Download Social Norms or Social Preferences?

Social Norms
or
Social Preferences?
Ken Binmore
Economics Department
University College London
Gower Street
London WC1E 6BT, UK
[email protected]
tel+fax: +44-1600-860-691
Abstract: Some behavioral economists argue that the honoring of social norms can be adequately modeled as the optimization of social utility functions in which the welfare of
others appears as an explicit argument. This paper suggests
that the large experimental claims made for social utility functions are premature at best, and that social norms are better
studied as equilibrium selection devices that evolved for use in
games that are seldom studied in the economics laboratories.
Keywords: social norm, social preference, evolutionary game theory, behavioral economics, inequity aversion, Ultimatum Game.
Social Norms or Social Preferences?
1
Behavioral Economics
Every so often, an attempt is made by economists to muscle in on neighboring
disciplines like psychology and sociology. This paper argues against a new imperial
venture that originates from within the discipline of behavioral economics.
Behavioral economics seems not to fit the standard mold at first sight because
it eschews the language of human or social capital. It traces its origins instead
to the experiments of the psychologists, Kahneman and Tversky, whose work is
now generally accepted to have blown away some of the cobwebs of tradition that
economic theorists once treated as axiomatic. But now there are those who blow
so hard that they seem determined to blow away everything in social science that
does not fit within their newly created orthodoxy.
As an economist who joined the experimental pioneers at an early stage, I continue to welcome the breath of fresh air that behavioral economics has brought to
my discipline. Studies of the psychology of fairness, reciprocity and reputation are
particularly important for my own interest in the evolution of social norms. However, the group of behavioral economists criticized in this paper seem to me to have
betrayed the scientific ethos of the founders of their subject by letting themselves
fall prey to a new economic dogma—that social behavior is always best explained
by assuming that people simply maximize social (other-regarding) utility functions.
Among much else, it is argued that people are nicer than the selfishness axiom
of neoclassical economics admits, and that their discoveries are sufficiently robust
that they can be used to inform public policy (Fehr and Gintis 2007; Gintis 2009;
Henrich et al. 2004; Thaler and Sunstein 2008). They certainly sing a beguiling
song, but it would be a mistake to take their claims at face value. For example:
1. It is not true that it is axiomatic in neoclassical economics that people are selfish. On the contrary, students are traditionally taught the motto de gustibus
non est disputandum, which means that we must take whatever the preferences of economic agents happen to be as given. Neoclassical economics
is therefore in no danger of being refuted if people sometimes prefer other
things to money.
2. Nor do experiments show that all traditional economics is hopelessly wrong.
On the contrary, in the market contexts of most interest to economists, laboratory experiments show that traditional theory works rather well (Vernon
Smith 1991).1 It is true that traditional economic theory sometimes fails
1 Vernon Smith is the leading example of a large number of experimental economists who do
not follow the behavioral line.
2
miserably in other contexts, notably in respect to human behavior in risky
situations (Kahneman and Tversky 1979), but that is no reason to condemn
the theory in contexts where it works pretty well.
3. Nor is it true that all or any of the theories propounded by behavioral economists
to explain what they call anomalous data are particularly successful. For example, the traditional idea that people will act to maximize expected utility
predicts very badly, but Prospect Theory—which is Kahneman and Tversky’s
alternative behavioral theory—predicts no better when its extra parameters
are taken into account (Camerer and Harless 1994; Hey and Orme 1994).
The claim that theories of other-regarding preferences are robust seems to be
without any serious foundation at all.
4. It is not even true that behavioral economics can always be regarded as
a new alternative to neoclassical economics. On the contrary, theories of
other-regarding utility functions might better be classified as retroclassical
economics, as they revert to the dogma that people actually do have utility functions in their heads that they seek to optimize when interacting with
others—an idea that was popular in Victorian times, but which was abandoned
by neoclasssical economists many years ago.
I think it fair to say that almost all behavioral economists endorse the idea
that social norms can be adequately described using social or other-regarding utility
functions, but only a vocal minority insist that the evidence is so strong that the
hypotheses favored by other disciplines deserve no better fate than the waste basket.
My own guess is that their insistence on blurring the distinction between a social
norm and a social preference will turn out to be a bad mistake. In explaining
why, I occasionally need to refer to the wider criticisms outlined above, but it will
be necessary to look elsewhere for a full justification of these complaints (Binmore
2007; Binmore and Shaked 2010). It will also be necessary to consult the appendices
to this paper when the claims being made require technical support.
2
Social Norms
How one models a social norm depends on the purpose for which the model is being
constructed. Since I am interested in the origin and stability of social norms, I adopt
a framework that is nowadays expressed in the language of game theory, but goes
back at least as far as David Hume (1978):
Two men who pull the oars of a boat, do it by an agreement or convention,
although they have never given promises to each other. Nor is the rule concerning
the stability of possessions the less derived from human conventions, that it arises
gradually, and acquires force by a slow progression, and by our repeated experience
of the inconveniences of transgressing it. . . . In like manner are languages gradually
established by human conventions without any promise. In like manner do gold
and silver become the common measures of exchange.
3
As Hume explains, social conventions govern a wide spectrum of social behavior,
from the trivial to the profound. Their range extends from the arcane table manners
we employ at formal dinner parties to the significance we attach to the green pieces
of paper we carry round in our wallets bearing pictures of past presidents. From the
side of the road on which we drive to the meaning of the words in the language we
speak. From dietary taboos to the standards of integrity we expect of honorable
folk. From the vagaries of fashion to the criteria that determine who owns what.
From the amount people tip in restaurants to the circumstances under which we
are ready to submit ourselves to the authority of others.
