Personality and Social Psychology Review
2003, Vol. 7, No. 1, 2-19

Copyright ( 2003 by
Lawrence Erlbaum Associates, Inc.

The Logic of Social Sharing: An Evolutionary Game Analysis ofAdaptive Norm Development
Tatsuya Kameda
Department of Behavioral Sciences
Hokkaido University, Japan

Masanori Takezawa
Max Planck Institute for Human Development, Germany

Reid Hastie
Graduate School ofBusiness
University of Chicago

Although norms can potentially serve useful constructs to understand humanminds, being fundamentally social in evolutionary as well as cultural senses, there
are as yet no useful psychological theories of adaptive norm development. This article provides an illustrative model about how a norm emerges in a society. We focus on the "communal-sharing norm" in primordial societies, a norm designating
uncertain resources as common properties to be shared with other members. Basedon anthropologicalfindings, we develop a theory about how the communal-sharing
norm emerges and is maintained. Then, using evolutionary computer simulations,
we test several hypotheses about the conditions under which the norm will dominate social resource sharing. We further test behavioral implications of the norm,
demonstrating that uncertainty involved in resource acquisition is a key factor thattriggers the psychology of sharing even in highly industrialized societies. Finally,
we discuss the importance of norm construct for analyzing the dynamic relation between minds and society.
The notion of "social norm" has a distinctive status in modem psychology. Although the concept has
been one of the most central constructs in other social
sciences such as sociology, law, political science, andanthropology (e.g., Axelrod, 1986; Coleman, 1990;
Cooter & Ulen, 1996), its theoretical status in psychology has been less firm. Despite an early emphasis on this notion (Asch, 1952; Jacobs & Campbell,
1961; Sherif, 1936), even in modern social psychology, the role of social norm construct for explaining
behavior is often criticized. Some theorists argue thatin many realistically complex social contexts, a variety of social norms are potentially applicable to agiven social situation, some of which are mutually incompatible. Thus, unless we can tell which norm is
actually operating in the situation in advance, thevalue of social norm as an explanatory construct for
behavior is limited (Darley & Latane, 1970; Krebs &Miller, 1985).
However, rather recently, the social norm andother collective constructs have been gaining renewedinterest. One manifestation is the growth of cultural
psychology that emphasizes dynamic processes between minds and society; collective constructs suchas norms, conventions, values, and customs are core
concepts in this discipline (e.g., Cohen, 2001; Markus& Kitayama, 1991; Nisbett & Cohen, 1996; Triandis,1994). The emergence of evolutionary psychology
maintaining that, as a group-living species, our minds
are tuned to be socially adaptive (e.g., Barkow,
Cosmides, & Tooby, 1992; Campbell, 1975), is also
an impetus for the resurgence of social constructs. As
Cialdini and Trost (1998) argued, because the existence of social norms is one of the most essential
characteristics of group lives, this construct may havea great potential to understand humans as fundamentally social animals. Sharing such a perspective, we
aim to provide a model analysis of the emergence andmaintenance of a social norm in this article, using aformal method called "evolutionary game analysis"(Maynard Smith, 1982).

We are grateful to Robert Boyd, R. Scott Tindale, Joe
Whitmeyer, Toshio Yamagishi, and Nobuyuki Takahashi for theirhelpful comments on an earlier version of this article. Work on thisarticle was supported by the Grant-in-Aid for Scientific Research(1 1610096) to Tatsuya Kameda from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
Requests for reprints should be sent to Tatsuya Kameda, Department
of Behavioral Science, Hokkaido University, Bungakubu, NlO W7
Kita-ku, Sapporo 060-0810 Japan. E-mail:tkameda@let.hokudai.acjp

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EVOLUTION OF SOCIAL NORMS

Social Norm as an Emergent Property
from Autonomous Interaction
Although norms have been conceptualized in various ways by a variety of researchers, Cialdini and
Trost's (1998) definition in a recent review article isone of the clearest. According to their definition, "Social norms are rules and standards that are understoodby members of a group, and that guide and/or constrainsocial behavior without the force of laws. These norms
emerge out of interaction with others, they may or may
not be stated explicitly, and any sanctions for deviatingfrom them come from social networks, not the legal
system" (p. 152).
Put differently, norms are socially-shared rules thatemerge and are sustained through people's autonomous interaction without formal regulating authorities
or forces such as laws.
This definition highlights emergence andsustainability of social norms as core issues for any
theory of norms. That is, to elaborate the norm construct fully, we need to understand how social norms
can emerge voluntarily through people's interaction
without external regulating forces (although as notedby some theorists, the norms can eventually be manifested in more formal sanctions such as explicit laws;
cf. Axelrod, 1986, p. 1106). This theoretical perspective is shared by other social scientists (e.g., Axelrod,
1986; Coleman, 1990), yet we do not have a reasonable
theory about norm development. For example, mostsocial psychological research has studied developmentof arbitrary, adaptively-irrelevant norms such as conformity to a response norm for the visual autokinetic
effect or to transient fashions or fads (e.g., Jacobs &Campbell, 1961; Latane & L'Herrou, 1996; Sherif,1936). Such a focus on arbitrary norms may have inadvertently led us to assume that social-cultural learningper se is a sufficient mechanism for norm development.
Although social-cultural learning is vitally importantfor norm development (Boyd & Richerson, 1985), amore fundamental question may be why some beliefs
are acquired socially and are maintained as a sharedrule, whereas other beliefs are not. A theory of norm
development must address this question explicitly, explaining why particular behavioral-cognitive patterns(and not other patterns) proliferate in a society and aremaintained as shared rules and standards.
Toward such a theoretical end, this article develops
a theory about how a well-defined social norm, the"communal-sharing norm" (Kaplan & Hill, 1985;
Kaplan, Hill, & Hurtado, 1990), is realized in a sustainable manner across many independent societies. Thisis a norm about social exchange, designating uncertain
resources as common properties to be shared withother members of a social group. Our purpose here is toillustrate how a useful theory about norm-emergencecan be developed, with the communal-sharing norm asa guiding example. In this article, we start with anthropological findings about the communal-sharing normin basic, primordial societies. Based on these findings,
we develop a theory about how the communal-sharing
norm emerges and is maintained, using a game-theoretic method called "evolutionary game analysis"(Gintis, 2000; Maynard Smith, 1982). We then test several behavioral implications of the communal-sharing
norm in highly industrialized societies such as theUnited States and Japan. Finally, we discuss importance of social-norm construct as a conceptual tool toanalyze the micro-macro relation between individual
minds and society systematically, viz., a useful theoretical device to link psychology to other social sciences.

Communal-Sharing Norm in
Hunter-Gatherer Societies
Anthropological Findings About
Sharing
Sharing important resources such as food widely beyond direct kin is one of core features characterizing human societies. Although a primitive form of food sharing is known in several primates including
chimpanzees, bonobos, and capuchin monkeys (see deWaal, 1996, for a comprehensive review), no primatesother than humans have a broad social-sharing system.
Indeed, Issac (1978) argued that the social-sharing system played vital functions for hominids to evolve into
the human species in its present form. Likewise, evolutionary psychologists claim that the need for social exchange and sharing promoted evolution of domain-specific cognitive mechanisms, such as a cheater-detection
algorithm (Cosmides, 1989) and Machiavellian intelligence (Byrne, 1995).
Research on social exchange and sharing in basic,
primordial societies to explore its origin and early forms
has been a prime agenda in anthropology. Among various studies, Kaplan & Hill's (1985; Kaplan et al., 1990)
observation ofthe Ache (pronounced Ah-chay) foragers
living in the lowland subtropical eastern Paraguay isparticularly pertinent to this article. These researchers
found that food transfers among the Ache show markedly different patterns between hunted games (e.g., peccary, monkey, deer) and collected resources (e.g., vegetables, fruits). Hunted game, especially when large inpackage sizes, tends to be shared widely across many
community members beyond the acquirer's family. Although a substantial portion of collected resources isstill given to nonfamily members, hunted game is much
more likely to be the target of communal sharing, interms of both "depth" (the proportion of the food given
away to nonfamily members) and "breadth" of sharing(the number of nonfamily members who receive theshare; cf. Gurven, 2002). Related findings have also
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KAMEDA, TAKEZAWA, & HASTIE

been obtained for other hunter-gatherer societies (cf.
Cashdan, 1989; Gibson, 1988; Woodburn, 1982). Theseobservations suggest that the properties of the resources
affect deeply how they may be transferred among community members. Although the principle ofkin-sharing
essentially operates for collected resources, sharing
across the entire community is often observed for
hunted game. This raises the theoretically importantquestion as to why different sharing norms emerge andare maintained for different resources. More specifically, why is hunted game shared communally across
many band members beyond the acquirer's direct kin(cf. Hamilton, 1963)?

