from homo economic us to homo sapiens

8/6/2019 From Homo Economic Us to Homo Sapiens

1/12

From Homo Economicus to Homo

Sapiens

Richard H. Thaler

Responding to a request for a forecast is especially tricky for someone like

me, who specializes in other peoples biases. Research in psychology

suggests that certain biases are very likely to creep into my forecasts about

the future of economics (or anything else).

1. Optimism (and wishful thinking). We all tend to be optimistic about the future.

On the first day of my MBA class on decision-making at the University of Chicago,every single student expects to get an above-the-median grade, yet half are inevi-

tably disappointed. This optimism will induce me to predict that economics will

become more like I want it to be.

2. Overconfidence. In a related phenomenon, people believe they are better

forecasters than they really are. Ask people for 90 percent confidence limits for the

estimates of various general knowledge questions and the correct answers will lie

within the limits less than 70 percent of the time. Overconfidence will induce me

to make forecasts that are bolder than they should be.

3. The False Consensus Effect. We tend to think others are just like us. My

colleague, George Wu, asked his students two questions: Do you have a cell phone? What percentage of the class has a cell phone? Cell phone owners thought 65

percent of the class had mobile phones, while the immobile phoners thought only

40 percent did. (The right answer was about halfway in between.) The false

consensus effect will trap me into thinking that other economists will agree with

me20 years of contrary evidence notwithstanding.

4. The Curse of Knowledge. Once we know something, we cant imagine ever

thinking otherwise. This makes it hard for us to realize that what we know may be

less than obvious to others who are less informed. The curse of knowledge will lead

y Richard H. Thaler is the Robert P. Gwinn Professor of Economics and Behavioral Science,

Graduate School of Business, University of Chicago, Chicago, Illinois.

Journal of Economic PerspectivesVolume 14, Number 1Winter 2000Pages 133141


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me to think that others will have read the same articles I have, and have learned the

same lessons from them (lessons I now take for granted), when in fact others have

been busy reading entirely different material, and have never even heard of the

findings that have so influenced my thinking.In making some forecasts for the future of economics, it would be embarrass-

ing to commit (in writing) all the mistakes I spend weeks warning my students to

avoid. However, the alternatives are not very attractive, either. Rationally, I realize

that the forecast most likely to be right is to predict that economics will hardly

change at all. (Have I mentioned status quo bias?) Although such a forecast has the

virtue of brevity, it would not make very interesting reading (or writing). So, with

trepidation, I am going to make six bold predictions about how economics will

develop over the next couple decades, forecasts that are guaranteed to contain

every bias mentioned above, as well as some others. You have been warned.

Homo Economicus Will Begin Losing IQ, Reversing a 50-year

Trend

Economics in the first half of the 20th century was much more of a social

science. Writers such as Irving Fisher and John Maynard Keynes stressed psycho-

logical factors in their explanations of economic behavior (Loewenstein, 1992).

With the mathematical revolution that began to take off in the 1940s with the likes

of John Hicks and Paul Samuelson, economic agents began to be more explicitlyoptimizing. In the 1950s, economists who began formalizing the micro foundations

of Keynes developed more rational models; for example, compare Keyness simple

consumption function with the life-cycle hypothesis, and then with the rational

expectations hypothesis of Muth, Lucas, and so on. Eventually the models came to

include agents that detractors called hyperrational. The aesthetic in the field

became that if the agents in model A are smarter than the agents in Model B, then

Model A is better than Model B. The IQ of Homo Economicus became bounded

only by the IQ of the smartest economic theorist!

My prediction is that this trend will be reversed in favor of an approach in

which the degree of rationality bestowed to the agents depends on the context

being studied. To illustrate how this can work in practice, consider the guess the

number game first studied by Rosemarie Nagel (1995). In this game, contestants

are told to guess a number from 0 to 100, with the goal of making their guess as

close as possible to two-thirds of the average guess. In a world where all the players

are known to be fully rational, in the sense that they will form expectations about

the guesses of others can carry out as many levels of deduction as necessary, the

equilibrium in this game is zero.