In modern times, the idea that a social norm or institution should be identified
with what Hume calls a convention was pursued by Thomas Schelling (1960) and
by David Lewis (1969) before being taken up by game theorists.2
From the perspective of game theory, human social life consists largely of the
play of a succession of coordination games that we commonly solve without thought
or discussion—usually so smoothly and effortlessly that we do not even notice that
there is a coordination problem to be solved. Who goes through that door first?
How long does Adam get to speak before it is Eve’s turn? Who moves how much
in a narrow corridor when a fat lady burdened with shopping passes a teenage boy
with a ring through his nose? Who should take how much of a popular dish of
which there is not enough to go around? Who gives way to whom when cars are
maneuvering in heavy traffic? Who gets that parking space? Whose turn is it to
wash the dishes tonight? These are picayune problems, but if conflict arose every
time they needed to be solved, our societies would fall apart.
Most people are surprised at the suggestion that there might be something
problematic about how two people pass each other in the corridor. When interacting
with people from our own culture, we often solve such coordination problems so
effortlessly that we do not even think of them as problems. However, when a
coordination problem is modeled as a game, it is impossible to fail to see the need
for social criteria that determine which of the various different ways of solving the
game will be operated in whatever society is under study.
The Driving Game of Appendix 1 is a familiar example. When Adam and Eve set
off for work in the morning, they each have two strategies. They can drive on the
left or on the right. If their solution to this game is to be stable, it must be a Nash
equilibrium of the game. That is to say, both players must simultaneously choose
a strategy that is a best reply to the strategy chosen by the other. Otherwise, at
least one player would have a motive to deviate from his or her solution strategy.
The Driving Game has three Nash equilibria. The players can both drive on
the left. They can both drive on the right. Or they can both choose the side of
the road on which to drive at random. The third equilibrium is inefficient, in the
sense that both players would prefer to coordinate on either of the other equilibria.
But the players are indifferent between the two efficient equilibria. They do not
2 I think that Lewis went astray by insisting that conventions must be common knowledge in
order to be operational, but this is another story (Binmore 2008).
4
care whether they drive on the left or on the right, provided that they succeed in
coordinating on the same choice. Rationality therefore provides no guide on how to
make the choice. The players need to look elsewhere for an equilibrium selection
device to resolve their problem. Game theorists argue that social norms exist for
this purpose. Each social norm is a society’s solution to the equilibrium selection
problem for a particular class of coordination games.
The Swedes drive on the right because they made a conscious decision to switch
from driving on the left on September 1, 1967. Nearly everywhere else in the world,
the social norms that determine on which side of the road a country drives are
the result of a series of historical accidents. Cultural evolution ensures that most
countries end up at an efficient equilibrium of the Driving Game, since accidents
are best avoided by driving on the same side of the road as the majority of other
drivers. However, rationality is not particularly relevant to why the Japanese drive
on the left and the French on the right. Economists therefore have little or nothing
to contribute to this question. One needs to look to social historians for an answer.
The Driving Game is so transparent that nobody disputes this conclusion. However, behavioral economists reject the same conclusion in more complicated coordination games.3 They implicitly argue that people operate one social norm rather
than another because they like the first social norm better than the second.
There is, of course, a tautological sense in which this is true. I like driving on
the left in London because I am less likely to have an accident if I drive on the
same side of the road as everybody else. I like driving on the right in New York
for the same reason. But all that is being said here is that it is optimal for me
to play my part in whatever Nash equilibrium the local social norm selects. No
insight at all is being offered into why a society should have ended up with one
social norm rather than another. One might as well offer as an explanation that
Japan drives on the left because the Japanese get more utility from driving on the
left, and France drives on the right because the French get more utility from driving
on the right. Sociologists who ask how the French come to like one thing and the
Japanese another can then be fobbed off with the neoclassical dictum that there is
no accounting for tastes. Psychologists who report that their subjects are unaware
of the preferences attributed to them can be told that their subjects nevertheless
behave as if they were optimizing a utility function.
The methodological problem is most acute with fairness norms. For example,
Fehr and Schmidt’s (1999) theory of inequity aversion offers a functional form (with
two free parameters) of a utility function that incorporates a taste for fairness. But
even if Fehr and Schmidt’s claims that such utility functions are able to predict
laboratory data were not wildly exaggerated (Binmore and Shaked 2010), how much
insight is gained by arguing that people play fair because they like playing fair?
3 Neoclassical economists have traditionally avoided the issue by confining their attention to
models with only one equilibrium, which explains their lack of interest in fairness. If there is no
equilibrium selection problem to solve, why take an interest in an equilibrium selection device like
a fairness norm?
5
3
Adaptation and Habituation
Most social norms emerge without any conscious intent as cultural adaptations to
commonly played classes of coordination games. The citizens of a society operating
a particular social norm are almost always unaware of playing a game at all, let
alone that the game has multiple equilibria amongst which a selection needs to be
made. When a norm works very smoothly, people will even sometimes deny that a
norm is in use at all.
We mostly pick up the habit of honoring the norms that operate in our society
as children by imitating the behavior of those around us. Eventually, the norms
become as internalized as our routines for driving a car or tying our shoelaces. The
idea that we are consciously maximizing some utility function when we honor a wellestablished norm lacks any support from anyone. Everybody knows that we mostly
just respond with an appropriate habituated response when we receive certain cues
from the social environment in which we find ourselves. It is therefore evolutionary
game theory to which one must appeal when seeking to model what is going on.
Evolutionary game theory differs from rational game theory in its focus on the
trial-and-error adaptation processes by means of which boundedly rational agents
find their way to equilibria in the games they play. In biological applications, the
players may not reason at all, but there is usually some mixture of unconscious
adaptation and conscious learning in cultural applications. Discussion of laboratory
experiments on social norms are often confused by a failure to appreciate that it is
necessary to pay attention to cultural evolution and learning on at least two levels:
1. The social norms of ordinary life are usually the end-product of a largely
unrecognized process of cultural evolution. They are therefore adapted to
coordination games played in everyday life.
