Evolution and Human Behavior 23 (2002) 373 – 393

Cost–benefit analysis of social/cultural learning in a
nonstationary uncertain environment
An evolutionary simulation and an experiment withhuman subjects
Tatsuya Kameda*, Daisuke Nakanishi
Department of Behavioral Science, Hokkaido University, Bungakubu, N10 W7, Kita-ku, Sapporo 060-0810, Japan
Received 14 January 2002; accepted 10 April 2002

Abstract
Social/cultural learning is an effective way to reduce uncertainty about the environment, helping
individuals adopt an adaptive behavior cheaply. Although this is evident for learning about temporally
stable targets, such as acquisition of a skill in avoiding toxic foods, the utility of social/culturallearning in a temporally unstable environment is less clear, since knowledge acquired by social
learning may be outdated. This paper addresses the adaptive value of social/cultural learning in a
nonstationary environment both theoretically and empirically. We first conducted an evolutionary
computer simulation that extended Henrich and Boyd’s [Evol. Hum. Behav. 19 (1998) 215.] model ofcultural transmission, with the following results. When individual learning about the nonstationary
environment is costly, a mixed equilibrium emerges in the population, where members who engage incostly individual learning and members who skip the information search and free-ride on othermembers’ search efforts coexist at a stable ratio. Such a ‘‘producer –scrounger’’ structure qualifies
effectiveness of social/cultural learning severely, especially ‘‘conformity bias’’ when using social
information. We then tested these propositions by an experiment implementing a nonstationary
uncertain environment in a laboratory. The results supported our thesis. Implications of these findings
and some future directions are discussed. D 2002 Elsevier Science Inc. All rights reserved.
Keywords: Social learning; Cultural transmission; Conformity; Nonstationary environment; Producer – scrounger
equilibrium* Corresponding author.
E-mail address: tkameda@let.hokudai.ac.jp (T. Kameda).
1090-5138/02/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved.
PII: S 1 0 9 0 - 5 1 3 8 ( 0 2 ) 0 0 1 0 1 - 0

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1. Introduction
Uncertainty reduction is a central adaptive problem for many species. Some group-living
species have evolved effective social mechanisms for reducing uncertainties in their environments. A prime example is a system of reciprocal exchange (e.g., Axelrod, 1984; Trivers,
1971; Wilkinson, 1984) and sharing of resources such as food (e.g., Flack & de Waal, 2000;
Kameda, Takezawa, & Hastie, in press; Kameda, Takezawa, Tindale, & Smith, 2002; Kaplan& Hill, 1985). These social mechanisms secure a stable supply of resources by collectively
buffering uncertainties associated with their acquisition. In this paper, we extend theuncertainty-reduction view beyond the domain of exchange and sharing, and we reconsider
adaptive functions of social learning from this perspective. As argued by various theorists(Boyd & Richerson, 1985, 1996; Cavalli-Sforza & Feldman, 1981; Durham, 1991; Heyes &Galef, 1996; Tomassello, Kruger, & Ratner, 1993), social or cultural learning is anotherimportant way, besides genetic transmission, to transmit information across generations. We
first explore uncertainty reduction via social learning with an evolutionary computer
simulation that extends Henrich and Boyd’s (1998) recent model of cultural transmission.
We then test theoretical propositions derived from the simulation, by an experiment withhuman subjects. Finally, we discuss how evolutionary game modeling of cultural learning,
coupled with experimentation, provides useful insights into various social psychologicalphenomena in modern societies, as well as into the evolutionary forces that have shaped oursocial minds.
1.1. Uncertainty reduction by social/cultural learning
To prepare the ground for our theoretical development, let us start with a simple example.
Suppose that you have found a clump of mushrooms while walking in a forest. Although themushrooms look tasty, you are uncertain if they are indeed edible. Individual learning by trial
and error may be fatal in this case, so a cheap and reliable way to cope with this uncertainty is
to ask an expert’s or elder’s opinion, or simply observe their behaviors. Indeed, as illustrated
by this example, the previous literature suggests that acquisition of food preferences isheavily influenced by cultural transmission (Katz & Schall, 1979; Rozin, 1989; see also Galef& Whiskin, 2001 for social acquisition of food preferences in rats). Boyd and Richerson(1985) showed formally that social or cultural learning is effective in these cases, and inparticular, that conformity bias, a propensity to preferentially adopt the cultural traits that are
most frequent in the population, is adaptive in these uncertain situations. (See Festinger,
1950, 1954, for a related argument.)
Although the ‘‘mushroom problem’’ illustrates the adaptive value of social learning, it
may illuminate its limitations as well. Notice that culturally transmitted knowledge about themushroom holds true across generations: If someone in your tribe died from the mushroom
centuries ago, the incident still conveys valuable information to the current generation.
Social or cultural learning about such a temporally stable target should therefore function asa highly effective mechanism to reduce uncertainty, but a far more challenging case isprovided by a temporally unstable environment where a behavior that was adaptive inT. Kameda, D. Nakanishi / Evolution and Human Behavior 23 (2002) 373–393

