Is Behavioral Economics Doomed?

Front matter

Davıp K. LEVINE

Is Behavioral Economics Doomed?

The Ordinary versus the Extraordinary

Introduction

Does Economic Theory Work?

Why Is the World so Irrational?

Does Economic Theory Fail?

You Can Fool Some of the People...

Behavioral Theories I: Biases and Irrationality Behavioral Theories II: Time and Uncertainty Learning and Friends

Conclusion: Psychology, Neuroscience and Economics References

Index

To Milena Davidson-Levine and Catharina Tilmans

Acknowledgements

I owe an immeasurable intellectual debt to my coauthors Michele Boldrin, Drew Fudenberg, Salvatore Modica, Zacharias Maniadis, Tom Palfrey, and Jie Zheng with whom I’ve worked, discussed and debated the issues discussed here for many years. Tim Sullivan encouraged me to write this up in the form of a book, and took the time to read and comment on the first draft. Rupert Gatti, economics editor of Open Book Publishers, Alessandra Tosi and two exceptional referees have enormously improved that original draft. Juan Block proofread the book — for sense as well as typos — and provided the index.

This book originated as a Max Weber lecture presented at the European University Institute. Much of it was written while on sabbatical leave in the Economics Department there. I am grateful to the EUI and the Economics Department. I also owe a special debt of gratitude to the Max Weber program and to Ramon Marimon, Karin Tilmans and the Weber fellows for the invitation to speak, for a very constructive presentation, and for encouragement and assistance in writing up the lecture.

I have presented variations of this lecture in various venues including FUR, the NYU Experimental Workshop, the Neuroeconomics Meetings and the Milan Neuroeconomics Conference. I am grateful to Glenn Harrison, Guillame Frechette, and Colin Camerer for those invitations and to them and the meeting participants for helpful comments and criticism. Like Guillame and Colin, Rosemarie Nagel disagrees with practically everything written here — but her constant provocation has resulted in a much more coherent book. In the other dimension — Charlie Plott’s work in a direction similar to mine has been an example and an inspiration.

Although we may disagree — hopefully with respect — I could not have written this book without my many behavioral, neuroscientific and psychological friends. George Ainslie, Gary Charness, Ernst Fehr, Paul

Glimcher, Len Green, Joel Myerson, David Laibson, Camillo Padoa- Schioppa, Drazen Prelec, Aldo Rustichini and Klaus Schmidt have through their careful research contributed to my understanding of behavioral economics.

Iam also grateful to my daughter Milena Davidson-Levine, to Catharina Tilmans and to my many students both graduate and undergraduate at Washington University in St. Louis. To the outstanding faculty there and the fine research organization at the Federal Reserve Bank of St. Louis I am also indebted for constant feedback and support.

Finally, I would like to thank the National Science Foundation and grants SES-03-14713 and SES-08-51315 for financial support.

l. Introduction

Under these conditions, the erotic relation seems to offer the unsurpassable peak of the fulfillment of the request for love in the direct fusion of the souls of one to the other. The boundless giving of oneself is as radical as possible in its opposition to all functionality, rationality, and generality. It is so overpowering that it is treated

“symbolically”: as a sacrament. The lover realizes himself to be rooted in the kernel of the truly living, which is eternally inaccessible to any rational endeavor. He knows himself to be freed from the cold skeleton hands of rational orders, just as completely as from the banality of everyday routine. Max Weber, 1958

Even Max Weber — one of the early proponents of the social analysis of rational man -recognized the essential irrationality of emotions such as love. Today it has become so very fashionable to criticize economic theory for focusing too much on rationality and ignoring the imperfect and emotional way in which decisions are reached in the “real world.” Psychologists and other social scientists have been especially vocal in their dismay. A bright new group of behavioral economists has picked up the criticism:

Economics traditionally conceptualizes a world populated by calculating, unemotional maximizers that have been dubbed Homo economicus. The standard economic framework ignores or rules out virtually all the behavior studied by cognitive and social psychologists. This “unbehavioral” economic agent was once defended on numerous grounds: some claimed that the model was “right”; most others simply argued that the standard model was easier to formalize and practically more relevant. Behavioral economics blossomed from the realization that neither point of view was correct. (Thaler and Mullainathan, 2010)

The authors go on to point out how modern economics is based on a foundation of sand.

The standard economic model of human behavior includes three unrealistic traits — unbounded rationality, unbounded willpower, and unbounded selfishness — all of which behavioral economics modifies.

Those who have read about — and who has not? — the current economic crisis may wonder indeed just how rational an economic man or woman might be. Behavioral economics has become the modern rage. Is, therefore, rational economic man — homo economicus — dead? Has the economics profession moved on to recognize the true irrationality of humankind? Nothing could be further from the truth.