2. The mind of a subject entering a laboratory is not a blank slate. It brims
over with a menu of social norms, each of which is adapted to a different
class of coordination games. When the subject plays a game in a laboratory,
what usually happens is that the manner in which the game is framed—the
hints and cues built into its description—trigger one or more of these social
norms. But there is no guarantee that the social norm that gets triggered is
adapted to the game being played in a laboratory (as it probably would be
when subjects respond to the hints and cues that accompany a game played in
ordinary life). The subjects are then at risk of failing to coordinate on a Nash
equilibrium of the laboratory game. When this happens, we must expect to
see the subjects’ behavior changing over time in the laboratory as they adjust
to a situation for which they have no suitable social norm on their menu.
Appendix 2 describes a laboratory experiment of my own that illustrates both these
points. However, the leading example is the one-shot Prisoners’ Dilemma, which is
the simplest of a whole class of public goods games. In such games, each player can
privately make a contribution to a notional public good. The sum of contributions
6
is then increased by a substantial amount, and the result redistributed to all the
players. In such games, it is optimal for selfish players to “free ride” by contributing
nothing, thereby pocketing their share of the benefit provided by the contributions
of the other players without making any contribution themselves.
Experiments on the one-shot Prisoners’ Dilemma show that subjects (usually
students) who are new to the game contribute slightly more than 50% of the time,
but that their behavior changes as they gain experience. After playing repeatedly
(against a new opponent each time), about 90% of subjects end up free riding
after ten trials or so. One can disrupt the march towards free riding in various
ways, but when active intervention ceases, the march resumes. Since there is a
small but vociferous school of behavioral economists who seek to downplay this
inconvenient fact (Eckel and Gintis 2010), it is worth pointing out that Camerer
(2003, p.46) explicitly endorses the result as standard in his much cited Behavioral
Game Theory. As he says, the huge number of experimental studies available in
1995 was surveyed both by John Ledyard (1995) and by David Sally (1995).
The mistake that behavioral economists commonly make when interpreting laboratory data is to take for granted that the subjects are optimizing something. The
subjects may well behave as though they were genuinely optimizing something when
they honor the social norms that tell them how to play the kind of everyday games
for which the social norms evolved, but the social norms triggered in the minds of inexperienced subjects are unlikely to be adapted to the artificial games they are asked
to play in the laboratory. One can learn a great deal by manipulating the framing
of experimental games to trigger different social norms in the laboratory—just as
Konrad Lorenz learned a good deal about what is instinctive and what is not when
a bird takes a bath by observing a totally inexperienced baby jackdaw go through
all the motions of taking a bath when placed on a marble-topped table. But such
insight is gained only by avoiding the mistake of supposing that bath-taking behavior confers some evolutionary advantage on birds placed on marble-topped tables.
Similarly, behavioral economists need to learn from their psychological cousins that
norm-governed behavior is not necessarily compatible with optimizing behavior.
It is particularly important to be aware that many of the coordination games we
play in our daily lives are not at all like the one-shot games that subjects usually meet
in the laboratory. A one-shot game is played just once with a particular opponent,
and its outcome has no significance for past or future events. However, the games
we play in real life are often repeated games—games played repeatedly with the
same opponents. Even when a game is to be played only once, it is seldom the case
that the way it is played has no significance for the future.
For example, when people are at the receiving end of a take-it-or-leave-it demand
in real life, their behavior is normally observed by onlookers with whom games may
be played in the future. A social norm that tells one how to respond to ultimata
must therefore take account of the fact that people cannot usually afford to acquire
a reputation of being a soft touch. As a consequence, we are socially programmed
to refuse “unfair” offers, even if we end up with nothing as a result. It is therefore
not particularly surprising that subjects also tend to refuse “unfair” offers when they
7
play the one-shot Ultimatum Game (Section 3) in the laboratory.
Reciprocity is as important as reputation in repeated games.4 For example,
Axelrod (1984) popularized a particular kind of reciprocal strategy called tit-fortat for the infinitely repeated Prisoner’s Dilemma. The strategy calls for a player
to begin by cooperating, and then to copy whatever the opponent did last time. If
both players use tit-for-tat, then the result is a Nash equilibrium of the repeated
game in which both players cooperate all the time. The folk theorem of repeated
game theory shows that all infinitely repeated games have many such efficient Nash
equilibria (provided that the players have no secrets from each other and do not
discount time too fast for long-term relationships to be irrelevant).
The strategies that support such efficient equilibria in repeated games work by
requiring a player to punish any deviation by an opponent from the implicitly agreed
equilibrium path by withdrawing cooperation (or worse) for long enough to make
the deviation unprofitable. As David Hume (1978) explains:
I learn to do service to another, without bearing him any real kindness,
because I foresee, that he will return my service in expectation of another
of the same kind, and in order to maintain the same correspondence of
good offices with me and others. And accordingly, after I have serv’d him
and he is in possession of the advantage arising from my action, he is
induc’d to perform his part, as foreseeing the consequences of his refusal.
The consequence of a refusal translates into some kind of punishment that is seldom
rationally thought out. When someone causes a coordination failure by deviating
from a norm, it annoys everybody else. As far as they are concerned, the punishment
they inflict on the deviant is often just a way of relieving their anger at being
discommoded, but the ultimate reason that their emotions become engaged is that
otherwise the social norm would not survive,
Because they have many efficient equilibria, the equilibrium selection problem
is endemic for repeated games, and so it is not surprising that many of our social
norms are tailored for use in situations that are likely to be repeated either with
one’s current opponent or with some onlooker. When subjects confront the oneshot version of a repeated game in the laboratory, they commonly respond with
the behavior specified by the social norm that evolved for the repeated case. As
with the Ultimatum Game, one then needs to be careful not to mistake what one
is observing as behavior that is adapted to the one-shot game.