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previous generations may no longer be so. This sort of environmental instability was
actually quite common in our evolutionary history; for example, recent studies on ice coresand ocean sediments suggest that the Pleistocene EEA was an environment with frequentclimate fluctuations on submillennial time scales (cf. Potts, 1996; Richerson & Boyd, 2000).
Is social or cultural learning still adaptive in such a temporally unstable environment?
Specifically, is conformity bias in social learning, as observed in acquisition of foodpreferences, functional?

1.2. Effectiveness of ‘‘conformity bias’’ in social/cultural learning in a nonstationary
environment: Henrich and Boyd’s (1998) simulation modelHenrich and Boyd (1998) addressed this question theoretically by an evolutionary
computer simulation. Since we extend the Henrich and Boyd (1998) simulation in thispaper, let us review their model first in some detail. An outline of their simulation algorithm isshown in Fig. 1.
To model a temporally fluctuating environment, Henrich and Boyd (1998) assumed thatthe environment can change between two states, A and B, with a small probability in any twoconsecutive generations. Behavior A is more fit (i.e., provides a better chance of surviving) if
the environment is in state A, whereas Behavior B is more fit in environment B. Thus, natural
selection favors learning mechanisms that make individuals more likely to adopt the behavior
that is adaptive in the current environment (see Fig. 1, bottom).
As shown in the figure, two information sources are available for individuals to makebehavioral choices in the current environment, viz., opportunities for social (cultural) learningand individual learning. The social learning opportunity provides information about the
choices of several cultural parents in the preceding generation, and learning from several
predecessors leads to a statistically reliable estimate about the environment in many cases(law of large numbers), but the information is outdated if an environmental change has
occurred. By contrast, the individual learning opportunity provides updated information about
the current environment, but because of random noise in environmental information, as asingle observation, individual learning is statistically less reliable.
Each agent then combines the two kinds of information to decide how to behave in thecurrent environment. Two ‘‘genes’’ are pertinent to this combination. One is a gene affecting
variations in propensity to use social information over individually learned information. As
stated earlier, environmental information acquired by individual learning contains random
noise, so that even if the signal suggests that the current environment is in State A, it may
actually be in State B. As in signal detection theory (Green & Swets, 1966), Henrich andBoyd (1998) assumed that each agent has a decision threshold and if the signal value exceeds
it, he or she makes a choice based on the individually learned information (e.g., adopting
Behavior A). However, if the signal is insufficiently diagnostic, the agent disregards theindividual information and relies solely on social information. Individual variations about thethreshold were represented as effects of a gene in the simulation (the higher one’s threshold,the more likely one is to rely on social information).

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Fig. 1. An outline of Hernich and Boyd’s (1998) simulation algorithm.

The other gene regulates variations in conformity bias when using social information.
Suppose that the environmental signal is insufficiently diagnostic, so the individual must rely
on social information, and that three of five cultural parents sampled from the previous
generation chose Behavior A, while two chose Behavior B. Henrich and Boyd (1998)