Strangely, the criticisms that have caused behavioral economics to blossom are nothing new. Writing in 1898 Thorstein Veblen wrote sarcastically of rational economic man as a lightning calculator of pleasures and pains, who oscillates like a homogenous globule of desire of happiness under the impulse of stimuli.

Students of economic history can argue about whether Veblen’s description of homo economicus is an accurate reflection of economics as it was practiced then — it is definitely not an accurate reflection of economics as it is practiced today. For starters, while mainstream economics does indeed presume unlimited self-control, it does not presume unlimited rationality or unbounded selfishness. The paradigmatic man (or more often these days woman) in modern economics is that of a decision-maker beset on all sides by uncertainty. Most important, the central focus of economics is on how successful we are in coming to grips with that uncertainty.

Remarkably, for a long period of time during the 1960s and 1970s, irrational economic man dominated economics. It was the abysmal failures of the “neoclassical synthesis” leading to absurd and costly failures of economic policy — I am old enough to remember waiting in long lines to buy gasoline — that led to the modern and much-criticized theory of rational expectations. The fact is that irrational economic man is a poorer description of how we behave than that of a “lightning calculator of pleasures and pains.” As Robert Lucas wrote in 1995, in many ways the rational expectations model was a reaction to

[t]he implicit presumption in these... models [of irrational man]... that people could be fooled over and over again.

Modern economics is not the theory imagined by critics — including apparently some Nobel Prize winning economists — who are unfamiliar with it. The theory used by working economists is far more sophisticated and successful than is generally imagined. The fact that policy makers choose to ignore our warnings does not make us wrong. Weaknesses in economic analysis exist — but bear little connection to those cited by critics.

1. Introduction 3

My objective in this volume is to set the record straight by explaining some of the true successes and failures of both economics and behavioral economics.

To understand whether or not behavioral economics is doomed is to first ask the question whether mainstream economics has failed. If it has not, then surely behavioral economics is doomed. And mainstream economics has not failed. Existing economic theory in those situations of greatest interest to economists makes strong and robust predictions. Those predictions are borne out by the facts — in the laboratory as well as in the field.

In some situations less central to economics the theory makes weak predictions. These are also borne out by the facts — but the theory is less useful as it fails to narrow down the range of possibilities. It is here — in strengthening existing theory — that there exists a potential for behavioral ideas. Indeed — long before the term “behavioral economics” existed —- many of the ideas discussed by “behavioral economists” had already been incorporated into mainstream economic models. Here I will tell the story of both the successes and failures.

“Wait!” you say. Does not the inability of economists to forecast the current economic crisis show that all you claim is false? How can you defend a science that has met with such an abysmal failure? In response I ask — do you condemn quantum mechanics as useless because it cannot predict simultaneously the location and velocity of subatomic particles? Because not only can it not do so — according to the theory it is impossible for it to do so. Just so: according to economic theory — for reasons I will elucidate — it is equally impossible to predict the timing of economic crises. Does that make us useless? If we can — and we certainly can — tell how economic crises can be avoided, how they can be mitigated, and how best to recover from them — then surely you ought to listen to what I have to say.

It is impossible to have an intelligent discussion of economics, of game theory, or of behavioral economics — let alone their successes and failures — without some idea of what they are about. Homo economicus is a far different creature than commonly imagined. Let us begin by examining this mythical construct more closely.

What is Game Theory?

The heart of modern “rational” economic theory is the concept of a non-cooperative or “Nash” equilibrium of a game. If you saw the movie A Beautiful Mind this theory — created by Nobel Laureate John Nash — is briefly described, albeit inaccurately. But to put the oxen before the cart, let us first describe what a game is. A game in the parlance of a game theorist or economist does not generally refer to a parlor game such as checkers or bridge, nor indeed to Super-Mario III. Instead, what economists call game theory psychologists more accurately call the theory of social situations. There are two branches of game theory, but the one most widely used in economics is the theory of non-cooperative games — I shall describe that theory here.

The central topic of non-cooperative game theory is the question of how people interact. A game in the formal sense used by economists is merely a careful description of a social situation specifying the options available to the “players,” how choices among those options result in “outcomes,” and how the participants “feel” about those outcomes. The timing of decisions and the information available to players when undertaking those decisions must also be described.

The critical element in analyzing what happens in a game (or social situation) is the beliefs of the players: what do they think is likely to happen? How do they think other players are likely to play? From a formalistic perspective the beliefs of players are generally described by probability distributions — we assign a probability to an outcome — although in more advanced theories — such as epistemic game theory — beliefs are more sophisticated and mathematically complicated objects. Please observe that the notion that we are uncertain about the world we live in and about the people we interact with is at the very core of game theory.