"Communal Sharing as Risk
Reduction" Hypothesis
Kaplan and Hill (1985) explained the difference interms of the degree of uncertainty involved in resource
acquisition. Although provision of collected resources(e.g., vegetables, fruits) is relatively stable, acquisitionof meat is a highly variable, uncertain prospect. On average, there is a 40% chance that an Ache hunter will
come back empty-handed (Kaplan et al., 1990). It isthus essential for them to manage the variance associated with meat-acquisition, securing a stable supply of
the resource. Storage by freezing is an obvious individual solution to reduce the uncertainty, but such a technique is not readily available in hunter-gatherer societies. Other storage methods such as drying and smoking
meat may result in nutrient loss. Kaplan and Hill
(1985) argued that, instead, the sharing system functions as a collective risk-reduction device. By including more individuals in the risk-pooling group, thevariance in food supply decreases exponentially. Onceestablished and maintained, the sharing system that includes many individuals can buffer the variance in the
resource supply collectively.
The risk-reduction hypothesis is intuitively appealing. Yet, we think that this explanation still leaves one
critical problem unanswered-the problem of egoism insocial dilemmas (cf. Dawes, 1980; Hardin, 1968;
Messick & Brewer, 1983). According to Hawkes,
O'Connell, and Blurton Jones (2001), hunted meat, especially when large, is often regarded as a common
property in hunter-gatherer societies; the process ofmeat distribution is more like appropriation from thepublic domain (see also Woodburn, 1998). Then, what if
some individuals behave as egoists who just share otherpeople's acquisitions but are never willing to share theirown acquisitions with others? Those egoistic individuals should be better off than those who are loyal to communal-sharing norm. If such egoists, who outperformthe loyal individuals in terms of individual fitness, proliferate in a group, then the social-sharing system should
inevitably collapse. The risk-reduction explanation per
se is incomplete in this sense, because it is silent about

how the emergence and proliferation of such egoists isprecluded in the group. A seemingly "obvious" mechanism to solve this puzzle would be social sanctioning,
but as we discuss later in detail, the maintenance of sucha sanctioning mechanism requires elaborate safeguardsof its own.

Tolerated-Theft Hypothesis
A different anthropological explanation that is freefrom the public-goods problem is a tolerated-theft hypothesis (Bliege Bird & Bird, 1997; Blurton Jones,
1984, 1987; Hawkes, 1992). This hypothesis explainssocial sharing as a consequence of contests over the acquired resource. A common feature of hunter-gatherer
societies is a lack of "privacy;" it is highly difficult toconceal acquisitions of food from other people's eyes(cf. Cashdan, 1989). Now, imagine a situation where an
acquirer of a resource is challenged by another individual having failed to obtain the resource ("nonacquirer"
hereafter). Suppose also that each individual's utility
function for the resource is marginally diminishing, asassumed in economics and psychology. Then, the gain(i.e., increase in utility) that the nonacquirer receives bysnatching one unit of resource from the acquirer should
be larger in value to the nonacquirer, than the loss thatthe theft causes for the acquirer. One unit ofthe resource
is more valuable for the nonacquirer than the acquirer.
This means that the nonacquirer will be willing to bear alarger fighting (or other punishment) cost to snatch one
unit of resource than the acquirer will accept to defend
the same one unit of the resource. Given this asymmetry,it is adaptive for the acquirer to avoid the contest with the
nonacquirer and "tolerate the theft." Because such an
asymmetry exists until both parties hold the sameamount ofthe resource, communal sharing should be theresult.
The tolerated-theft hypothesis explains how thestate of communal sharing is a result of the individual-level self-maximizing adaptation. The explanation
is logically coherent and intuitively plausible for sharing in a pairwise situation (i.e., one acquirer and one
nonacquirer). However, what if we apply this model toa group situation composed of more than two individuals? In an n-person group situation, the asymmetry inresource level can be defined (2 for pairs. However,given multiple pairs, "equal-sharing" solutions in specific pairs do not necessarily terminate the contest process in the group, as long as asymmetries remain between other individuals. The pairwise tolerated-theftsolution can logically lead to infinite contests in thegroup; there must be some collective termination
mechanism for the potentially endless contests. We
think that a generalized communal-sharing norm is aprime candidate for such a collective termination

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EVOLUTION OF SOCIAL NORMS

mechanism. Indeed, in the Ache society, sharing of uncertain resources is conducted in an orderly manner,and the hunter who has acquired the game is expected
to behave modestly (Kaplan & Hill, 1985; see also
Cashdan, 1989). People seem to share beliefs about
what to do with uncertain resources-a genuine communal-sharing norm.
To recapitulate, the risk-reduction hypothesis explains why people value the communal-sharing rule,
but leaves the issue of its sustainability in the society,where there may be "egoists," unanswered. In contrast,
the tolerated-theft hypothesis explains why a state ofcommunal sharing may emerge, but ignores the factthat such a sharing often reflects an expressible, generally-shared rule (cf. Fiske, 1992). Thus, to combine
these two explanations, we need a theory about how
the communal-sharing norm can emerge and be maintained as a socially-shared rule in primordial environments. We now apply evolutionary game analysis todevelop, explicate, and justify such a norm.
An Evolutionary Game ofCommunal-Sharing Norm

Evolutionary game analysis is a game-theoretic approach initially proposed by Maynard Smith (1982) inevolutionary biology and later introduced into social sciences by Axelrod (1984; Axelrod & Hamilton, 1981).
This approach represents various behavioral-cognitive
properties of individuals as strategies in a game, and exIFood-sharing in hunter-gatherer societies is currently the topic ofa vigorous debate in anthropology, and various theoretical models
have been proposed (see Winterhalder, 1997, for a review). Besides
the theories discussed in this article, two other theories are notable.
Reciprocal altruism theory (e.g., Gurven, Allen-Arave, Hill, &Hurtado, 2000; Trivers, 1971) views a food transfer between two
nonkin individuals as a conditional exchange conducted within thepair repeatedly-meat for meat, meat for help in emergency, and soforth. This theory assumes that an acquirer shares the resource withother individuals in a pairwise, one-to-one manner ("restricted exchange;" Ekeh, 1974), rather than providing it to the community as apublic property ("generalized exchange;" Ekeh, 1974; Kameda et al.,2002). Costly signaling theory (e.g., Smith & Bliege Bird, 2000;
Sosis, 2000) maintains that an individual's hunting and sharing activity often serves as an honest and reliable signal (Zahavi, 1975) about
the individual's desirable qualities such as vigor, stamina, good intention, and so forth. Individuals with these qualities are more likely to be
selected as mates or allies, which provides an incentive for the individuals to share. These theories, including the one proposed in this article,
make differential predictions about details of actual food-transfer patterns (e.g., who demands social sharing most vigorously, what kind of
sanctioning is viable) and about forms ofpotential free-riding in foodprovision (e.g., who goes out for hunting, who engages in sanctioning). These predictions should be examined empirically in future
work. However, because our purpose here is to present a model analysis of adaptive norm development using communal sharing as an illustration (rather than to differentiate the specific theories about foodtransfers), we do not discuss these predictions in this article. Interested
readers are referred to, for example, Gurven (2002), Kameda et al.
(2002), and Smith and Bliege Bird (2000).