Provided that one skips the misleading introductory chapters, a book that describes the results of running a series of canonical experiments in small, undeveloped
societies is instructive on this issue (Henrich et al. (2004, p.376). In her chapter,
Jean Ensminger, comments on why the Orma of Uganda contributed generously in
4 There is a dispute about whether human beings are strong or weak reciprocators. Strong
reciprocators reciprocate because reciprocation is built into their utility functions—they reciprocate
because they like reciprocating. The text applies even with weak reciprocation, in which people
reciprocate only for instrumental reasons.
8
her Public Goods Game:
When this game was first described to my research assistants, they immediately identified it as the ‘harambee’ game, a Swahili word for the institution of village-level
contributions for public goods projects such as building a school. . . . I suggest that
the Orma were more willing to trust their fellow villagers not to free ride in the Public
Goods Game because they associated it with a learned and predictable institution.
While the game had no punishment for free-riding associated with it, the analogous
institution with which they are familiar does. A social norm had been established over
the years with strict enforcement that mandates what to do in an exactly analogous
situation.5 It is possible that this institution ‘cued’ a particular behavior in this game.
If Ensminger is right, then it would be a huge mistake to try to explain the behavior
of the Orma in the Public Goods Game on the hypothesis that their behavior was
adapted to the game they played in her makeshift laboratory. In particular, inventing
other-regarding utility functions whose maximization would lead to generous contribution in the Public Goods Game would be pointless. Ensminger is suggesting
that the subjects’ behavior is adapted to the public goods game embedded in the
repeated game that the Orma play in their daily lives, for which the folk theorem
provides an explanation that does not require us to invent anything at all.
4
The Ultimatum Game
The stress placed on adaptation in the previous section is absent from the work
of most behavioral economists. They choose to ignore the many experiments on
games with money payoffs in which the subjects’ behavior converges—as in public
goods games—on a Nash equilibrium of the game. Instead they focus on a class
of “anomalous” games in which matters are more complex. The leading example is
the Ultimatum Game of Güth et al. (1982), which has been the object of nearly as
much experimentation as the Prisoners’ Dilemma.
In the Ultimatum Game, a sum of money can be divided between Adam and Eve
if they can agree on a division. The rules are that Adam proposes a division and
that Eve is then restricted to accepting or refusing. If she refuses, both players get
nothing. It is said that neoclassical economics necessarily predicts the outcome in
which Eve acquiesces when Adam demands nearly all the money. The idea is that
Eve would be irrational to refuse anything positive and so Adam would be irrational
to offer her anything more than the minimum positive amount.
However, it is a much replicated result that Adams’s modal offer in the laboratory
is a fifty:fifty split, and that Adam would be unwise to offer less than a third of
what is available, since he would then have half a chance of being refused. The
result survives when the amounts of money are made large,6 and when the players
are allowed to gain experience of the game through repeated play (against a new
opponent each time).
6A
dissident note is sounded by Anderson et al. (2010).
9
Camerer (2003, p.24) is typical of the behavioral response to finding that subjects
sometimes refuse low offers of money in favor of nothing: “According to behavioral
game theory, responders reject low offers because they like to earn money but
dislike unfair treatment (or like being treated equally).” That is to say, their desire
for money is modified by a taste for fairness. Fehr and Schmidt’s (1999) theory
of inequity aversion allows this compromise to be quantified by admitting two free
parameters per person that can be fitted to the data quite well—although the claims
made by Fehr and Schmidt that their model with the fitted parameters predicts data
from other games fail to survive critical scrutiny (Binmore and Shaked 2010).
The previous section offers the alternative explanation that subjects are habituated to a social norm that militates against accepting “unfair” demands when these
arise in ordinary life. The second part of the explanation predicts that subjects will
adjust their behavior as they gain experience of the laboratory game, eventually
ending up at a Nash equilibrium of the game. So why does this not happen in the
Ultimatum Game?7 A simple explanation lies in the fact that the Ultimatum Game
actually has many Nash equilibria other than the (subgame-perfect) behavior said
to be the only possible neoclassical prediction, which actually represents a selection
from among the set of all Nash equilibria. In fact, any split of the money whatsoever is a Nash equilibrium outcome in the Ultimatum Game, although this fact is
systematically overlooked in the behavioral literature. For example, if there is $40
to be divided, then it is a Nash equilibrium for Adam to demand only $7 for himself,
and for Eve to plan to accept $33 or more and to refuse anything less.
The neglect of such Nash equilibria in the behavioral literature is not confined
to the Ultimatum Game; it is an endemic feature of the experimental games behavioralists regard as canonical. But there is a good reason why it is a bad mistake
to discard inconvenient Nash equilibria as irrelevant; if the social norm originally
triggered in a laboratory game should actually result in the subjects playing according to one of the Nash equilibria of the laboratory game, then the adaptive process
that might otherwise take subjects to a Nash equilibrium of the game will not easily
swing into action, because the subjects are already operating a Nash equilibrium.
It is not possible to press the argument further without raising technical issues
that are relegated to Appendix 3. However, I hope enough has been said to make
it clear that the Ultimatum Game does not represent a knockdown victory for the
behavioralist position that only social preferences can explain the data.
5
Social Preferences
Recall that social or other-regarding utility functions take into account not only the
welfare of the agent to whom they are attributed, but also the welfare of other
7 I do not know of experiments on the Ultimatum Game in which subjects have experience of
more than ten trials, which is not a large number. Cooper and Dutcher (2009) nevertheless find
evidence of change in the responders’ behavior over time, but the behavioralists are right that
there is not a lot of movement overall.
10
agents. I do not know of anybody who denies that most people must care even
about strangers to some extent. How else are we to explain donations to charity?