Given beliefs about consequences and sentiments about those outcomes it is almost tautological to postulate that players choose the most favorable course of action given their beliefs. At one level this is what it means for players to be “rational” and should scarcely be controversial... yet many dense books have been written criticizing this notion of rationality.

Of course a theory that says that players believe something and do the best they can based on those beliefs is an empty theory because it does not say where beliefs come from. I sell my stocks? I must believe the market is going down. I spend all my money? I must believe the world is coming to an end. And so forth. The formation of beliefs is at the center of modern economic theory.

Our beliefs surely depend on what we know. I believe that if I drop this computer it will fall to the ground — because I have a lifetime of experience with falling objects. By way of contrast I have no idea when I wake up tomorrow morning whether the stock market will have gone up or down, and even less what might be the consequences of clean coal technology for global warming over the next decade.

Historically the economics profession has been most interested in situations where the players are experienced. For example, investment decisions are typically made by investors with long and deep experience of investment opportunities; most transactions are concluded between buyers and sellers with much experience in buying and selling. Under these circumstances it is natural to imagine that beliefs reflect underlying realities. In the theory of competitive markets this has been called rational expectations. In game theory it is called Nash equilibrium. Notice, however, that such a theory does not demand that people know the future — we call that “perfect foresight” not “rational expectations” — only that the probabilities they assign to the future are the same probabilities shared by other equally experienced individuals. Put differently: while I have no idea whether when I wake up tomorrow morning the stock market will have gone up or down, I do know that both outcomes are about equally likely. As this view is widely shared, it represents “rational expectations” about tomorrow’s stock prices.

Another way to describe Nash equilibrium is this: Nash equilibrium represents a setting in which no further learning is possible. That is — if some player holds wrong beliefs the possibility exists that they will discover their mistake and learn something new. When possibilities for learning are exhausted what we find is Nash equilibrium.

How well does the theory of Nash equilibrium work? One of the most widely used empirical tools in modern behavioral economics is the laboratory experiment in which paid participants - many times college undergraduates, but often other groups from diverse ethnic and social backgrounds — are brought together to interact in artificially created social situations to study how they reach decisions individually or in groups. Many anomalies with theory have been discovered in the laboratory — and rightfully these are given emphasis among practitioners — we are, after all more interested in strengthening the weaknesses in our theories than in simply repeating that they are correct. Amidst all this the basic fact should not be lost that standard economic theory works remarkably well in the laboratory.

Let me be more specific. Let us take as our theory the theory of Nash equilibrium. Let us also suppose (we will talk more about this later) that laboratory subjects care only about bringing home the most possible money from the experiment. Do we observe Nash equilibrium in the laboratory?

Voting

One of the most controversial applications of the theory of rational man is to voting. Modern voting theory, for example the 1996 theory of Feddersen and Pesendorfer, is based on the idea that your vote only matters when it decides an election — when your vote is pivotal. This has implications for voter participation. If elections are not close there is no chance of your vote mattering, and no incentive to participate. To be an equilibrium, elections must be so close that the chance of changing the outcome is enough to compensate for the cost of participating. Whether this is how voters behave is quite controversial: it is often referred to as “the paradox of voter turnout.” It is central to Green and Shapiro’s harsh 1994 critique of rational choice theory in which they assert that Those tests that have been undertaken [of rational choice theory] have either failed on their own terms or garnered theoretical support for propositions that, on reflection, can only be characterized as banal: they do little more than restate existing knowledge in rational choice terminology.

In 2007 Levine and Palfrey examined voter participation in the laboratory. Our subjects were UCLA (University of California, Los Angeles) undergraduates. After arrival at the laboratory the subjects were divided into unequal teams of voters. Later, various elections were conducted: in some elections one “party” had a 2/3rds majority, in others a one-vote majority. We conducted elections with numbers of participants ranging from three to fifty-one.

In these elections voters had a choice between casting a vote for their own party and abstaining. Voters received a small payment for participating in the experiment plus the members of each winning party received a prize of $0.37 each. This was split between the two parties in case of a tie. Voting in the laboratory — as in real life — was costly. Each voter was randomly assigned a cost of voting ranging from $0.00 to $0.185. This cost was known only to the voter to whom it was assigned — all other aspects of the experiment were commonly known to all the voters.