amines how each of the strategies performs in the game,against other strategies, in terms of net profit. Different
from the classical game theory, this approach does not assume "rational actors" with unlimited information-processing capacity. Instead, analogous to biological evolution, the evolutionary game theory assumes that a
strategy, which may be limited in terms of informa-tion-processing capacity yet performs better than otherstrategies in terms of net profit, proliferates gradually inthe population. In social scientific applications, suchchanges are not necessarily evolutionary but may reflect,
most notably, social imitative learning of successful strategies in a group (Gintis, 2000).
Evolutionary game analysis enables systematic
analysis of theoretical issues such as whether the interaction among individual behavioral-cognitive strategies leads to a stable collective state (similar to an equilibrium), in which the population is dominated by acertain strategy (or a set of strategies) and no further
changes occur. Because a social norm refers to a socially-shared (and valued) stable set of behavioral-cognitive properties (Cialdini & Trost, 1998),
this approach is suited to analyze how a particular
norm can emerge and sustain in a society. Althoughstill new to psychology, evolutionary game analysis isa powerful tool theoretically to analyze how asociocognitive system develops.2
Overview of Our Model: Two Main

Assumptions
Now we propose an evolutionary game model of theemergence and maintenance of the communal-sharing
norm in basic, primordial environments. Before explaining behavioral strategies in the game, let us define
two main characteristics of our model.
First, how shall we operationalize uncertainty in themodel? High uncertainty is a key feature characterizing theacquisition of valuable resources (e.g., hunted meats) inprimordial environments. There are individual differences
as to hunting skills, yet luck still explains a large varianceof a hunter's performance (Cashdan, 1989; Kaplan et al.,1990). Given this, we operationalize such uncertainty byasynchronicity in successful resource acquisition among
members (Gurven, 2002). Because resource acquisition isa highly variable process, the number of individuals wholuckily acquire the resource at one time point is quite
small, whereas most individuals remain unsuccessful. This
operationalization is consistent with Kaplan and Hill's
2See Liebrand and Messick (1996) for recent applications ofevolutionary games to the emergence of joint cooperation in social
dilemmas. Although not necessarily focusing on the emergence ofsharing norms, some of the models in the volume used the aforementioned evolutionary algorithm, rather than the classicalgame-theoretic approach, to consider "evolvability" of cooperative
strategies in an N-person setting. The model we develop in this article shares this feature.

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KAMEDA, TAKEZAWA, & HASTIE

Basic Model

Dependingonabehavioral

strategies, some individuals
may demand communal
sharing of the resource.
Refusal of the acquirer toshare the resource leads tofights that may incur cost C
to each loser.
Figure 1. A schematic representation of the evolutionary game of communal sharing.

(1985) view that communal sharing reduces high variancein resource acquisition statistically.
Second, in line with the tolerated-theft hypothesis,
we assume that nonacquirers can challenge the acquirer
to share the resource. The difference from the tolerated-theft model is that such challenges are not conducted on a pair-by-pair basis where the acquirer and a
nonacquirer interact to "share" the resource just between the two. Instead, we assume that nonacquirers demand communal sharing of the resource, designating
the resource to be common property. In other words, weconceptualize a potential contest between the acquirer
and nonacquirers as a conflict about whether the resource should be private or public property (cf. Hawkes
et al., 2001). This assumption reflects the aforementioned problem in the original tolerated-theft hypothesisthat pairwise "sharing" can lead to endless contests in agroup setting.
Figure 1 summarizes these two assumptions graphically. We presume a social situation in which, after anindividual luckily acquires a resource of value V individuals engage in social interaction over the resource
as determined by their respective behavioral strategies.
If some nonacquirers demand communal sharing of theresource and the acquirer declines it, then contests
arise over the resource that incur cost, C (e.g., physicalinjury), to each loser. With these two key features inmind, let us define behavioral strategies in the following section.

Four Basic Behavioral Strategies
When high uncertainty is involved in resource acquisition, an individual always faces two kinds of deci-sion problems: How to behave when the individual
happens to be an acquirer of resource? And, how to behave when the individual happens to be a nonacquirer?
As shown in Table 1, we formulate four basic behavioral strategies in the game, as a combination of two
substrategies under each of these two situations.
The four cells in Table 1 specify how an individual
adopting a given strategy (named "communal sharer,""egoist," "saint," or "bourgeois," respectively) behaves
with reference to uncertain resource acquisition. Thesebehavioral strategies differ in their underlying "ideology" about what to do with uncertain resource-to be
privatized or to be communalized. For example, a communal sharer is the purest supporter of the communal-sharing ideology, provisioning resource that he or
she has acquired as a common property and demanding
communal sharing of resource that another individual
has acquired. An egoist and a saint are located somewhere between the two poles. An egoist claims private
ownership of the resource that he or she has acquired,but demands communal sharing of resource acquiredby another.3 A saint is a mirror image of the egoist,provisioning his or her resource as a common property
but granting another acquirer's private ownership.
Finally, a bourgeois's ideology is exactly the oppositeof the communal-sharing ideology, claiming private
3Notice that an egoist defined here demands communal sharingof resource that another individual has acquired. An egoist does not
try to snatch resource from the acquirer to monopolize it (anotherpreliminary analysis confirmed that such a "hardcore egoist" is selected against right away), but to realize communal sharing of the resource. These egoists are supporters of the communal-sharing ideology as to resource that another individual has acquired. It may bepossible to view these egoists as "weak-minded" communal sharers
who are tempted to claim private ownership when they themselvesacquire resources.

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EVOLUTION OF SOCIAL NORMS

Table 1. Four Behavioral Strategies in the Evolutionary Game Model of the Emergence ofa Communal-Sharing Norm
Under Uncertainty
When in the nonacquirer role

Provisioning as a common property

When in the acquirer role

Claiming private ownershipownership as an acquirer and granting another acquirer's private ownership; so, a bourgeois respects the ideology of private ownership consistently whether or not
the self is an acquirer.

Computation Algorithm of the Model
Defining individual strategies this way, we need to
specify a computation algorithm about how these strategies interact in the game. The algorithm is composedof two phases: resource-acquisition phase and resource-processing phase.
Because we are concerned with a highly uncertain
situation, we assumed that the probability of two individuals simultaneously acquiring the resources was
negligible in our model. Thus, in the acquisition phase,
one individual was randomly designated as an acquirerof a resource of value V (cf. Figure 1 ).How the resourceis handled in the group following the acquisition depends on the acquirer's strategy and the nonacquirers'
strategies. This corresponds to the resource-processing
phase. If the acquirer claims private ownership (i.e.,bourgeois's and egoists) and some of the nonacquirers
demand communal sharing of the resource (i.e., egoists
and communal sharers), fights arise over the resourcethat incurs cost C to each loser. We assumed that if andonly if the acquirer won all of the fights against those
who demanded communal sharing, that acquirer could
privatize the resource; otherwise, the resource wasshared evenly among the nonacquirers who demanded
sharing plus the acquirer. For simplicity, we presumed
that all individuals' fighting abilities were identical.
In other cases, no fights about the resource arise in thegroup. If all nonacquirers in the group grant the acquirer's ownership immediately (i.e., bourgeois and saints),
the resource belongs solely to the acquirer. Also, if theacquirer is ready to provision the resource as a common
property on requests (i.e., saints and communal sharers), the resource is shared evenly among those who demand communal sharing plus the acquirer.
The focus of our initial analysis is to see if communal sharers perform better than individuals with theother three strategies in terms of net profit and, therefore, proliferate in a group. Is the communal-sharing
norm indeed realized in a stable manner in the group?
In the following section, we first report an analysis of

Demanding

Granting anothercommunal-sharing

acquirer's ownership

Communal sharer

Saint

EgoistBourgeois

most general questions concerning simple forms of thestrategies. We then examine an important issue that is
not addressed in the first analysis, viz., the issue of"free-riding" in norm enforcement (Olson, 1965). As
can be seen in Cialdini and Trost's (1998) definition,
the issue of enforcement is essential for any theory ofsocial norms (Boyd & Richerson, 1992; Coleman,1990). Thus, by a series of computer simulations, we
examine if the communal-sharing norm is indeed realized in a stable manner even when considering the further possibility of free-riding in norm enforcement.