Nor is there any doubt that there are saints who behave as though they care a lot
about others. But do enough of the population care sufficiently much about others
to explain the data from the Ultimatum Game and elsewhere?8
Some behavioral economists prefer to pose a more dramatic question. Neoclassical economics is said to be refuted because experiments show that people do not
behave selfishly. So what social preferences should be attributed to subjects in order
to allow the traditional methods of economics to be applied successfully?
The introductory chapters of Henrich et al. (2004) provide a typical summary
of the claims that these behavioralists make. The particular claims that are relevant here concern the extent to which social or other-regarding utility functions
can usefully be said to explain to the data obtained from games like the Ultimatum
Game, which generate results described as “anomalous” because they are (sometimes mistakenly) said to be inconsistent with the neoclassical paradigm (Thaler
1988).
Mainstream economists are cautious about these behavioral claims, partly because they dislike their views being misrepresented, and partly because they suspect
that some of the experimental claims are not genuinely supported by the data.
However, a vocal minority of behavioralists have succeeded in outflanking the skepticism of mainstream economists by appealing directly to a wide range of social and
life scientists with the aid of publications in journals like Science and Nature that
do not normally publish the work of economists (for example, Fehr and Gächter
2002; Bowles and Gintis 2002). They have thereby been successful in discrediting
psycho-sociological explanations of the data (in terms of cultural norms) in favor of
traditional economic explanations (in terms of the optimization of utility functions)
while simultaneously decrying the very economic methodology that they seek to
persuade others to adopt.
Psychologists and sociologists may feel uneasy at such a characterization of
the situation, for is it not true that the behavioralists do succeed in fitting utility
functions to “anomalous” data obtained from games like the Ultimatum Game? The
answer is that they are indeed successful in this enterprise, but that psychologists
and sociologists need feel no unease, because neoclassical economics explains why
their success in so doing is entirely consistent with an explanation of the data in
terms of social norms. If this explanation seems paradoxical, it is only because the
real neoclassical economics—the economics actually taught to students in modern
economic textbooks (for example, Varian 1990)—is quite different from the classical
economics to which the behavioralists have reverted.
Classical economists of the nineteenth century thought of utility as measuring
how much pleasure or pain a person feels. Psychologists commonly believe that
economists still hold this naive view. However, the time has long gone when main8 This question begs the question of whether subjects in the Ultimatum Game are maximizing
anything at all, but we adopt the point of view of an old-fashioned economist here.
11
stream economists thought that a simple model of a mental utility generator is
capable of capturing the complex mental processes that swing into action when a
human being makes a choice. The modern theory of utility has therefore abandoned
the idea that a util can be interpreted as one unit more or less of pleasure or pain.
Neoclassical economists offer no theory at all of why people choose one thing rather
than another. They make no attempt to explain choice behavior at all. They see
this as a job for psychologists or neuroscientists. Neoclassical economics finesses the
problem by assuming that we already know what people choose in some situations,
and uses this data to deduce what they will choose in others. For this purpose,
economists need to assume only that the choice behavior of economic agents is
both stable and consistent.
The theory of revealed preference based on this principle became orthodox more
than fifty years ago, largely as a result of the advocacy of Paul Samuelson (1947).
With appropriate definitions of what consistency means, its theorems assert that
the behavior of consistent agents can be described by saying that they behave
as if they are maximizing something. Whatever this abstract something may be,
necoclassical economists call it utility. Identifying utility with money fits some data
sets reasonably well, but the claims made by some behavioralists that neoclassical
economists always identify utility with money are untrue. What would be the point
of a theory of revealed preference if this were the case?
There is also a strong tendency to gloss over the important fact that a major
difference between classical and neoclassical economists is that only the former
argue that economic agents actually maximize the utility functions attributed to
them. In saying that a consistent agent behaves as if maximizing a utility function,
neoclassical economists adopt an entirely neutral atttitude to what goes on inside
a person’s head. Their theory continues to be relevant to the decisions made by
rational folk who consciously decide what to buy or sell by weighing costs and
benefits, but evolutionary biologists have found that the neoclassical paradigm also
sometimes fits the behavior of spiders or fish rather well, although they presumably
do not consciously decide anything at all. By giving up the bad psychology on which
their subject was initially based, neoclassical economists have therefore created a
theory that can be applied both to conscious and unconscious choice behavior.
Given the theory of revealed preference, it is no great surprise that it is possible
to fit utility functions to the behavior of subjects in the class of canonical games that
generate “anomalous” data. All behavior can be described in terms of maximizing
a utility function, provided only that whatever is behind the behavior causes the
subject to act consistently. In particular, the subjects may be consistently operating
a social norm in the laboratory that is adapted to a real-life game with a strategic
structure different from the game being played in the laboratory.
How hard is it for behavior to count as consistent? Economists of all stripes tend
not to notice that this question deserves an answer. But the fact that all behavior
can be treated as consistent if one is willing to help oneself to a sufficiently large
number of parameters of the right kind means that economic models are always at
risk of becoming tautological, and so incapable of being refuted by new data. (Fehr
12
and Schmidt’s (1999) behavioral theory of inequity aversion, for example, allows
two parameters for each member of the population from which subjects are drawn.)
An historical example may help here. Ptolemy famously elaborated a epicyclic
theory of the movement of the planets. With enough epicycles within epicycles, one
can fit the astronomical data exceedingly well—much better than Kepler’s theory of
ellipses with the sun at one focus (because Kepler’s theory neglects the gravitational
influence of other planets). But we think Kepler’s theory is better, partly because it
succeeds in predicting the movement of new planets, whereas Ptolemy’s theory can
only describe the movement of a new planet after a whole new set of parameters
has been fitted to the data.