Notice that in this setup the most you can hope to do is to swing a losing election to a tie, or swing a tie to a win, in either case garnering an additional $0.185. So if you drew the lowest voting cost of $0.00 it makes sense to vote as long as there is even a small chance of changing the outcome, while if you drew the highest cost of $0.185 you would never vote unless you were absolutely certain to change the outcome. For other costs whether it is a good idea to participate or not depends on how likely you think you are to alter the outcome. For instance, if you think the probability of influencing the election is high you should accept a higher cost of voting.

Sticking with the (not entirely plausible) assumption that voters are strict moneygrubbers, it is possible but not easy to compute the Nash equilibrium of this game. Depending on the probability of making a difference there is a threshold cost below which it is rational to vote, and above which itis not—this is known as a “cut-off” decision. The participation rate is determined by this threshold — the higher the threshold, the higher the participation rate. Conversely the higher the participation rate, the less likely it is that voters make a difference. This kind of interdependence is described by economists and mathematicians as a fixed-point problem, and requires solving — in this case — some rather complex non-linear equations. This can be done only on the computer, and while in principle there could be more than one solution to these equations, in fact there is only one. Thus using the computer we made this difficult calculation determining for each election what was the Nash equilibrium.

As indicated, we then re-created the theoretical environment in the laboratory. We had no expectation that voters could guess, calculate, or otherwise intuitively figure out how best to behave — as I mentioned it is quite a complex problem. Rather, as is central to modern economic theory (see the quote of Lucas above), we imagined that if voters were given an opportunity to learn they would reach an equilibrium. Hence, we gave them ample opportunity to learn — voters got to participate in fifty elections each.

To measure how well the theory worked we focused on how likely it was for a player to make a difference. A pivotal event in this experiment is a situation that is either a tie, or one party wins by a single vote. Since voters only participate because they have a chance of being pivotal, in equilibrium the chance of such an event cannot be too small. Since elections “often” have to be close, it follows that there must also be upsets in which the minority party wins. The theory also predicts how frequently this will occur. For each type of election we computed what was the probability of pivotal events and upsets. For those of you familiar with social science research, you should notice what we did not do. We did not collect a bunch of data about behavior and fit a curve to it and declare that our curve “fits the data well” or is “statistically significant.” We did not declare that if there are more voters the participation rate should be “lower.” For any election with any number of voters, and any size of prizes and probabilities of drawing participation costs the theory of Nash equilibrium makes precise quantitative predictions about the frequency with which we should observe elections results that are pivotal and elections that result in an upset.

What happened with real people in our experimental laboratory? The figure below shows the results on a graph in which the horizontal axis has the frequencies we computed from the theory of Nash equilibrium and the vertical axis has the corresponding frequencies of actual election results in the laboratory. Each different election setting with different numbers of voters in each party corresponds to a different point on the graph. If the theory worked perfectly all of these points should lie on the 45 degree line where the theory exactly matches the data — for instance if the theory predicts 0.4 then we should observe 0.4. As you can clearly see — that is exactly what happens — the theory works more or less perfectly. If you do not believe this, try dropping tennis balls out your window and calculating the force of gravity and see how accurate your measurement is. Less good than this I can assure you.

0 Pivotal Events m Upsets

0.4 0.5 Theoretical

Probability of Pivotal and Upset Elections

Let us again emphasize what we did not do. Often when social scientists say their theory fits the data what they mean is that they “estimated free parameters” and given their best estimate of those parameters the corresponding model reflects the data. This would be as if instead of saying that our observations should lie on the 45 degree line, we said they should lie on some unknown line — the slope and intercept of that line being “free parameters” — and declaring victory if we could find a line that more or less passed through the data. Here there are no free parameters; nothing is estimated, there are no unknowns. We take the information about the setting - how many voters; what prizes, and so forth — and we calculate a number — the probability of a pivotal event or upset using the sharp predictions of Nash equilibrium. This number is then either right or wrong — in fact it is right. But there is no wiggle room to “estimate parameters” or otherwise fudge around with things.

Economics is a Quantitative Subject

The voting experiment illustrates what economics is and what it is not. It is not about the intersection of supply and demand curves, and about what direction prices move if a curve “shifts.” It is a quantitative theory of human behavior both individually and in interaction with other people.

The importance of the quantitative nature of economics often eludes clever observers, especially philosophers and lawyers. Take for example the following (possibly apocryphal) quotation — from the “original” behavioral economist Kenneth Boulding anyone who believes exponential growth can go on forever in a finite world is either a madman or an economist.