A Formal Analysis of the Basic
Strategies
In the evolutionary game, there are two criteria toevaluate how well a given strategy (x) behaves compared to other strategies. The first criterion is called
evolutionary stability. Suppose there is a situation inwhich a group is composed only of individuals withthe focal strategy, x. Now a question arises concerning if such an all-x group is robust enough to block asmall number of individuals with another strategy (y)from intruding into the group. Does strategy x outperform strategy y in terms of average profit? If strategy
x actually outperforms strategy y, it can block y's intrusion into the group, analogous to biological "competition" for an ecological niche. If strategy x is dominant in this sense over all other strategies in thegame, then strategy x is called an Evolutionarily Stable Strategy (ESS).
The second criterion is a mirror image of the first,
called evolvability. This time, strategy x is the intruder
trying to intrude into the environment of a group composed of another strategy (y). If a small number of individuals with strategy x can outperform y, then x can"evolve" (i.e., intrude and proliferate) in the group.
This criterion is concerned with evolvability of strategy x in groups composed of another strategy (seeGintis, 2000; Maynard Smith, 1982, for a comprehensive discussion about these success criteria).
To examine whether a given strategy x indeed satisfies the two criteria, we need to compare expected
payoffs between x and each of other strategies in apairwise manner. Because there are four strategies in

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KAMEDA, TAKEZAWA, & HASTIE

the game, there are six pairs for comparison. In our
first analysis, we compared expected payoffs between
two strategies of each pair, varying parameters of themodel (group size G, resource value V fighting costC) and the intruder-defender role systematically. We
have provided the equations for these comparisons inAppendix A.

Results of the Formal Analysis:
Dominance of theCommunal-Sharing Strategy
The formal analysis (see Appendix A for details) revealed that communal-sharing strategy is a unique ESS
in the game for a wide range of parameters. When agroup is composed of communal sharers, it blocks asmall number of individuals with another strategy(saint, bourgeois, egoist) from intruding into the group.
Specifically, from the equations in Appendix A, it can beshown that an individual with any other strategy whotries to intrude into a group of communal sharers obtains
lower expected payoffs than the communal sharers, if
the following parameter condition is satisfied:

0.5G-1 (G -1) V.
(1 - 0.5G-1 )(G - 2)

(1)

To illustrate, when a group is composed of 10 people (G = 10), communal-sharing strategy is ESS if C
> 0.0022V in the game, that is, if cost C (e.g., physical injury) associated with fighting over the resource(e.g., meat) is larger than 0.22% of the resource value
V Given that fighting cost such as physical injury
tends to be substantive (sometimes even risking alife), this boundary condition is highly marginal. Notice also that when group size G gets larger, this condition becomes more marginal; the right-hand side ofequation 1 quickly approaches 0. In other words, fora very broad range of parameters, communal-sharing
strategy is evolutionarily stable.
Then, what about evolvability of communal sharing
strategy when it is rare and in the role of intruder? The
formal analysis revealed that, even when rare, communal-sharing strategy can intrude and proliferate in a groupof saints unconditionally, and a group of egoists in theidentical (broad) parameter range as specified in equation
1. Intrusion into a group of bourgeois's is possible if

C<

G

-2G-4

V.

(2)

This parameter range is less marginal than the range
specified by equation 1. On the surface, this may seemto limit evolvability of communal-sharing strategy intoa group of bourgeois's in the specific parameter range.

However, as can be easily verified in Appendix A, agroup of bourgeois's is vulnerable to the intrusion by
saints unconditionally. Given that communal sharers
can intrude and proliferate in a group of saints unconditionally (discussed earlier), they can intrude into agroup of bourgeois's in an indirect, two-step manner.
Even when direct intrusion is impossible (when equation 2 is not met), communal sharers can intrude into agroup of bourgeois's in the two-step manner with the"guide" of saints.
To summarize, the formal analysis shows that
communal-sharing strategy is the only strategy in thegame that satisfies the two criteria (evolutionary stability and evolvability) simultaneously for a broad parameter range.

Why is Communal-Sharing
Strategy Dominant?
It is easy to explain why communal-sharing strategy
is dominant in the first analysis. When resource acquisition is highly uncertain, the nonacquirers necessarily
exceed the acquirers in number by a wide margin at any
time point. In such a situation, individuals who are
loyal to the communal-sharing ideology when in the
nonacquirer role (i.e., communal sharer and egoist) can
enjoy an advantage in numbers with respect to fighting
cost, C; when refused to access the resource, these individuals can distribute the fighting cost among themselves statistically, whereas enhancing the probabilityof winning the fights collectively. In contrast, an individual who claims private ownership as an acquirer(i.e., bourgeois and egoist) is quite unlikely to win allthese challenges to privatize the resource, althoughquite likely to bear the heavy fighting cost. Accordingly, an egoist, whose expected payoff is exactly identical to that of a communal sharer when in the
nonacquirer role (by demanding communal sharing of
the resource), suffers from own behavior to privatize
the resources he or she acquires. Therefore, a communal sharer achieves the highest net profit among thefour strategies in the game.
Before closing this section, we should acknowledge
that some of the model's assumptions may be much
simpler than reality. For example, in reality, there canbe more than one acquirer at one time point; individuals may differ in their hunting or fighting abilities; conflicts between acquirer and nonacquirers may take acollective (one-to-many) form rather than a series of
one-to-one fights; and so forth. However, the key point
here is that high uncertainty associated with resource
acquisition necessarily yields a situation where thenonacquirers outnumber the acquirers by a wide margin at any time point. It is this discrepancy in number
between the haves and have-nots that provides an advantage for communal sharers to perform better than
individuals with other strategies. Thus, as far as this ba-

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EVOLUTION OF SOCIAL NORMS

sic relation holds, alternations of the simplifying assumptions to more "realistic" complex ones do not affect the main conclusions of the analysis.

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Enforcers of the Norm Versus Free
Riders: First Computer Simulation
The results of the first evolutionary game analysis
indicate that a communal-sharing norm may indeed
evolve in a sustainable manner when uncertainty in resource acquisition is high. Yet, is this analysis theoretically sufficient? Let us complicate the first analysis byconsidering the "free-rider problem" (Olson, 1965).

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Free-rider problem. A free rider refers to a person who enjoys a benefit of a social system without
bearing cost to maintain it. For instance, imagine a system of "vigilance committees" in the old Wild West. A
vigilance committee serves the public interest by securing safety equally for everyone in a town. The operation of the vigilance committee is costly in terms ofmoney, time, and physical risk. Then, what happens if
free riders, who "endorse" the vigilante actions but do
not participate, appear in such a situation? Such freeriders do not bear the costs but they enjoy the social
benefits of the vigilantes, thus, they are better off than
individuals who honestly bear the costs. The logic of
the evolutionary game implies that, under many conditions, those free riders will proliferate in the group,
eventually causing the collapse of the nonfree riders
and the norms they promote.
This general logic is applicable to the maintenanceof the communal-sharing norm as well. Recall how wedefined behavior of those who demand sharing when
they happen to be nonacquirers. We presumed that not
only communal sharers but even egoists, who are selfish when in the acquirer role, are equally cooperative in
enforcement of the communal-sharing norm, ready topunish a violator of the norm (cf. Footnote 3). However, what if free riders exist among those "demanders," who endorse the communal-sharing norm but areless willing to enforce the norm to its violators? If otherindividuals enforce the norm successfully, the free riders can enjoy the fruits of the others' costly efforts
without bearing the enforcement burden (fighting cost,C) at all. As in the example of a vigilance committee,
the logic of evolutionary game implies that such freeriders will proliferate in the group, causing the eventual collapse of the communal-sharing norm. Thus, aproblem with our initial analysis is that it neglected thefree-rider problem, by assuming that all individuals
who demand communal sharing are equally and fully
willing to enforce the norm.
To confirm this reasoning, we conducted a computer
simulation modifying the original model by incorporating individual differences in willingness to bear the costfor norm-enforcement. In this simulation, we started out

Figure 2. Collapse of the communal-sharing norm when there
exist nonenforcing free riders (first computer simulation).

with an initial state in which a group was composed onlyof communal sharers. However, different from the first
analysis, we now permit individual differences in thewillingness to enforce the norm. When in the
nonacquirer role, some communal sharers are less willing to punish the violators of the norm than other communal sharers. We allowed random "mutations" in behavioral strategies to emerge with a small probability inthis all-communal-sharer-group.4 Our focal question
here is whether communal-sharing strategy is still an
ESS: Is the communal-sharing norm sustainable when
free riders exist? We have provided the algorithm used inthis modified analysis in Appendix B.
Results. Figure 2 displays representative resultsof the first simulation. This figure shows proportions of
the four behavioral strategies over time ("generations")
in a 10-person group, when the ratio of fighting cost C
to resource value V CIV, was 0.25. Notice that the
aforementioned formal analysis assuming no free riders predicts that communal-sharing strategy is an ESS
for these parameter sets (cf. equation 1).
However, with the passage of time, the initial communal-sharing system gradually collapsed. Instead, thegroup was eventually dominated by egoist-free-rider
hybrids, who claimed private ownership when in the
41n an evolutionary game analysis using computer simulations, abehavioral strategy is often represented by a "gene" or a "set ofgenes." For example, each strategy in Table 1 can be represented by
two genes, specifying how to behave as an acquirer and as anonacquirer, respectively. Analogous to actual inheritance, a random"mutation" may occur to these genes with a small probability in each"generation." The notion of mutation is used to check if a given strategy is an ESS. A random mutation to genes provides an opportunity
for a small number of individuals with other strategies to emerge in
the group. It then becomes possible to check whether the focal strategy can block the "evolution" of those mutants in the group in termsof net profit. See Axelrod (1984, 1986) and Lomborg (1996) for examples and a more comprehensive discussion of this technique.