In any other setting, however, guessing zero is not a good strategy. Recently, I

had the opportunity to play this game for quite large stakes (Thaler, 1997). At myrequest, the Financial Timesran a guess the number game contest using the rules

134 Journal of Economic Perspectives


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described above and offered two business class tickets from London to the United

States as a prize (worth over $10,000). Only integer guesses were permitted.

Although many contestants did guess zero or one, the most popular guesses were

33 (the right guess if everyone else chooses a number at random) and 22 (the rightguess if everyone else picks 33). The average guess was 18.91 and thus the winning

guess was 13. Although modeling how this game is actually played is not easy, some

lessons are clear enough. An appropriate model would have to allow for two kinds

of heterogeneity in sophistication. First, agents differ in how many levels of pro-

cessing they engage in (33 is one level, 22 is two levels, and so on). Second, there

is heterogeneity in how much agents think about the behavior of other agents.

Agents who guess zero are sophisticated on the first dimension and nave on the

second. Many economists fall into this category (due in part to the False Consensus

Effect and the Curse of Knowledge!) Sophisticated economic models will haveagents that are both more and less sophisticated than the agents we are used to

modeling. I predict this sort of modeling will be the norm in the future.

Homo Economicus Will Become a Slower Learner

Most economic models have no reason to introduce learning because agents

are assumed to solve the relevant problem correctly on trial one. When learning is

explicitly introduced, Homo Economicus (hereafter abbreviated HE, with no gen-

der inference intended) is typically taken to be a quick study. If, perchance, HEmakes an error, HE quickly learns to correct it. However, the students I have taught

over the years, even at our best universities such as Cornell, MIT, and Chicago, are

a little slower on the uptake. Even after hearing what is, to my unbiased view, a

completely clear explanation, they still often make a mistake in applying a concept

if the context is slightly disguised. This is why putting a question about the first part

of the course on an exam covering the later part of the course is considered so

unfair by the students.

The problem with many economic models of learning is that they seem to

apply to a very static environment. In fact, such models seem to be directly

applicable only to the situation in which Bill Murray finds himself in the movieGround Hog Day.1 In that movie, Bill Murray is a TV weatherman sent to report on

whether the groundhog sees his shadow on Feb. 2. Murrays character ends up

reliving the same day over and over again. Although he is a slow learner, the

opportunity to rerun the same day repeatedly, and to learn from the consequences

of his actions each time, creates a controlled experiment in which he is able to

learn many things eventually, from how to prevent accidents to how to play the

piano. Alas, life is not like Ground Hog Day. In life, each day is different, and the

1 The idea that economic models of learning are similar to this movie evolved during a conversation Ihad with Colin Camerer during a Russell Sage Foundation summer institute on behavioral economics.It is a safe bet that we each think it was our idea.

Richard H. Thaler 135


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most important of lifes decisions, such as choosing a career or spouse, offer only

a few chances for learning! I predict that economic models of learning will become

more sophisticated by making their agents less sophisticated and giving greater

weight to the role of environmental factors, such as the difficulty of the task and thefrequency of feedback, in determining the speed of learning. This means that

models of saving for retirement (a hard problem with few opportunities for

learning) should be very different from models of frequency of milk purchases

(easier, with many learning chances).

The Species Populating Economics Models Will Become More

Heterogeneous

Although you can get the wrong impression reading economics textbooks and

journal articles, most economists are happy to admit they know many people whose

reasoning is quite flawed: their spouses, children, students, colleagues, deans,

college presidents, and so on. When pressed on why it is reasonable to base

economic models exclusively on rational representative agents, while at the same

time thinking that most of the people they interact with are at least occasionally

bozos, typically some kind of evolution plus markets argument is offered. The

argument proceeds something like this. Suppose there were some less-than-fully-

rational agents. I like to call them quasi-rational, meaning trying hard but subject

to systematic error. Once these quasi-rationals started interacting with rationaltypes, the rationals would quickly take all their money, after which the quasis would

either learn or would be rendered economically irrelevant. Rarely is this argument

spelled out carefully, and for good reason: It is false!