The point here is that one cannot claim anything very much from being able to
find parameters that make a behavioral model fit data from one canonical game.The
acid test is whether a model whose parameters have been calibrated using the data
from one game is able to predict the data from other games. I cannot claim to
know all the literature, but I do not believe the behavioralists have had any success
at all in this endeavor. When challenged on this point, behavioralists used to quote
the work of Fehr and Schmidt (1999) on inequity aversion as the leading counterexample. Avner Shaked and I therefore made a detailed study of numerous papers
in which Fehr and Schmidt claim to have predicted the data in various other games
after “calibrating” their model using data from the Ultimatum Game (Binmore and
Shaked 2010). But their claims do not survive close examination. It even turns
out to be logically impossible to calibrate their model using only data from the
Ultimatum Game as they claim to have done.9
The fact that we have shown Fehr and Schmidt’s claims to be empty does
not exclude the possibility that their social utility function (or some alternative
social utility function) might nevertheless pass a properly conducted predictive test.
This will only be possible in a restricted context, if only because the experimental
evidence in most games shows that subjects’ behavior changes over time (Binmore
2007). Even the examples that Fehr and Schmidt take from Fehr and Gächter
(2000) clearly show the subjects’ behavior changing over time in some conditions.
Experienced subjects therefore behave differently from inexperienced subjects. If
one fits social utility functions to the behavior of inexperienced subjects in such
games (as is commonplace), one will not even succeed in predicting the behavior
of the same subjects playing the same game (with different partners) ten minutes
later.
However, there is a minority of games like the Ultimatum Game in which the
subjects’ behavior does not seem to change much over time. Can social utility functions whose parameters have been calibrated using data from such games sometimes
be used to predict data from different games? Surely there must be cases where
this happens, so that the social norm that is being honored is indeed representable
in terms of social preferences, as many behavioral economists maintain is always the
9 All one can say of the vital parameter β when a proposer makes the modal offer of a fifty-fifty
split is that β ≥ 0.5.
13
case. However, the long and careful research that will be necessary to tease out the
extent to which social preferences can genuinely predict behavior across some reasonably wide class of games is unlikely to generate anything like the excitement that
behavioral enthusiasts for the theory of social preferences are currently enjoying.
6
Conclusion
Behavioral economists often talk as though social preferences and social norms are
terms that can or should be used interchangeably. This paper argues that it is a bad
mistake to blur the distinction between an equilibrium selection device and a utility
function that takes into account the welfare of others. It goes on to explain how
it is possible for behavioralists to fit social utility functions to experimental data
without coming anywhere near refuting the hypothesis that the data is generated
by subjects honoring a culturally determined social norm. Finally, it draws attention
to the failure of the social preference hypothesis to predict new data, although there
must surely be situations where this is possible.
Acknowledgements
The financial support of both the British Economic and Social Research Council
through the Centre for Economic Learning and Social Evolution (ELSE) and the
British Arts and Humanities Research Council through grant AH/F017502 is gratefully acknowledged.
References
Anderson S, Ertac S, Gneezy U, Hoffman M, List J (2010) Prices matter in Ultimatum Games (Working paper, Copenhagen Business School)
Axelrod R (1984) The Evolution of Cooperation. Basic Books, New York
Binmore K (2005) Natural Justice. Oxford University Press, New York
Binmore K (2007) Does Game Theory Work? The Bargaining Challenge. MIT
Press, Boston
Binmore K (2008) Do conventions need to be common knowledge? Topoi 27:17–27
Binmore K, Gale J, Samuelson L (1995) Learning to be imperfect: The Ultimatum
Game. Games and Econ Behav 8:56–90
Binmore K, Shaked A (2010) Experimental economics: Where next? J of Econ
Behav and Org 73:87–100
Bowles S, Gintis H (2002) Behavioural science: Homo reciprocans. Nature 415:125–
128
14
Camerer C (2003) Behavioral Game Theory: Experiments in Strategic Interaction.
Princeton University Press, Princeton
Camerer C, Harless D (1994) The predictive utility of generalized expected utility
theories. Econometrica 62:1251–1290
Cooper D, Dutcher, E (2009) The dynamics of responder behavior in Ultimatum
Games: A meta-study (Working paper, Florida State University).
Eckel C, Gintis, H (2010) Blaming the messenger: Notes on the current state of
experimental economics. J Econ Behav and Org 73:105–110
Fehr E, Gachter, S (2000) Cooperation and punishment in public goods experiments.
Amer Econ Rev 90:980–994
Fehr E, Gachter S (2002) Altruistic punishment in humans. Nature 415:137–140
Fehr E, Gintis H (2007) Human motivation and social cooperation: Experimental
and analytic foundations. Ann Rev Soc 33:43–64
Fehr E, Schmidt K (1999) A theory of fairness, competition and cooperation. Quart
J Econ 114:817–868
Gintis H (2009) The Bounds of Reason: Game Theory and the Unification of the
Behavioral Sciences. Princeton University Press, Princeton
Guth W, Schmittberger R, Schwarze B (1982) An experimental analysis of ultimatum bargaining. J Econ Behav and Org 3:367–388
Henrich J et al (2004) Foundations of Human Sociality: Economic Experiments and
Ethnographic Evidence from Fifteen Small-Scale Societies. Oxford University Press,
New York
Hey J, Orme C (1994) Investigating generalizations of expected utility theory using
experimental data. Econometrica 62:1291–1326
Hume D (1978) A Treatise of Human Nature (2nd ed). Clarendon Press, Oxford
(first published 1739)
Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under
risk. Econometrica 47:263–291
Ledyard J (1995) Public goods: A survey of experimental research. In: Kagel J,
Roth A (eds) Handbook of Experimental Economics. Princeton University Press,
Princeton
Lewis D (1969) Conventions: A Philosophical Study. Harvard University Press,
Cambridge
Nash J (1950) The bargaining problem. Econometrica 18:155–162
Rawls J (1972) A Theory of Justice. Oxford University Press, Oxford
Roth A, Erev I (1995) Learning in extensive-form games: Experimental data and
simple dynamic models in the medium term. Games and Econ Behav 8:164–212
Sally D (1995) Conversation and cooperation in social dilemmas: A meta-analysis
of experiments from 1958 to 1992. Rat and Soc 7:58–92
15
Samuelson P (1947) Foundations of Economic Analysis. Harvard University Press,
Cambridge
Schelling T (1960) The Strategy of Conflict. Harvard University Press, Cambridge
Smith V (1991) Papers in Experimental Economics. Cambridge University Press,
Cambridge
Thaler, R (1988) Anomalies: The Ultimatum Game. J Econ Persp 2:195–206
Thaler R, Sustein C (2008) Nudge: Improving Decisions on Health, Wealth, and
Happiness. Yale University Press, Newhaven
Varian H (1990) Intermediate Microeconomics (2nd ed). Norton, New York
Appendix 1: Coordination Games
Figure 1 shows two coordination games.The Driving Game is a game of pure coordination, because the players have precisely the same aims. If they could discuss
the game before playing it, they would therefore have no difficulty in resolving their
coordination problem. But the game must be played without any pre-play negotiation. The Battle of the Sexes is a game of impure coordination. The players
face a more difficult coordination problem in this game, because they have different
preferences over its possible solutions.