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KAMEDA, TAKEZAWA, & HASTIE

acquirer role, and who demanded communal sharing of
the resource but were not willing to enforce ("fightfor") their demand when in the nonacquirer role. In
other words, when some of the communal sharers areless enthusiastic than others about norm enforcement,
the communal-sharing system cannot be sustained.
Is the Free-Rider Problem Solvable?

The free-rider problem arises from the fundamental
fact that a social norm is essentially a "public good"(Hardin, 1968). At least some individuals can benefit
from a public good without incurring a cost for its maintenance, as in the example of the vigilance committee.
Thus, the free-rider problem in norm enforcement is one
ofthe central issues for any theory ofnorm development(Boyd & Richerson, 1992; Coleman, 1990).
It is often argued that social sanctioning is a solution
to the free-rider problem. Let us go back to our illustrative case: Some norm enforcers may sanction the freeriders by blocking their access to communally-sharedresources. Although such sanctions may look like a solution, the identical logical problem that applied to thebasic four-strategy case applies recursively: Amongthe norm enforcers, who incur the additional sanctioning costs for excluding the nonenforcing-free riders
from social sharing? Exclusion of those free riders
from communal sharing may also be costly (e.g., fights
with the free riders). However, at the same time, successful exclusion of the free riders from communal
sharing is beneficial to all norm enforcers. A new, but
structurally identical free-rider problem, "2nd-orderfree-rider problem" (Axelrod, 1986; Yamagishi, 1986)
arises here.
To illustrate, suppose that there are two types of norm
enforcers. One type is intolerant; in addition to their
willingness to punish a direct violator of the communal-sharing norm (e.g., egoist, bourgeois), these individuals are ready to bear additional costs for excluding
the nonenforcing free riders from social sharing. The
othertype is tolerant; this type is willing to punish the direct violator of the norm, but does not punish the free riders. (See Figure 3 for a summary for the distinctions
within the class ofindividuals who endorse the communal-sharing ideology when in the nonacquirer role.)
Notice that the tolerant enforcers can be seen as"2nd-order free riders" who benefit from costly efforts of
the intolerant enforcers trying to exclude the nonenforcing"lSt-order free riders" from social sharing. In other words,
the tolerant enforcers should always be better off than the
intolerant enforcers in terms of net profits.
Now, what happens if we apply this logic to thesustainability of the communal-sharing system? Suppose that again we start with an all-communal-sharer-group while allowing for mutants to emerge
with a small probability. The logic of the evolutionary
game implies that if the intolerant communal sharers

Figure 3. An overall summary for the distinctions within theclass of "demanders," who endorse the communal-sharing ideology when in the nonacquirer role.

disappear from a group because of their fitness disadvantage, then the tolerant communal sharers will be
wiped out by the nonenforcers, who are eventuallywiped out by the egoists. In other words, a domino-likecollapse of the communal-sharing system is expected
logically, even if social sanctioning against the Ist-order free riders is originally present.

Possibility of a Sustainable
Communal-Sharing Norm
Second- (and higher-) order free riding is an essential problem for the maintenance of any general social
norm. Although there have been several theoretical attempts to solve this puzzle (e.g., Axelrod, 1986; Boyd
& Richerson, 1992; Yamagishi & Takahashi, 1994), no
general solution has been reached. In this article, weshow that the problem of 2nd-order free riding is solvable at least for social sharing in an uncertain environment. Through two additional computer simulations,
we demonstrate that the communal-sharing norm is"evolvable" and sustainable, when acquisition of resource is uncertain as for the Ache and otherhunter-gatherer societies.
Throughout the following simulations, we introduce the aforementioned distinction within the class ofnorm enforcers between tolerant and intolerant individuals (see Figure 3). Our second simulation assumesa "behavioral rationality" about how the nonenforcers(1st-order free riders) react to the intolerant enforcers.
As explained later, we think that behavioral rationalityof 1St-order free riders is a key to solve the potential
problem of 2nd-order free riding in communal sharing.
The third simulation relaxes this rationality assumption and tests if the communal-sharing norm is still"evolvable" and sustainable.

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EVOLUTION OF SOCIAL NORMS"Behavioral Rationality" of
1st-Order Free Riders: Second
Computer Simulation
Recall the definition of nonenforcing free riders;these are individuals who just "endorse" the communal-sharing norm but do not punish its violators whenin the nonacquirer role (see Figure 3). Now, why would
these free riders avoid fighting with direct violators of
the norm, such as egoists and the bourgeois's? At least
two "psychological motives" may be relevant: (a) fearof a fight, and (b) greed, exploiting norm-enforcers'
costly efforts (cf. Van de Kragt, Orbell, & Dawes,
1983; Yamagishi & Sato, 1986).
Given these two motives, let us speculate how thesefree riders may react, when refused access to the resource by the intolerant enforcers: how likely will thefree riders fight back against the intolerant enforcers?
From the first motive ("fear"), fights with the intolerant
enforcers should be feared even more than fights withthe direct violators of the norm, because of the relativerisk involved. If such free riders enforced the communal-sharing norm against its direct violators at the outset, they could distribute fighting costs broadly withother individuals who demanded sharing (see the top
box in Figure 3). However, when they free-ride and arechallenged later by the intolerant enforcers, those freeriders must distribute the full fighting costs just among
themselves. Because the free riders are a subset of "demanders" by definition (cf. Figure 3), the actual risk
(expected fighting cost) involved is much higher in thelatter case, whereas the stake in the fight (share of resource) is identical. Thus, fights with the intolerant enforcers should be more threatening to free riders thanfights with the direct violators of the norm. At the sametime, from the second motive ("greed"), free riders
should be inclined to exploit other free riders' efforts tofight with the intolerant enforcers, just as they selfishly
avoid the fights against the direct violators of the norm.
The aforementioned reasoning suggests that, if thosenonenforcing-free riders are "rational" in terms of
cost-benefit assessment, then their propensity to fightwith direct violators of the sharing norm (pi) should begreater than (or equal to) their propensity to fight backagainst the intolerant enforcers, who block their accessto the resource (P2). We label this relation pi 2 p2, the"behavioral rationality of Ist-order free riders."
The behavioral rationality of free riders has an important implication for the solvability of the 2nd-orderfree-rider problem in communal sharing. Again, recall
the definition of Ist-order free riders. These are individuals who just endorse the communal-sharing norm but
are not willing to enforce the norm themselves. In otherwords, their propensity to fight with direct violators of
the sharing norm (pI) is small by definition (cf. Appendix B). Now, given the behavioral rationality (pi > pn),
their propensity to fight back against the intolerant en-forcers (P2) when blocked to access shared resources
should be even smaller, almost negligible.
What does this mean to the sustainability of thecommunal-sharing system? The behavioral rationality
assumption implies that those free riders tend to retreat(i.e., P2 is negligible) in the face of the intolerant enforcers' refusals. Then, the intolerant enforcers can exclude the free riders from social sharing efficiently, by
only threatening punishment ("bluffing") and not actually engaging in the costly fight. This allows the intolerant enforcers to survive, because there is no effective
difference in net profits between the intolerant and tolerant enforcers. The 2nd-order free-rider problem isthus avoidable, and nonenforcing 1St-order free riders
are effectively eliminated. With the assumption of freerider's behavioral rationality (pi . P2), the communal-sharing norm should be sustainable.
To test this reasoning, we conducted a second computer simulation with the assumption of free rider's behavioral rationality, while keeping other aspects of themodel identical to the first simulation (see Appendix C
for the algorithm). Figure 4 displays representative results of this simulation.
Figure 4(a) displays results when we started out
with a group initially composed of all intolerant,
norm-enforcing communal sharers (cf. Figure 3). The
simulation parameters used were exactly identical tothose used in the simulation reported in Figure 2,
where group size was 10 and the ratio of fighting cost C
to resource value V C/V was 0.25. Here again, random"mutations" in behavioral strategies could emerge witha small probability.
The difference from Figure 2 is clear. The initialcommunal-sharing system that collapsed over time in
Figure 2 was sustainable against egoist mutants; withthe assumption of free rider's behavioral rationality,
the aforementioned domino-like collapse of the communal-sharing system did not occur. Figure 4(b) examined "evolvability" of the communal-sharing norm.
Here, we started out with the "worst" condition wherea group was initially composed only of tolerant,
least-enforcing (pi = 0.1) egoist hybrids (cf. Appendixes B & C)-agents with the "most vicious" behavioral properties. The communal-sharing norm was indeed evolvable from zero. Figure 4(c) displays
complementary results to Figure 4(b). We again started
out with the identical "worst" condition, but did not allow for intolerant enforcers to emerge by random mutation. The communal-sharing norm did not evolve
when intolerant enforcers could not emerge.
To test the robustness of the result patterns as observed in Figures 4(a) and 4(b), we further conducted asensitivity analysis varying the parameters of the model(group size G, and the ratio of fighting cost C to resource
value V C/V) systematically. The sensitivity analysis indicated that the communal-sharing system is indeed
quite robust for a wide range of parameters. For exam11