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conceptualized the degree of conformity bias when using social information as a likelihood ofpreferentially adopting the most frequent behavior among the cultural parents (Behavior A in
the above example). That is, individuals with no conformity bias adopt Behavior A only
proportionally (with a 60% chance in this case), having no tendency to focus preferentially on
the most common behavior among the cultural parents. Individuals with a full conformity
bias adopt Behavior A with a 100% chance, always following the majority view. Combining
individual and social information as determined by these two genes, each individual makes abehavioral choice.
Then, natural selection operates: Those who behave adaptively gain a slight survival
advantage, and with the relevant genes transmitted in a haploid, asexual fashion, the genes
and resultant learning mechanisms that generate adaptive behavior in the current environment
increase in the population gradually. The simulation repeats this process for many generations
until an equilibrium state emerges in the population.1
Henrich and Boyd (1998) examined the adaptive value of conformity bias in social
learning by systematically varying key parameters, such as the rate of environmental
fluctuation and the average accuracy of environmental information. The result of primary
relevance for our purposes was that a conformity bias was favored under a wide range ofmodel parameters. Even when environmental change was rather frequent (e.g., the probabilityof environmental change between generations is .3), almost a full conformity bias emerged asa stable equilibrium.
1.3. Cost–benefit analysis of social learning
1.3.1. Free-rider problem
Henrich and Boyd (1998) established that conformity bias in social/cultural learning canserve as an effective uncertainty-reduction mechanism even in a nonstationary environment,but one critical factor seems to be missing from their model: asymmetry of learning cost
between individual and social/cultural learning. The simulation assumed that the costs ofindividual and social/cultural learning were identical and that everyone engaged in bothlearning modes fully. However, in many actual situations, individual learning may be costlier
than social learning in energy, time, or risk, and such an asymmetry creates the possibility that
if many others engage in costly individual learning, it may be better for some to skip theindividual information search completely and ‘‘free-ride’’ on others’ search efforts, whereas if
too many others just rely on social information, it may be better to engage in individual
learning. In other words, a free-rider problem exists with respect to costly individual
information search, and this may qualify the evolvability of conformity bias in social/
cultural learning.

1

This is a rough outline of the Henrich and Boyd (1998) simulation. The original simulation (see their
appendix) also had a phase of migration between different populations. However, we ignore this feature here, since
it is not critical for our theoretical development in this paper.

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1.3.2. A ‘‘producer–scrounger’’ structureThis problem is conceptually parallel to the ‘‘producer–scrounger’’ issue in social foragers(Barnard & Sibly, 1981; Giraldeau & Caraco, 2000; Krebs & Inman, 1992; Vickery,
Giraldeau, Templeton, Kramer, & Chapman, 1991). If there are many producers in a group,
it is better for an individual to scrounge food discovered/captured by others, but if manyothers cease foraging, then it is better to forage. As a result, producers and scroungers often
coexist in the group, constituting a mixed strategy Nash equilibrium.
Similarly, in the case of social/cultural learning, there may be equilibria in which
individual learners (information producers) and social learners (information scroungers)
coexist, and such a producer–scrounger relation may substantially limit the effectivenessof a conformity bias in social/cultural learning. Specifically, when the extra cost of individual
learning is high, the proliferation of ‘‘information scroungers’’ in the population may degrade
the average quality of collective knowledge under a nonstationary environment, reducing theadaptive value of conformity bias.
1.4. An extended simulation
To test these ideas, we conducted simulations that extended Henrich and Boyd’s modelby adding a third gene controlling variations in individual propensity to engage in costly
individual learning. Haploid agents with the ‘‘on’’ allele at this locus are information‘‘producers’’ who pay an extra cost for updated information about the current environment;
those with the ‘‘off’’ allele are information ‘‘scroungers’’ who skip the individual learning

Fig. 2. Mean proportions of information producers (members who engage in costly individual learning) in the
population over time, when the individual learning cost was high or low (simulation).