When rational agents interact with quasi-rational agents, the rational agents

cannot be expected either to take all the quasis money, or to set prices unilaterally.

Indeed, careful analyses of such situations in financial markets, such as those by

De Long et al. (1990), show that it is possible for the quasiscalled noise traders

in finance circlesto end up richer than their rational counterparts (by inadver-

tently bearing more risk). Although papers mixing rational and quasi-rational

agents have become popular over the past decade or so, it is still considered a

novelty to have some quasis in the model. In workshops, presenters of such models

still feel compelled to explain why they need to have these quasis mucking things

up. My prediction is that in future seminars presenters will have to explain why they

are using a model with only rational agents (unless the paper is on the history of

economic thought). After all, analyses of market interactions between agents of

various types is exactly what differentiates economics from other social sciences.

Psychologists, sociologists and anthropologists might help us improve our charac-

terizations of economic behavior, but economists are the only social scientists with

the tools to analyze what happens in market contexts.Note that I am not predicting that HE will disappear from economics research.



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At least two roles should remain. First, many aspects of the standard HE model are

useful as theoretical special cases, much as perfect competition is used today.

Second, when a few highly trained special agents can influence markets, as in

financial markets, they can be usefully modeled as HEs, especially in models withheterogeneous agents.

Economists Will Study Human Cognition

One way of modeling bounded rationality is to reduce the information-

processing capabilities of the agents; for example, in the case of the numbers game

discussed earlier, by assuming that individuals will only do two steps of backward

induction rather than infinite steps. This is a sensible initial approach, but we cando more that make HE dumber. A more interesting research agenda is to attempt

richer characterizations of economic agents via a better understanding of human

cognition. This, I predict, will be a major area of effort over the next two decades.

Some successful examples published in the last 20 years prove that this kind of work

is both feasible and useful.

The most significant exemplar is the prospect theory of Daniel Kahneman

and Amos Tversky (1979). This positive theory of decision-making under uncer-

tainty manages to capture an enormous amount of psychological wisdom in its

S-shaped value function. The value function shows changes in material well-being

on the horizontal axis, rather than levels as in expected utility theory, becausehumans (and other species) have a strong tendency to adapt to their environment

and react only to perceived changes. The vertical axis shows happiness resulting

from these changes. The S-shape displays diminishing marginal sensitivity to both

gains and losses, a basic finding in the psychology of perception (psychophysics).

Finally, the loss function is steeper than the gain function, a property that has come

to be known as loss aversion. Losses hurt about twice as much as gains make us feel

good. These three psychological concepts yield plenty of explanatory power, having

been used to explain as diverse phenomena as consumers reaction to price changes

in the supermarket to the labor supply behavior of cab drivers (Camerer,

forthcoming).There are an enormous number of exciting ways in which a better understand-

ing of human cognition could help us do better economics. Ill suggest two here.

First, there is a problem with prospect theory that cognitive psychology might help

us fix; namely, the theory is incomplete. Prospect theory tells us that choices

depend on the framing of a problem, but does not tell us how people will

spontaneously create their own frames. By directly studying how people attack

decision-making problems, we may learn more about this problem-editing process.2

Second, though we have given considerable attention in recent years to the impli-

2 Some of what we know about this problem falls into the category of mental accounting. For a currentreview of this literature, see Thaler (1999).

From Homo Economicus to Homo Sapiens 137


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cations of bounded rationality, we have spent less time studying the impact of

bounded memories. A simple example is hindsight bias: after the fact, events that

happen are thought to have been predictable. For example, one year in my class I

asked my students on the first day of class (in late January) to make predictionsabout stock market returns for the next two months. Their forecasts were bearish:

they thought it was more likely that the market would go down than up. Two

months later I asked them to try to recall their earlier forecast. They remembered

being bullish. Needless to say, the market rose sharply over this two month period.