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KAMEDA, TAKEZAWA, & HASTIE(/V (Fighting-Cost/Resource-Value)=0.25

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Figure 4. These are the results of the second computer simulation. All nonenforcing free riders exhibit "behavioral rationality" (PI >P2;
see text for explanation): (a) cases starting with a group were initially composed only of intolerant communal sharers; (b) cases startingwith the "worst case" group were initially composed of tolerant, least-enforcing egoists; (c) cases were identical to (b) except that intolerant enforcers can never emerge by mutation; and (d) a sensitivity analysis was conducted examining the robustness of the communal-sharing system under various parameter conditions.

ple, as shown in Figure 4(d), the communal-sharing system is sustainable and is also evolvable in a 10-persongroup, even when C/V = 0.003 (fighting cost C is only

0.3% of resource value V-an unrealistically "severe"parameter condition as discussed earlier; cf. equation 1).

These results confirm our reasoning. Despite thepotential problem of 2nd-order free riding, the communal-sharing norm is "evolvable" and sustainable inan uncertain environment. The presence of intolerance toward nonenforcing free riders in the groupprevents the domino-like collapse of the communal-sharing norm.

Emergence of Free Rider's "Behavioral
Rationality:" Third Computer
Simulation
As shown in the second simulation test, if the st-order free riders exhibit behavioral rationality (pi pP),

intolerant enforcers can enter and thrive in the group,
enabling the communal-sharing norm to evolve anddominate (Figure 4b). We believe that this is abehaviorally plausible assumption that has a rational
ground, but it may still be possible to criticize this assumption as arbitrary. The intolerant enforcers can enjoy the benefit of "bluffing" (i.e., only threatening punishment and not actually engaging in the costly fight),
if and only if the free riders believe the threat and retreat. Then, what if those free riders do not believe thethreat? What if they have no built-in "rational" tendency to avoid fights against the intolerant enforcers?
To address these points, we conducted a third simulation without the "rationality" assumption. We
dropped the restriction on the relation between the two
fighting propensities (p I. P2) of the Ist-order free riders. The two propensities were initially set independently; thus, there could be "irrational" cases (p1 < p2),
in which free riders were more aggressive against intolerant enforcers (e.g., "calling their bluffs") than

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EVOLUTION OF SOCIAL NORMS

against direct violators of the norm-those free riders
did not take the intolerant enforcers' threat seriously.
Figure 5 displays representative results of the third
simulation. The simulation parameters were exactly
identical to the previous cases in Figures 2 and 4. Figures
5(a), 5(b), and 5(c) correspond to Figures 4(a), 4(b), and4(d), respectively. The results of the second simulation
were replicated, indicating that even ifthe rationality assumption was not initially built in, the communal-sharing system was still evolvable and sustainable.
Does this mean that the assumption of "behavioral
rationality of Ist-order free riders" is unnecessary tosolve the potential problem of 2nd-order free riding incommunal sharing? Figure 5(d) displays the relations
between the two fighting propensities (pi and P2) overtime in the simulation reported in Figure 5(a). Because
we set the two propensities independently, the proportion of 1St-order free riders who satisfied the behavioral
rationality (p1I P2) was almost equal to the proportionof those who behaved "irrationally" (pi < p2), at theoutset. However, the figure shows that such a balance
rapidly disappeared over time and that almost all individuals in the group eventually behaved consistent
with the rationality.
As repeatedly emphasized, nonacquirers outnumber
acquirers by a wide margin at any time point in an uncertain environment. Thus, if a nonacquirerjoins norm-enforcement activities against a small number of acquirers
trying to privatize the resource, the nonacquirer could
enjoy the advantage in numbers and reduce the expectedfighting cost substantially. However, if the individual
decides to free ride and is challenged later by intolerant
enforcers, such an advantage in numbers is lost. The individual must now bear a larger expected fighting cost,whereas the stake in the fight (share of resource) is identical. In other words, if having to fight, fighting withnorm violators at the outset is more economical than
postponing the fight until challenged by intolerant enforcers. Thus, in the uncertain environment, the "irrational" free riders, who react more aggressively against in-

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Figure 5. These are the results of the third computer simulation without the "behavioral rationality" (P1 .p2) assumption for free riders:(a) cases starting with a group initially composed only of intolerant communal sharers; (b) cases starting with the "worst case" group initially composed of tolerant, least-enforcing egoists; (c) a sensitivity analysis examining the robustness of the communal-sharing system
under various parameter conditions; and (d) relations between the two fighting propensities (p1, norm-enforcement probability; P2,
probability of fighting back against intolerant enforcers) over time.

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KAMEDA, TAKEZAWA, & HASTIE

tolerant enforcers than against direct violators of thenorm (p1 <p2), are less fit compared to the "rational" freeriders who tend to believe the intolerant enforcers' threat(PI .pP2). Accordingly, the behavioral rationality of freeriders emerges naturally over time. This feature allows
the intolerant, norm-enforcing communal sharers tofunction effectively without actually bearing the costs
for preventing free riders from social sharing. Infinite
regress to the 2nd- and higher-order free riding in norm
enforcement is avoidable under the uncertain environment, due to the sustainability of intolerant communal
sharers in a society.

Behavioral Experiments on
Psychology of Social Sharing:
Demonstrations of "Windfall as aCommon Property Effect"
The theoretical analysis shows that the communal-sharing norm is evolvable and sustainable in a primordial environment, where uncertainty in resource
acquisition poses a critical adaptive problem for survival. Interestingly, although we live in industrializedsocieties where such uncertainty is reduced by various
social systems (e.g., production technologies), our own
contemporary attitudes and behaviors toward a resource may also be affected by uncertainty involved in
its acquisition. For instance, we seem to have a "psychology of sharing" to use windfall money, more often
than money acquired by hard labor, for social purposessuch as treating friends or donations to charities. Although the identical fungible resource (money) is under consideration in both cases, different psychological processes seem to be triggered, depending on how
the resource is acquired.
Of course, this phenomenon may simply reflect amodem ideology of labor theory of value ("money
earned without making effort has little value"). However, our evolutionary game analysis suggests anotherexplanation. The key factor for triggering such a psychology of sharing may be the uncertainty associatedwith the acquisition of the money, rather than the absence of effort. As Cosmides and Tooby (1992) argued,
it may be the case that "information about variance inforaging success should activate different modes of operation of these (cognitive) algorithms, with high variance due to chance triggering a psychology of sharing"(p. 213, parentheses added).
To test this possibility, we have recently conducted
vignette experiments in which the uncertainty factor
was manipulated independently of the effort factor(Kameda, Takezawa, Tindale, & Smith, 2002; Studies
1-3). Participants were provided with a series of imaginary scenarios in which they (or a friend) obtainedsome money, either (a) contingent on investing substantial effort; (b) unexpectedly (i.e., high out-

come-variance due to low contingency between effort
and outcome) but after investing substantial effort; or(c) unexpectedly with almost no effort. The following
is an example scenario used.
Certain and high-effort condition
An acquaintance requested you to fill out application forms for a prize giveaway. It was a tedious job to fill out the form. You completed 50
forms in total. Your acquaintance paid you $100
for this service.