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opportunity. Thus, an individual learning opportunity is not the default, but is optional in
our algorithm. All else, including the fact that the social learning opportunity was provided
free to all individuals, remained as in Henrich and Boyd’s original simulation.
1.4.1. Results
Since the purpose of this simulation was to illustrate the logic of free-riding in social/
cultural learning, we do not provide a full report of the simulation results here. Instead,
we focus on the key parameter in our argument — extra cost required for individual
learning. (Readers who are interested in other features of the simulation may write to theauthors for details.) Fig. 2 illustrates changes in the prevalence of information producers
over generations. Individual learning was four times as costly in the ‘‘high-cost’’ case as
in the ‘‘low-cost’’ case.2 We conducted 10 simulation runs separately for each case, andthe figure shows the average proportions.
As shown in Fig. 2, a mixed strategy equilibrium emerged over generations, whereby
agents who engaged in costly individual learning and agents who relied solely on social
information coexisted. The equilibrium proportions depended on the cost of individual
learning. Fig. 3 displays equilibrium proportions of information producers in the population
as a function of this extra cost, which was varied systematically while keeping the othersimulation parameters unchanged (see Footnote 2). The equilibrium proportion of information producers decreased monotonically with an increase in individual learning cost; thepopulation was dominated rapidly by free-riding information scroungers.
The evolvability of conformity bias was also affected by the individual learning cost.
Fig. 4 displays equilibrium levels of conformity bias among information producers andinformation scroungers, respectively. (Simulation parameters other than individual learning
cost were unchanged; see Footnote 2.) Recall that full conformity bias (= 1) means that
individuals always choose the most frequent behavior among sampled cultural parents,
while no conformity bias (= 0) means a proportional imitation. As shown in Fig. 4,
conformity bias evolved when individual learning cost was negligible, as in Henrich andBoyd (1998), but with an increase in this cost, evolvability of full conformity bias washindered for both information producers and scroungers.
1.4.2. Discussion
The results of the extended simulation confirmed our reasoning that the free-rider
problem substantially affects adaptive values of social/cultural learning. As in the
producer–scrounger dilemma, the asymmetry in learning cost between individual andsocial learning leads to a mixed strategy Nash equilibrium in the population, with those
who engage in costly individual learning and those who free-ride on others’ search efforts
in stable coexistence (cf. Rogers, 1988). Such a structure also qualifies the adaptive value

2

The simulation parameters in Fig. 2 were set as follows: rate of environmental fluctuation = 0.01, average
accuracy of environmental information = 0.66, individual learning cost = 0.0024 (high cost) and 0.0006(low cost).

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Fig. 3. Equilibrium proportions of information producers in the population as a function of individual learning
cost (simulation).

of conformity bias in social/cultural learning. If individual learning is too costly, theequilibrium proportion of information producers is quite small. Given the nonstationary
nature of the environment, this prevents fresh environmental information from flowing intothe ‘‘cultural knowledge pool.’’ Thus, with an increase in individual learning cost, theaverage quality of the knowledge pool is degraded, and natural selection favors a decrease
in reliance on this flawed cultural knowledge and hence on conformity.

2. Experiment
We now submit these theoretical notions to empirical test in an experiment featuring anonstationary uncertain environment in a laboratory setting. Here, we focus on adaptive
learning of learning strategies rather than genetic evolution. That is, we examine whether
participants may adjust their learning strategies adaptively (i.e., in the manner suggested by
the theoretical analysis) through their experiences in a laboratory group. Our experimental
hypotheses are as follows:
Hypothesis 1a: In a temporally fluctuating uncertain environment, a mixed strategy
equilibrium will emerge over time, with information producers who engage in costly
individual learning and free-riding scroungers coexisting.

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Fig. 4. Equilibrium levels of conformity bias among information producers and information scroungers, as afunction of individual learning cost (simulation).

Hypothesis 1b: The equilibrium proportion will depend on the cost of individual
learning, with fewer producers in a group when the information search cost is higher.
Hypothesis 2: Participants will adjust their degree of conformity according to theindividual learning cost, showing less conformity when the information search costis higher.

2.1. Method
2.1.1. ParticipantsParticipants were 246 (157 male and 89 female) undergraduate students enrolled in
introductory psychology classes at Hokkaido University, Japan.
2.1.2. Experimental task
We implemented a fluctuating uncertain environment by means of a computer game called‘‘Where is the rabbit?’’ In this game, participants judged in which of two nests a rabbit wascurrently located based on stochastic information. Participants played the game for a total of
60 rounds. To operationalize the fluctuating environment, we instructed participants that the
rabbit ( = environment) had a tendency to stay in the same nest over time, but this tendency
was not perfect. The rabbit might change its location between any two consecutive roundswith a small probability. Thus, the location of the rabbit in a given round corresponds to thecurrent state of the fluctuating environment, conceptually. All participants experienced thesame randomly determined fluctuation pattern.

