooperating rather than the “purely rational” choice to defect.14 Another study used a version of the trust game to examine the brains of people who punish those who play uncooperatively (by keeping all the money instead of returning a fair share). In this game, players who feel cheated may assess a fine on the defector (even though they must pay the price of reducing their own earnings by half the amount of the fine they impose). People who choose to fine the defector display extra activity in a brain region associated with the expectation of reward. That suggests that some people derive pleasure from punishing wrongdoers—the payoff is in personal satisfaction, not in money. In the early evolution of human society, such “punishers” would serve a useful purpose to the group by helping to ostracize the untrustworthy noncooperators, making life easier for the cooperators. (Since this punishment is costly to the individual but beneficial to the group as a whole, it is known as “altruistic punishment.”)15 Such studies highlight an essential aspect of human behavior that a universal Code of Nature must accommodate—namely that people do not all behave alike. Some players prefer to cooperate while others choose to defect, and some players show a stronger desire than others to inflict punishment. A Code of Nature must accommodate a mixture of individually different behavioral ten-

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature dencies. The human race plays a mixed strategy in the game of life. People are not molecules, all alike and behaving differently only because of random interactions. People just differ, dancing to their own personal drummer. The merger of economic game theory with neuroscience promises more precise understanding of those individual differences and how they contribute to the totality of human social interactions. It’s understanding those differences, Camerer says, that will make such a break with old schools of economic thought. “A lot of economic theory uses what is called the representative agent model,” Camerer told me. In an economy with millions of people, everybody is clearly not going to be completely alike in behavior. Maybe 10 percent will be of some type, 14 percent another type, 6 percent something else. A real mix. “It’s often really hard, mathematically, to add all that up,” he said. “It’s much easier to say that there’s one kind of person and there’s a million of them. And you can add things up rather easily.” So for the sake of computational simplicity, economists would operate as though the world was populated by millions of one generic type of person, using assumptions about how that generic person would behave. “It’s not that we don’t think people are different—of course they are, but that wasn’t the focus of analysis,” Camerer said. “It was, well, let’s just stick to one type of person. But I think the brain evidence, as well as genetics, is just going to force us to think about individual differences.” And in a way, that is a very natural thing for economists to want to do. “One of the most central and interesting things in economics is specialization and division of labor,” Camerer observed. “And so loosely speaking, the more individual difference there is, the better that might be for the economy—as long as you get people in the right jobs. And so knowing more about individual differences could be very important for areas like labor economics, where one of the central questions is are you matching the right workers to the right jobs.”16

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A Beautiful Math: John Nash, Game Theory, and the Modern Quest for a Code of Nature Zak, who has also performed studies to localize the brain’s computing of utility, notes that such work revolutionizes the kinds of questions that economists can study. “In economics we generally think of this utility function as pretty much uniform across individuals,” he said. “Now we can ask all kinds of questions about that. How stable is it, how different is it across people, why do you prefer coffee and I prefer tea? What if the price of coffee went up twice as much, what if you haven’t drunk coffee in two weeks? Do you value it more, do you value it less? These are really basic questions that may affect things like how things are priced in the market and it may affect how we design laws.”17 Yet while neuroeconomics may provide the foundation for understanding individual behavior and differences, it cannot alone provide the Code of Nature, or a science of human behavior like Asimov’s psychohistory. History comprises the totality of collective human behavior in various forms of social interaction— politically, economically, and culturally. It’s in understanding human culture that science must seek a Code of Nature, and game theory provides the best tool for that task.