Uncertain and high-effort condition
You decided to apply for a prize giveaway.
Although it was a tedious job to fill out the application forms, you completed 50 of them to increase your chances to win. Later, you found thatyou won a prize of $100.
Uncertain and low-effort condition
You decided to apply for a prize giveaway andsubmitted one application form. Later, youfound that you won a prize of $100.

Participants were then asked to rate their willingness to share the money with a friend (or the extent towhich they would demand some share from a friend)on 7-point scales. Notice that the modem notion ofproperty rights makes no distinction about the legitimacy of the entitled ownership among the three conditions. However, if uncertainty triggers psychology ofsharing, there should be a difference among the conditions, especially between the certain and high-effort
and the uncertain and high-effort conditions although
the amount of effort invested was identical.
Figure 6 displays results of this experiment using
Japanese and American undergraduate students. Although the Japanese sample generally showed alarger sharing-tendency than the American sample,
the predicted pattern was evident. Participants' mean
willingness to share was higher in the uncertain andhigh-effort condition than in the certain and high-effort condition, F(1, 174) = 22.76, p < 001, in theJapanese sample; F(1, 314) = 4.10, p < .05, in theAmerican sample by planned contrasts. This comparison was also significant for participants' "willingness to advocate sharing" tendencies, F (1, 174)
= 16.80, p < .00 1, in the Japanese sample; F (1, 322)
= 5.19, p < .05, in the American sample. Further,even when statistically controlling for the participants' personal ideologies about labor value or distributive justice (equality or equity), these effects
were still significant.
We also conducted a laboratory experiment to manipulate the experienced uncertainty in actual

money-acquisition directly (Kameda et al., 2002;
Study 4). In this study, 65 Japanese undergraduate stu-

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EVOLUTION OF SOCIAL NORMS
7

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Figure 6. A cross-national experiment on (a) willingness to share and (b) willingness to advocate sharing, as a function of uncertainty ofacquisition and invested labor in resource acquisition.

dents worked on 30 arithmetic problems individually.
Before they actually started working, the reward forsolving one problem correctly was decided either in a
deterministic manner ("As a unit reward per problem,
we have five conditions ranging from 5 yen to 25 yen.
You have been assigned to the 25-yen per problem condition."), or in a stochastic manner by "using a roulette
wheel of fortune" with the same unit-reward range. In
both conditions, participants received 750 yen as a final reward. At the end of the experiment, they were solicited to donate some money to help participants in another, unrelated experiment. Figure 7 displays the
distributions of individual donations in the two conditions. Participants in the uncertain, stochastic condition showed more sharing than those in the certain, deterministic reward condition. The difference wassignificant, z = 1.81, p < .05 (one-tailed) by
Mann-Whitney U test.
These results, of course, do not imply that otherfactors such as invested efforts, modem distributive
ideologies, different cultural-societal values, and soforth, are irrelevant to actual social sharing. These results also do not mean that high-variance informationis the strongest predictor of sharing breadth and depth(cf. Gurven, 2002). What was demonstrated is that,even controlling for these factors, our minds are stillsensitive to uncertainty information associated withresource acquisition; high-variance information in"foraging success" seems to be an essential ingredient of a computational algorithm that underlies social
sharing. Whether such a psychological mechanism
has been acquired either evolutionarily (cf. Cosmides& Tooby, 1992) or for historical-cultural reasons remains to be seen, but the game model we have developed suggests that individuals with such a cognitivealgorithm may be more fit than others in an adaptive
environment.

Social Norm as a Micro-Macro

Linkage Between Minds and Society
In this article, we showed how a theory about norm
emergence may be developed, with the communal-sharing norm as a guiding illustration. Our
metatheoretical perspective throughout this article has
been adaptive or functional (cf. Cialdini & Trost,1998). As expressed in our formal analysis, we view asocial norm as emerging from the autonomous interaction of individuals who are motivated to be adaptive,
and profit-seeking in their social and natural environment. Such an adaptive perspective, especially the perspective focusing on the individual-level adaptation,highlights the key theoretical issues in norm development, including subtle problems of "1st- and 2nd-orderfree-riding," norm-enforcement, and sustainability of
intolerance toward nonenforcers at various levels-es16

14

12
In
L-

0

ox
0
cu
0

8)

10
8

8).)_
C

a)

6

I)

LL

0

- 100

-

200

-

300

-

400

-

500

-

600

-

700

-

750

The am ount of money donated (in yen)

Figure 7. Changes in the donation pattern as a function of uncertainty involved in the acquisition of money.

15
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KAMEDA, TAKEZAWA, & HASTIE

sential notions for any theory of injunctive or punitive
norms (Coleman, 1990). We believe that the conceptual framework as illustrated in this article is particularly useful to consider these issues in a systematic andrigorous way.
Of course, there are other kinds of social norms, suchas fashions or conventional norms, the contents of which
are largely arbitrary and determined by the social factthat they are shared widely within the society (Cialdini& Trost, 1998; Nowak, Szamrej, & Latane, 1990). Although the scope of this article did not address theseconventional norms directly, the adaptive framework
may be useful to tackle those conventional norms as
well. For example, our basic susceptibility to "social
sharedness" (Kameda, Tindale, & Davis, in press) orother types of social influence and social comparison(Festinger, 1954) may have some adaptive (perhaps
even evolutionary) basis. Exploring the psychologicalmechanisms underlying the emergence and persistenceof conventional norms from the adaptive perspective is apromising research direction. Although some preliminary work exists to explore these issues (cf. Henrich &Boyd, 1998; Kameda & Hastie, 2002; Kameda &Nakanishi, 2002), many important theoretical and empirical questions are still open, awaiting future systematic investigations.
Given the fundamental fact that we are group-living
species, the thesis that our minds are socially adaptive
seems a reasonable metatheoretical assumption(Barkow et al., 1992; Campbell, 1975). Social norms
that link microlevel cognitions of individuals to a
macrolevel social condition, in a mutually-constrained, dynamic manner (as we have demonstratedusing the evolutionary game analysis), capture an essential characteristic of such group life. In this sense,
we believe that the notion of social norm can serve as
one of the central and most useful constructs in psychology, while linking psychology to other social sciences in an integrated way, as envisioned by early social theorists.

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Appendix A
Expected Payoff to Each Strategy
This appendix provides the equations for comparing
expected payoffs between two behavioral strategies.
Because we have four strategies in the game (Table 1),
there are six pairs for comparison. In the following equations, we denote expected payoff for an individual withstrategy x as U(x). U(x) is the sum of three components,
namely, (expected payoff if the self is an acquirer) + (expected payoff if another individual with the same strategy, x, is an acquirer) + (expected payoff if another individual with the other strategy, y, is an acquirer).
As stated in the text, conflicts arise if the acquirer of a
resource claims private ownership while thenonacquirers demand communal sharing. We assumed
that, in such a case, the acquirer could privatize the resource if and only if he or she wins all of the one-to-one
fights against those who demanded communal sharing.
For simplicity, we also assumed that all individuals'
fighting abilities were identical. Thus, if there are r individuals who demand communal sharing in the group,
the probability of the acquirer's successfully privatizing
the resource is given by 0.5r; otherwise, the resource isshared equally among r + 1 individuals, viz., rnonacquirers who demand sharing plus the acquirer.
The following equations are based on these assumptions. We denote group size as G. resource value as V

fighting cost for a loser as C, number of communal shar17

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KAMEDA, TAKEZAWA, & HASTIE

ers in the group as NCs, number of bourgeois's as NBr,
number ofegoists as NEg, and number ofsaints as NSt.