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2.1.3. Procedure
For each hourly session, six participants came together to the laboratory. These six-persongroups were randomly assigned to one of the two experimental cost conditions: 21 groups tothe high-cost condition and 20 groups to the low-cost condition.
Upon arrival, each participant was seated in a private booth and received further
instructions individually via computer. ‘‘Where is the rabbit?’’ was explained, and theplayers were told that their rewards would be contingent upon their performances in thisgame. Participants were instructed that they would play this game for many rounds(unspecified) and would gain 30 yen for each round in which they guessed the location of
the rabbit correctly.
Two information sources were available for making judgments about the rabbit’s location:
opportunities for social learning and individual learning. The social learning opportunity wasavailable to all participants as a default. That is, except for the first round, judgments of threeparticipants in the preceding round, who were randomly sampled from the five groupmembers other than self, were provided to each participant for free. As discussed earlier,social learning in a fluctuating uncertain environment provides statistically reliable (i.e.,aggregated) information cheaply, but this information may be outdated due to the possibilityof environmental change (the rabbit’s move).
In addition to this social learning opportunity, participants could use individual learning,
but at a cost: In each round, participants could choose to use a ‘‘rabbit-search-machine’’ by
paying a certain amount: 15 yen in the high-cost condition and 5 yen in the low-costcondition. Since the monetary reward for making a correct judgment was 30 yen, thoseparticipants who used the machine (i.e., individual learning) to get the correct answer in agiven round had to defray 50% (high-cost condition) or 16.7% (low-cost condition) of the
reward to cover the individual information search cost. The ‘‘rabbit-search-machine’’provided stochastic information about the location of the rabbit. By a series of pilot tests,
we set the accuracy of the search machine so that the using this individual learning
opportunity alone (i.e., without using social information) yielded 67% correct judgments
on average. In a practice session before the main experiment, participants were given
opportunities to familiarize themselves with the search machine and its accuracy. In the firstround only, when no social information was possible, all participants received information via
the search machine for free.
After every five rounds, participants received feedback about their relative performances in the group. That is, a summary table of all six members’ cumulative rewards up
to that point (e.g., 30 yen  the number of rounds where a participant made correct
judgments  15 yen  the number of rounds where the participant used the search machine,in the high-cost condition) was displayed on the computer screen after every five rounds.
This feedback provided an opportunity for participants to learn the effectiveness of theirlearning strategies, permitting adaptive learning of learning strategies for participants in a
way conceptually parallel to the selection phase in the evolutionary simulation. It should also
be noted that no direct feedback about the exact location of the rabbit was provided at any
point in the experiment; thus direct learning of the rabbit’s exact location was impossible
throughout the experiment.

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After completing 60 rounds, participants answered a brief postsession manipulation checkquestionnaire and were then paid and dismissed.
2.2. Results and discussion
2.2.1. Emergence of a producer–scrounger equilibrium over time
We hypothesized that, in a temporally fluctuating uncertain environment, a stable mixed
strategy equilibrium should emerge over time, with information producers who engage incostly individual learning and free-riding scroungers coexisting (Hypothesis 1a). We also
predicted that the equilibrium proportion would depend on the cost of individual learning,with fewer information producers when the information search cost was higher(Hypothesis 1b). Fig. 5 displays mean proportions of information producers in six-persongroups over 60 experimental rounds, in the high-cost and low-cost conditions, respectively,
and shows that both predictions were upheld. Dividing the 60 experimental rounds into threeblocks, a 2 (High Cost vs. Low Cost)  3 (Block) repeated-measures analysis of variance(ANOVA) yielded a significant Cost  Block interaction effect [ F(2,38) = 5.12, P < .02].
If this pattern indicates that the proportion of information producers in a group isapproaching equilibrium over time (as predicted by Hypothesis 1a), variances associatedwith the proportion should decrease over time. There are two ways to assess this possibility.
One way is to focus on between-groups variances. That is, if the proportion of information
producers is approaching equilibrium, between-groups variability in this proportion should

Fig. 5. Mean proportions of information producers in the six-person groups over time, in the high-cost and lowcost conditions (experiment).