This appears to involve a straw man, since as far as I know no economist would argue that exponential growth can go on forever — at best this is something we are uncertain about. The point is not debatable. But what conclusion can we draw from the fact that exponential growth cannot go on forever? Boulding evidently would like us to conclude that if it cannot go on forever, it cannot go on for very long. Of course that does not follow. Exponential growth might be possible for only the next ten years — or it might be possible for the next ten thousand years. If the latter, there is hardly any point in arguing over it — and a model in which exponential growth can go on forever can certainly be useful and relevant despite its obvious falsity. On the other hand if we are going to run out of resources in ten years time — then indeed fooling around with models of exponential growth is a waste of time.

The point is that philosophers’ and lawyers’ reasoning — trying to draw a practical conclusion from an extreme hypothetical statement — “exponential growth cannot go on forever” — is false reasoning. All the action is in the quantitative dimension: some numbers are big, some numbers are small and how big and how small matters, not whether numbers are exactly equal to zero, or “infinite.”

Here is a practical application of quantitative reasoning: let’s consider whether or not torture should be against the law. Notice the question is not whether or not torture is “good” or “bad” or whether it is “moral” or “immoral.” A standard argument that torture should be legal is based on a simple hypothetical choice experiment. Many people if faced with a choice of torturing a suspect to determine the location of a nuclear weapon set imminently to explode in a large city would be in favor of doing so. Under those circumstances I would be prepared to do so. The conclusion is then derived that torture should be legal under these circumstances, and therefore the debate should be about when not whether torture should be legal. But in fact the conclusion does not follow.

As I indicated I would be willing to torture a suspect under the specified circumstances — yet I believe that it should be illegal for me to do so. Of course were I to be brought to trial I would hope to be let off on the grounds of necessity, or to get a Presidential pardon — but the point is that because the act is illegal — hopefully with severe penalties — I would certainly not be inclined to torture someone for frivolous reasons. Thus here is an economic argument against legalizing torture: if it is legal, despite the limited circumstances under which it is legal, then — in practice — there will be far too much torture. By the way — the evidence is overwhelming — in every instance in which a government has bureaucratized torture it has quickly gotten out of hand. But again the basic point: from an economic point of view the issue is not “will there be torture” or “will there not be torture” but “what will be the impact of making torture legal or illegal on the amount of torture that is practiced.” Hypotheticals about nuclear bombs in cities do not help us answer this quantitative question.

I should also add a warning at this point. Be careful in debating lawyers and philosophers. At this point in the argument they will introduce yet another irrelevant hypothetical “suppose that torture can be made legal without leading to excessive torture — should it be legal then?” To which the only relevant answer is “don’t waste my time.”

If—asislikely—you aren't planning on debating any lawyers or philosophers over economic issues — at least when a behavioral economist insists that people exhibit this or that form of irrationality, please ask: how many people and how irrational is the behavior in question? Theories by their nature are false. The question is always — are they quantitatively useful or not?

The Rush Hour Traffic Game

Beware of lawyers and philosophers bearing hypothetical examples. But beware also of social scientist bearing only laboratory results. After all — our main interest is: does the theory work outside the laboratory? In particular — does Nash equilibrium work outside the laboratory? That question is easier to answer than you might think.

There is a game that most of us are intimately familiar with. It is played five days a week in every major city in the world: it is the rush hour traffic game. In the “morning game” the “players” are commuters trying to get to work. Their choices are which route to take. Their objective is to minimize the time it takes to get to work — the more time it takes to get to work, the less happy you are. A moment of reflection should convince you that the sheer size of this game is overwhelming - in a large city it involves millions of players each of whom chooses between millions of routes. Yet the outcome of this game is a Nash equilibrium.

Wow! How can I possibly know that? Even the biggest supercomputer in the world can’t compute the Nash equilibrium of this game. Recall, though, what a Nash equilibrium is. It simply means that each commuter is taking the quickest route given the routes of all the other commuters. So the test of the Nash equilibrium theory is a simple one: are there commuters who can find quicker routes?

This test, by the way, is why I want to focus on rush hour traffic. During non-rush times there are many inexperienced drivers on the road, some making one of a kind trips to unfamiliar locations, and often they take routes that are much slower than the fastest available. Nonetheless during rush hour commuters are experienced, and have tried a lot of routes in the past — there is no much room for learning. So — if you try to take a tricky combination of side streets rather than the main boulevard you discover that just enough traffic has spilled over on to the side streets that you can gain no advantage. How do I know this? I’ve tried — I suggest you do. For years I commuted about an hour to work through Los Angeles rush hour. I was often stuck on a very slow boulevard in Beverly Hills. In frustration I experimented with many alternative combinations of side streets. Sadly it never got me to work faster. In fact on one occasion, I was behind a large truck when I got off the main boulevard, and after ten minutes of tricky driving, I got back on the main boulevard — just to find myself behind exactly the same truck. In short: Nash equilibrium.