Communal sharers versus saints
V / NCs + (NCs-1) V / NCsO+ NSt * V /(NCs + 1)
G
G
G

U(c )

Communal-sharers versus bourgeois's
To illustrate, suppose that the group is composed ofcommunal sharers and bourgeois's. Expected payoff to
a communal sharer, U(Cs), is calculated as follows.
When the self happens to be an acquirer, he or she
shares the resource with other communal sharers
evenly (notice that bourgeois's who value private ownership do not join the sharing)-thus the expected payoff in this case is V/NO. Likewise, when another

Bourgeois's versus egoists

communal sharer acquired the resource, the expected
payoff to the communal sharer is given by(NCs -1)(V / NCs) Finally, when a bourgeois acquired

U(Eg)-

U(St) - VI(NCs+1) ,where NCs + NSt = G.
G

0.5NEg V + (1-0.5NEg )(-C +U(Br) =

ers are defeated by the bourgeois's, V(INOs +I1), while

potentially bearing a cost accruing from personal loss
1 NCs
in the series of one-to-one fights, C
A05 AgNg t
gregating these elements, we get
ViNCs (NCs-1)(V/NCs)

U(C)

G

G

NBr V (10.5NCs)1~~~NCS±1

G

NCS

NOs

O.5NEg lV+(1-0.5NEg 1)(-C+ N
U(Eg)G

0.5NCsV + (1- 0.5NCs)(-C + N

v

1)

NCs+I

U(Br) =

G

Communal sharers versus egoists

E-1
U(Cs) ±U+
(NCs-1)(V/G)+NEg.
VIG.1V(10(1-0.5G-1)_C
c. I Z-O.5'1.
G
G
G
G
G-1

r

0.5G-1 V + (1 0.5G- )(-C+) NEg1 V(1OG 1)
U(Eg)

=

G

CaG

G

1Eor }+NCs(V / G)

where NCs + Neg = G.

G

)

1)
~~~NEg +NEg-G -1 V (1 -.5NEg
NEg

INEg -1r0.5r}± NBr V-(1 -0.5NEg)G
NEg + 1

1

NEg

NEg

,

where NBr + NEg = G.

Bourgeois's versus saints
V
U(Br) = U(St) =-.
Egoists versus saints
0.5NEg-I V + (1 -05NEg l)(-C + N

U(Eg)

)
NEg +NEg- -1 V .(1 0.5NEg- 1)=

C.

where NCs + NB r= G.
A bourgeois can privatize the resource that he or she
acquired, if he or she wins all of the fights against communal sharers. Otherwise, he or she is forced to share theresource evenly with the communal sharers while bearing a cost accruing from loss in the fight. Thus, we get

)

G

G

G
the resource, the communal sharer receives an evenshare of the resource unless all of the communal shar-

NEg + 1

U(St) -GG
1 N~g-1
NSt -V/(NEg +1)
G0.51}+
G
NEg 1 ENEg

(Ng 1),where Neg + NSt = G.

Appendix B
Model Incorporating Free Riders(First Computer Simulation)
This model is identical to the basic model, exceptthat an individual's norm-enforcement when in the
nonacquirer role (confronting the violator of the norm)
is determined according to his or her "norm-enforcement probability," pi. By definition, pi is fixed at 0 for
those who always grant the acquirer's private ownership (i.e., bourgeois's and saints), but it can range from
0.1 to 1.0 for those who demand communal sharing
when in the nonacquirer role (i.e., communal sharers
and egoists). The communalized resource, however, isshared not only among the norm-enforcers (i.e., individuals who have participated in the enforcement activity), but also with those who just "endorse" thenorm, but without enforcement. In other words, thecommunalized resource is equally accessible by all individuals who endorse the communal-sharing ideology, more or less (with pi > 0, but not necessarily p1 =18
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EVOLUTION OF SOCIAL NORMS

1). Therefore, individuals with small pis are more
likely to behave as free riders than individuals withlarge pIs.
We conducted an evolutionary computer simulation
to evaluate the performance of each strategy. To represent individual strategies, two "genes" were used in thesimulation: a gene controlling how to behave when in
the acquirer role, and a gene controlling variations inprobability of norm enforcement when in the
nonacquirer role, Pi. The first gene had two alleles(claiming private ownership of a resource that one acquired or provisioning it as a communal property), andthe second gene had 11 alleles (norm-enforcement
probabilities when in the nonacquirer role, ranging
from 0 to 1.0 by a step of 0.1). Combinations of these
two genes allowed us to implement four basic behavioral strategies of Table 1 in the simulation, along withindividual variations in the norm-enforcement tendency (pI = 0 for bourgeois's and saints, whereas pI > °for egoists and communal sharers). At the start of simulation, we assigned these genes to G individuals. In
each round, one of the G individuals was randomly
chosen as an acquirer of the resource. As explained in
the text, social interaction over the resource continued
until the fate of the resource was settled (i.e., privatizedor communalized). This process constituted one round.
With the same set of G individuals, 40,000 rounds wereplayed. After 40,000 rounds, G individuals wereranked from high to low in terms of net payoff, andtheir genes were reproduced probabilistically according to the rank ("selection"). The higher the rank of anindividual, the more likely were the individual's genes
to be reproduced. We used a standard gene-reproduction procedure in the genetic algorithm literature (seeGrefenstette, 2000, p. 23, for the algorithm). Finally, a"mutation" occurred to each individual's genes withthe probability of 0.025. That is, an individual's"communalizing" allele at the first locus may mutate toa "privatizing" allele with the probability of 0.025, andvice versa. Independently from the mutation to the firstgene, mutation to the second gene could also occur
with the probability of 0.025. When mutation occurs,
an individual's original value for pi changes to one ofthe other 10 values randomly. The aforementioned process constituted one "generation." We traced changes
in proportions of the four behavioral strategies in thegroup over 5,000 generations.
For the analysis reported in Figure 2, we started out
with a 10-person group (G = 10) initially composed
only of communal sharers. Although these individuals
provide resource when in the acquirer role (all having
the "communalizing" allele at the first locus), theirnorm enforcement probabilities when in the
nonacquirer role, pIs, were generated randomly from a
uniform distribution ranging from 0.1 to 1.0. This procedure allowed individual differences about their willingness to enforce the communal-sharing norm whenin the nonacquirer role. Starting from this initial state,
we conducted five simulation-runs in total by the
aforementioned procedure. Figure 2 displays averagedata of these five runs.

Appendix C
Model Incorporating theTolerant-Intolerant Distinction(Second and Third Computer
Simulations)
Model C is an extension of Model B. We added two
new genes, one regulating the tolerant-intolerant distinction against nonenforcing free riders, and the otherregulating variations in the free riders' propensities tofight back against the intolerant enforcers. The former
gene had two alleles (tolerant or intolerant) and was activated only when an individual behaved as a norm enforcer (cf. Figure 3). Besides confronting a direct violator of the communal-sharing norm (i.e., egoists andbourgeois's), the intolerant enforcers were committed
to exclude free riders from sharing, although theymight have to bear an additional fighting cost, C. It was
assumed that, when such intolerant enforcers existed ina group, the free riders could access the resource only
if they won all the fights against the intolerant enforcers; otherwise, the communalized resource was shared
evenly just among norm enforcers including tolerant aswell as intolerant enforcers. The latter gene had 11 alleles (probability of fighting back, P2, ranging from 0
to 1.0 by a step of 0.1), and was activated only when an
individual behaved as a free rider and was refused toaccess the resource by the intolerant enforcers. Selections of and mutations to these genes occurred in thesame manner as in Model B. Other features of the simulation algorithm were also identical to Model B.
In the second simulation, we started out with a restriction so that pi > P2 ("behavioral rationality" of
1st-order free riders). For each individual, we generated
a norm-enforcement probability, pi, randomly from a
uniform distribution ranging from 0.1 to 1.0, and thenassigned a fighting-back probability, p2, randomly sothat pI > P2. The third simulation removed this restriction and generated the two fighting propensities, pI and
P2, independently.

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