This phenomenon (related to the curse of knowledge mentioned earlier) is

both strong and robust, and has powerful implications for economics. Consider, for

example, the role of hindsight bias in agency problems. A principal with a biased

memory (that is, any real world principal) will find it very difficult to distinguish

between a bad decision and a bad outcome, since an unlucky exogenous event willbe thought, in hindsight, to have been predictable. Agency theory with absent-

minded principals (and agents) would be an exciting field of inquiry.3

Economists Will Distinguish Between Normative and Descriptive

Theories

Psychologists distinguish between two kinds of theories: normative and de-

scriptive. To them, normative theories characterize rational choice: examples

would include the axioms of expected utility theory and Bayes rule. Descriptivetheories try to characterize actual choices. Prospect theory is an example of a

descriptive theory. Agents who choose according to prospect theory violate funda-

mental axioms of rational choice; for example, under certain circumstances they

will choose option A over B even when B dominates A, as long as the dominance

is not too obvious.

I would not want to call such choices rational, but since people do choose them

in real life, high stakes situations, it is important that economists develop models

that predict such behavior. Economists have traditionally used one theory to serve

both the normative and descriptive purposes. Expected utility theory and the

life-cycle theory of saving are rational (normative) models that economists have

used also as descriptive models. Occasionally economists have proposed explicitly

descriptive theories, such as William Baumols (1967) theory of the firm in which

managers maximize sales subject to a profit constraint. However, such descriptive

theories have not won great acceptance. Part of the resistance to such theories, I

think, has been based on a misunderstanding of the issues raised above regarding

how competition will force changes in quasi-rational behavior. For example, Bau-

mol often heard the critique that firms that maximized sales would inevitably lose

market share to competing firms that were trying to maximize profits. The self-

3 For a clever example of what absent-minded economics might look like, see Mullainathon (1999).



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contradictory nature of this critiquethat maximizing sales would cause lower

market sharedid not seem to bother (or occur to) its adherents. There is, of

course, a perfectly good equilibrium in which some firms are willing to accept lower

profits in order to be bigger, and such firms take market share away from profit-maximizing competitors, not vice versa. Similarly, if a baseball team owner chooses

to buy a World Series victory at the expense of profits, the profit-maximizing owners

of other teams can do little except become even richer losers.

One additional point about descriptive theories: they are, of necessity, driven

by data. Baumols sales maximization hypothesis was suggested to him by conver-

sations with managers. Kahneman and Tverskys prospect theory was derived by

examining hundreds of choices between pairs of gambles. Some economists seem

to feel that data-driven theory is, somehow, unscientific. Of course, just the opposite

is true. Copernicus watched the movements of the planets before devising histheory that the planets orbit the sun. What makes for a good descriptive theory is

out-of-sample tests; for example, the prediction that Pluto would be discovered

before telescopes were good enough to see it. So, this prediction leads to an

auxiliary prediction that more theorists will pay attention to data.

Homo Economicus Will Become More Emotional

The predictions I have made so far, though fraught with the biases I identified

early on, are still somewhat conservative in the sense that lots of good work isalready going on in the directions I suggest that the field will be headed. So, it

seems right to offer the slightly more courageous prediction that Homo Economi-

cus will become more emotional, by which I mean that economists will devote more

attention to the study of emotions.

To get a sense of what the study of emotions entails, I refer readers to Jon

Elsters (1998) recent article. Although Elster does not define emotions explicitly,

he does offer a list of states that he says are unambiguously emotions, of which a

subset are: anger, hatred, guilt, shame, pride, liking, regret, joy, grief, envy, malice,

indignation, jealousy, contempt, disgust, fear, and, oh yes, love. Elster distinguishes

this list from other visceral factors (a more general term, see Loewenstein, 1996)such as pain, hunger, and drowsiness, in that they are triggered by beliefs. Many of

these emotions are often accompanied by states of physiological arousal, like fear.