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decrease over rounds. To test this, we conducted a multiple regression analysis on the
between-groups variances, with experimental round and condition as predictors, and as
expected, the round was a significant predictor [ b =  0.21, t(115) = 2.64, P < .01]. Although
the between-groups variance was generally smaller in the high-cost than in the low-costcondition [t(115) = 5.59, P < .001], the regression line collapsed across the two conditions had
a negative slope ( b =  0.21), indicating that variability among the groups in the information
producer proportion decreased monotonically as play progressed.
The other way to check the emergence of equilibrium is to focus on within-groups
variances: If the proportion of information producers is approaching equilibrium, fluctuationsin the proportion within each group should become smaller with successive play. Thus, foreach group, we calculated variances in the proportion of information producers across the first
block of 20 rounds, the middle block, and the last block. Mean within-groups variances(collapsed over the two experimental conditions) were 0.040 for the first block, 0.032 for the
middle block, and 0.030 for the last block. As expected, a 2 (Cost)  3 (Block) repeatedmeasures ANOVA yielded a significant main effect for the Block [ F(2,78) = 5.73, P < .01].
Although the within-groups variance was smaller in the high-cost condition than in the lowcost condition [ F(1,39) = 5.14, P < .05], as was the case with the between-groups variance, italso decreased monotonically as play progressed.
Taken together, these results provide clear support for Hypothesis 1a about the emergenceof a producer–scrounger equilibrium over time.
2.2.2. Monomorphism or polymorphism?
Given this demonstration of an equilibrium, the next question is how it was realized. The
mixed strategy Nash equilibrium could be a monomorphic equilibrium where all members
play the identical mixed strategy; in our case, this corresponds to a state where each player
behaves as an information producer stochastically in each round, with a probability of around.2 in the high-cost condition and around .5 in the low-cost condition (see Fig. 5). Alternatively, it could be a polymorphic equilibrium, with about 20% of players in the high-costcondition and about 50% in the low-cost condition always behaving as information
producers — a situation where ‘‘division of roles’’ exists. (More precisely, there can be intermediate states between these two extreme cases, with the aggregated proportion corresponding to the equilibrium rate; cf. Gintis, 2000).
To shed some light on this question, we first checked how frequently each participant
engaged in costly individual learning during each of the three blocks. Since each block is
composed of 20 rounds, the frequency of individual information search during a block could
range from 0 (pure scrounging) to 20 (pure producing). Fig. 6 displays distributions of theinformation-search-frequency for each block in the low-cost (top) and high-cost (bottom)
conditions, respectively, and shows that the relatively symmetric distribution pattern in thefirst block disappeared over time. In the low-cost condition, pure scroungers (0) and pureproducers (20) increased in the later blocks, while pure scroungers (0) dominated groups
rapidly in the high-cost condition.
To understand these changes in play more fully, we checked the consistency in eachparticipant’s information search strategy between two consecutive blocks to see if someone

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who engaged in individual information search, say, five times in the tth block would again doso five times in the (t + 1)th block Fig. 7 displays the proportions of participants who wereconsistent in this way, engaging in costly information search exactly the same number oftimes in the second block as in the first, or in the third block as in the second. Evidently,
participants became more consistent in their individual information search strategies overtime, showing greater consistency from the second block to the third than from the first to thesecond. A 2 (Cost)  2 (Consecutive Blocks: 1 ! 2, 2 ! 3) repeated-measures ANOVA
yielded a significant main effect for the Consecutive Blocks [ F(1,39) = 12.58, P < .001]. The
main effect for cost was also significant [ F(1,39) = 10.46, P < .003], indicating that strategies
were more consistent in the high-cost than in the low-cost condition.
Taken together, these results indicate that the equilibrium observed in the experiment wascloser to a polymorphic equilibrium than to a monomorphic equilibrium. ‘‘Division of roles’’characterized the equilibrium, especially with some hardcore information scroungers, who
never engaged in individual information search and consistently free-rode on others’ costly
search efforts, emerging in a group over time.
2.2.3. Adaptive adjustment of conformity bias
Besides the emergence of a producer–scrounger equilibrium, our evolutionary simulation
indicated that the adaptive value of conformity bias, viz., behavioral tendency to preferentially adopt the most frequent behavior among cultural parents (Boyd & Richerson, 1985),
decreased as individual learning cost increased. Given this result, we hypothesized thatpeople would adjust the degree of conformity according to the individual learning cost,
showing less conformity in the high-cost than in the low-cost condition (Hypothesis 2).
To test this hypothesis, we calculated the proportions of times that each participant adopted(conformed to) a majority choice, when his/her choice was in disagreement with the majority
choice. To illustrate, suppose that a participant chose ‘‘Nest A’’ for the location of the rabbit in
the previous round. Suppose also that when making a choice in the current round, theparticipant received social information that all three other participants (cultural parents) chose‘‘Nest B’’ in the previous round. Although the participant could acquire additional
information by individual information search, he/she somehow had to resolve the difference
between own view and the majority view in the previous round. We calculated the
proportions of times each participant conforming to the dissenting majority choice in suchoccasions, separately for cases where the participant behaved as an information producer or asa scrounger in the current round. Fig. 8 displays mean proportions of participants’‘‘conformity’’ choices in each condition.
As expected, conformity was less common in the high-cost than the low-cost condition,
regardless of whether participants engaged in costly individual learning in the current round.
A 2 (Cost)  2 (Producer vs. Scrounger) repeated-measures ANOVA yielded an expected
main effect for cost [ F(1,160) = 6.00, P < .02]. A main effect for Producer vs. Scrounger was
also significant [ F(1,160) = 3.90, P < .05], indicating that participants showed a greater
conformity bias when behaving as information scroungers than when behaving as producers
in the current round (cf. Fig. 4). Furthermore, these results were significant for ‘‘consenting’’
cases as well, where a participant’s choice in the previous round was in agreement with a386