left
left
right
right
1*
1*
0
boxing
0
0
0
boxing
1*
ballet
1*
Driving Game
ballet
1*
2*
0
0
0
0
2*
1*
Battle of the Sexes
Figure 1: Two coordination games. A politically incorrect story accompanies the
Battle of the Sexes. Adam and Eve are a newly married couple honeymooning
in New York. At breakfast, they discuss whether to go to a boxing match or the
ballet in the evening, but fail to make a decision. They later get separated in the
crowds and now each has to decide independently where to go in the evening.
The two players in the games of Figure 1 are called Adam and Eve. Adam
chooses a row and Eve independently chooses a column. Adam’s payoff is written
in the south-west corner of each cell and Eve’s payoff in the north-east corner. The
stars indicate best replies. For example, the fact that ballet is Eve’s best reply to
Adam’s choice of ballet in the Battle of the Sexes is indicated by starring the payoff
of 2 that she will receive if these strategies are played. Cells in which both payoffs
are starred correspond to (Nash) equilibria in pure strategies.
16
At a Nash equilibrium, both players are simultaneously making best replies to
the strategy chosen by their opponent. If an adaptive process that always moves in
the direction of higher payoffs converges at all, it must converge on a Nash equilibrium, which is therefore a minimal requirement for evolutionary stability (whether
biological or cultural). Aside from their two Nash equilibria in pure strategies, each
game also has a Nash equilibrium in mixed (or randomized) strategies. For example,
it is a Nash equilibrium in the Driving Game for each player to toss a fair coin to
decide on which side of the road to drive. Both mixed equilibria are inefficient, in
that each player prefers either of the two pure equilibria to the mixed alternative.
Coordination games usually have many equilibria. The games of Figure 1 are
exceptional in only having two equilibria (in pure strategies). If the people playing
such a game are to succeed in coordinating their behavior, they need to aim for the
same equilibrium. In the Driving Game, they must both drive on the left or both
drive on the right. Social norms exist to solve such equilibrium selection problems.
Different social norms sometimes evolve in different societies to solve the same
problem.10 For example, the Japanese drive on the left and the French on the right.
Appendix 2: The Evolution of a Social Norm
An experiment I carried out with colleagues at the University of Michigan can be
used to illustrate both the two levels of adaptation mentioned in Section 3 (Binmore
2007, chapter 2). Since the adaptation took place within a laboratory, it illustrates
how subjects can change their behavior over time if offered the opportunity. As
in experiments on the one-shot Prisoners’ Dilemma, such changes in behavior over
time are standard. When one fails to see such adaptation in the laboratory, as in
the Ultimatum Game, some explanation is required. However, it is the second level
of adaptation that is perhaps more interesting. This is the level at which social
norms emerge in a society as solutions of real-life coordination problems.
The coordination game used was the Nash Demand Game, which is a primitive
bargaining game in which Adam and Eve can both gain if they can agree on how to
cooperate (Nash 1950). Each player simultaneously makes a demand. An agreement
is deemed to have been reached if the demands made are jointly feasible. The shaded
region in Figure 2 is the set of pairs of demands that count as feasible. If a pair of
demands lies outside this region, each player gets nothing.
The pure version of the game poses the equilibrium selection problem in an acute
form, because every efficient outcome is a Nash equilibrium of the game. (There are
also inefficient equilibria, including one in which both players get nothing because
they both make demands that cannot be met however reasonable the other player’s
demand.) As in the Battle of the Sexes, Adam and Eve are not indifferent between
10 I think that biological evolution sometimes provides a deep structure that limits what social
norms are possible for the human species (Binmore 2005), but it is social or cultural evolution that
is responsible for the differences between norms that arise in different cultures or within different
contexts in the same culture.
17
Figure 2: Evolution of a social norm. The shaded region is the feasible set for an
experimental version of the Nash Demand Game. Groups of subjects were conditioned
to play one of four putative social norms,labeled E, K, N , and U . The subjects
initially played ten trials against robots. Their screens showed small boxes indicating
the demands a particular robot made when last occupying the role of player I or player
II. The demands shown in the figure are each box’s initial position. During the ten
trials, different groups saw the boxes gradually converge on different social norms. This
device proved adequate to condition the subjects on a norm. When they knowingly
began to play each other, they began by making demands close to the norm on which
they had been conditioned. However, after thirty repetitions, all groups were playing
one of the efficient Nash equilibria of the game, irrespective of their initial conditioning.
In the figure, the efficient Nash equilibria lie on the frontier of the feasible set within
the large box. At least one group got close to each of these efficient equilibria.
the efficient equilibria. If one of a pair of equilibria assigns Adam a higher payoff,
then it assigns Eve a lower payoff.