How can emotions be incorporated into economic analyses? The ultimatum

game offers one simple example. In the ultimatum game one player, the Proposer,

is given a sum of money, say $10, and makes an offer of some portion of the money,

x, to the other player, the Responder. The Responder can either accept the offer,

in which case the Responder gets x and the Proposer gets $10-x, or reject the offer

in which case both players get nothing. Experimental results reveal that very low

offers (less than 20 percent of the pie) are often rejected. Speaking very generally,

one can say that Responders react emotionally to very low offers. We might get

more specific and say they react indignantly. What is certain is that Responders do

Richard H. Thaler 139


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not act to maximize their own payoffs, since they turn down offers in which they

receive a small share of the pie and take zero instead. Matthew Rabins (1993)

model of fairness, which is an attempt to explain such behavior (specifically, the

resisting of unfair offers) is based partly on emotions.Rejecting a positive offer in the ultimatum game is spiteful; it hurts both

parties. Unfortunately, such behavior is more common than economic theorizing

would lead us to expect. I need only mention the word divorce to bring to mind

all-too-many familiar examples. Spite is not confined to ex-spouses. The Coase

theorem prediction that the allocation of resources is independent of the assign-

ment of property rights depends on the willingness of the parties to a lawsuit to

recontract. However, recontracting requires interaction that can be made difficult

by spite. In a recent study of this issue, Ward Farnsworth (1999) interviewed

attorneys from over 20 nuisance cases in which injunctive relief was sought andeither granted or denied after full litigation before a judge. In not a single case did

the parties even attempt to contract around the court order.

Conclusion

My predictions can be summarized quite easily: I am predicting that Homo

Economicus will evolve into Homo Sapiens. This prediction shouldnt be an

outlandish one. It seems logical that basing descriptive economic models on more

realistic conceptions of economic agents is bound to increase the explanatorypower of the models. Still, a conservative economist might (emotionally) scoff: If

this were a better way of doing economics, we would already be doing it that way!

Why arent all my predictions already true? And why should I expect things to

change?

One reason economics did not start out this way is that behavioral models are

harder than traditional models. Building models of rational, unemotional agents is

easier than building models of quasi-rational emotional humans. Nonetheless, each

generation of scientists builds on the work of the previous generation. Theorems

too hard to prove 20 years ago are found in graduate student problem sets today.

As economists become more sophisticated, their ability to incorporate the findingsof other disciplines such as psychology improves. Simultaneously, we can hope that

new scholars in other disciplines can do for economics what cognitive psychologists

such as Kahneman and Tversky have already done: offer us useful findings and

theories that are relatively easy to incorporate into economic models.

I will close with a very safe prediction. If some of my predictions about the

future of economics come true, young economists will have done the work. (Old

economists, like me, cant learn new tricks any better than dogs.) A few of these

young economists are already on the horizon. Others will follow.

y The author would like to thank Colin Camerer, Peter Diamond, George Loewenstein, Jesus

Santos, Andrei Shleifer, Cass Sunstein and all the editors for helpful comments.



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York: Cambridge University Press.De Long, J. Bradford, Andrei Shleifer, Law-

rence Summers, and Robert Waldmann. 1990.Noise Trader Risk in Financial Markets. Journalof Political Economy. 98, pp. 70338.

Elster, Jon. 1998. Emotions and EconomicTheory. Journal of Economic Literature. March,XXXVI, pp. 4774.

Farnsworth, Ward. 1999. Do Parties to Nui-

sance Cases Bargain After Judgement? AGlimpse of the Cathedral. University of ChicagoLaw Review, 66, pp. 373 436.

Kahneman, Daniel and Amos Tversky. 1979.Prospect Theory: An Analysis of Decision underRisk. Econometrica. 47:2, pp. 26391.

Loewenstein, George. 1992. The Fall andRise of Psychological Explanations in the Eco-

nomics of Intertemporal Choice, in Choice Over

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Visceral Influences on Behavior. OrganizationalBehavior and Human Decision Processes. March,65:3, pp. 80524.

Mullainathon, Sendhil. 1999. A MemoryBased Model of Bounded Rationality, workingpaper, MIT Economics Department.

Nagel, Rosemarie. 1995. Unraveling inGuessing Games: An Experimental Study. Amer-ican Economic Review. 85:5, pp. 13131326.

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Thaler, Richard H. 1997. Giving Markets a Hu-man Dimension. Financial Times. 6, June 16, 1997.

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