In the experiment, we paid tribute to Nash by using a version of the game in
which he chose to fuzz the boundary of the feasible set slightly. As a result, the set
of efficient equilibria is reduced from being all points on the outer boundary of the
feasible set to the subset of the boundary enclosed in a box in Figure 2.11 It turned
out that this modification to the design generated much more interesting results
than would otherwise have been possible.
It is not normally possible to control the social conditioning with which subjects
enter a laboratory, but the conditioning the subjects have received on what counts
as fair in their society is clearly important when they are asked to play a game
11 Nash fuzzed the boundary to reduce the set of equilibria to the single point N , which is the
Nash bargaining solution of the game. We ended up with a larger set, because the use of a
computer meant that the strategy sets were necessarily discrete.
18
that everybody can see is a kind of bargaining game. However, we did our best to
frame the experiment in a manner unlikely to trigger any particular fairness norm
brought in from the outside. Instead, we sought to provide a substitute for outside
social conditioning within the laboratory by asking the subjects to knowingly play
ten “practice” games against against robots programmed to converge on one of four
outcomes E, N , K, and U . The putative social norm E (for egalitarian) gives each
player an equal payoff, and hence instantiates Rawls’ (1972) difference principle.
The putative social norm U (for utilitarian) maximizes the sum of the two players’
payoffs. The two other putative social norms correspond to bargaining solutions
from the game theory literature.
We found it surprisingly easy to condition different groups of subjects on any
of these norms. After ten trials playing the conditioning robots, they then played
thirty trials with randomly chosen partners from the same experimental group. At
the beginning of the thirty trials, the subjects played as they had been conditioned.
At the end of the thirty trials, each group ended up at one of the efficient Nash
equilibria of the game. Neither the egalitarian outcome E nor the utilitarian outcome
U were Nash equilibria in the experiment, and so our attempt to impose them as
social norms on our subjects was a failure.12
Different groups ended up at different Nash equilibria of the game. Indeed, the
collection of experimental outcomes at the end of the game covered the whole set
of Nash equilibria. If each group is regarded as a laboratory minisociety, then their
learning to coordinate on a particular equilibrium can be regarded as exemplifying the
process of cultural evolution by means of which social norms come to be accepted
in real societies.
After the experiment, we asked subjects what they regarded as fair in the game
they had just played. It turned out that the equilibrium on which a subject’s group
had converged was a good predictor of what that subject afterwards said was fair
in the game. That is to say, the subjects were willing to treat the social norm that
they had observed evolving in the laboratory as a legitimate fairness norm. In a
situation that does not match anything to which they were habituated, our subjects
therefore showed little sign of having some universal fairness stereotype built into
their utility functions.
Appendix 3: Ultimatum Game
Not only does the Ultimatum Game have many Nash equilibria, but computer simulations show that models of adaptive learning can easily converge on one of the
infinite number of Nash equilibria other than the equilibrium that behavioralists say
is the unique neoclassical prediction (Binmore, Gale and Samuelson 1995).
The same computer simulations show that one must expect any convergence
that takes place to be very slow. (See also Roth and Erev 1995). Figure 3 shows
12 Both
E and U are approximate Nash equilibria if amounts of a quarter or less are neglected.
19
Figure 3: Convergence in the Ultimatum Game. The figure shows one of many
simulations of a perturbed version of the replicator dynamics from Binmore,
Gale and Samuelson (1995). Pairs of players are chosen at random from a
population of proposers and a population of responders to play the Ultimatum
Game. Proposers are characterized by how much of the available $40 they will
offer if chosen to play. Responders are characterized by the least amount they
will accept. The upper and lower diagrams respectively show the effect of evolution in the two populations. The vertical axis shows the proportion of various
types in each population. The original distribution of types is concentrated
about a notional social norm in which proposers offer $33, which responders
accept, planning to refuse anything less. The system would not move from
this norm if there were no noise, because (7, 33) is a Nash equilibrium outcome (like any other split of the $40), but the existence of noise destabilizes
the system. Eventually, it converges on the outcome (30, 10) (which is not
a subgame-perfect outcome), but the immediate point is that it takes some
6,000 iterations before much movement from the original norm is discernible.
20
one of the very large number of computer simulations reported by Binmore et al.
(1995).
The original sum of money is $40 and the simulation begins with Alice offering
Bob about $33, leaving $7 for herself. One has to imagine that the operant social
norm in the society from which Alice and Bob are drawn selects this Nash equilibrium
outcome from all those available when ultimatum situations arise in their repeated
game of life. However, this split (like any other split) is also a Nash equilibrium
outcome in the one-shot Ultimatum Game.
The figure shows our (perturbed replicator) dynamic13 leading the system away
from the vicinity of this (7, 33) equilibrium. The system eventually ends up at a
(30, 10) equilibrium.
This final equilibrium does not yield the split (40, 0), which behavioralists insist
is the unique neoclassical prediction. But this fact is not the point of drawing
attention to the simulation. What is important here is that it takes some 6,000
periods before our simulated adaptive process moves the system any significant
distance from the vicinity of the original (7, 33) equilibrium. This very large number
of periods has to be compared with the 10 or so trials commonly considered ample
for adaptive learning to take place in the laboratory.
More generally, if a society’s social norms lead inexperienced players to start
playing close to a Nash equilibrium of a one-shot laboratory game, then, if there
is any movement away from the original Nash equililibrium at all due to adaptive
learning, we must expect such movement to be slow at the outset, although the
Ultimatum Game is presumably exceptional in taking such an enormously long time
for the adaptation process to converge.
13 The replicator dynamics is often used to model adjustment processes in games. Nobody thinks
that it is anywhere near adequate by itself to predict how real individuals learn, although Roth and
Erev [?] find that perturbed versions are not too bad at tracking average behavior in some subject
populations.
21