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Fig. 6. Distributions of information-search-frequency for each block in the low-cost and high-cost conditions (experiment).

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Fig. 7. Mean proportions of members who were consistent in their information search frequencies between two
consecutive blocks (experiment).

majority choice. A 2 (Cost)  2 (Producer vs. Scrounger) repeated-measures ANOVA yieldeda significant main effect for Cost [ F(1,143) = 9.44, P < .01], again confirming less conformity

Fig. 8. Mean proportions of participants’ conformity choices toward dissenting majority in the high-cost and lowcost conditions (experiment).

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bias in the high-cost than in the low-cost condition. A main effect for Producer vs. Scrounger
was also significant [ F(1,143) = 94.45, P < .0001].
Thus, Hypothesis 2 was clearly supported; consistent with the evolutionary simulation
results, participants adopted a majority view to a lesser extent with an increase in information
search cost.

3. General discussion
This paper has examined adaptive values of social/cultural learning in a nonstationary
uncertain environment, for although such learning can be a cheap but highly effective
uncertainty-reduction mechanism about a temporally stable target, as illustrated by themushroom problem, its efficacy for temporally unstable targets is intuitively less clear.
Both the computer simulation and the laboratory experiment supported our reasoning that afree-rider problem is essential in a nonstationary, uncertain environment. As shown by
Henrich and Boyd (1998), social/cultural learning coupled with ‘‘conformity bias’’ whenusing social information (Boyd & Richerson, 1985, 1995) is an effective mechanism, if an
extra cost required for individual learning is negligible, but if this cost is not negligible, thefree-rider problem produces a producer–scrounger equilibrium about information search.
With an increase in individual learning cost, the equilibrium proportion of information
producers decreases, reducing the flow of fresh environmental information into the population and degrading the average quality of the cultural knowledge pool under a nonstationary
environment. Thus, conformity bias in social/cultural learning decreases with an increase inindividual learning cost. The experiment provided evidence that such adjustments in learning
strategies could also occur through one’s own experiences: the adaptive learning of learning strategies.
3.1. Theoretical links to social psychology and innovation research
These results seem to have important implications across several disciplines. Besides the
direct links to anthropology and animal learning that we have discussed in this paper (e.g.,
Boyd & Richerson, 1985, 1996; Durham, 1991; Giraldeau & Caraco, 2000; Henrich & Boyd,
1998; Heyes & Galef, 1996; Laland, Richerson, & Boyd, 1996; Rogers, 1988; Tomassello
et al., 1993), they have theoretical implications for social psychology and innovation researchin particular.
3.1.1. Implications for social psychology
In a classical paper on attitude formation in social psychology, Festinger (1954)emphasized the role of social learning, proposing a notion of social comparison. Festinger
postulated that there is a basic human drive to evaluate own beliefs, and that, if some‘‘physical reality checks’’ are available to assess the validity of beliefs, one will try to usethem, but if such checks are not available, one will engage in ‘‘social comparison,’’ using

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others as points of reference. Elswhere, Festinger (1950) also proposed a notion of consensual
validation: Consensus with others provides confirming evidence of the validity of one’s
beliefs. (See Kameda, Ohtsubo, & Takezawa, 1997; Kameda, Tindale, & Davis, in press foran extension of the notion of consensual validation at various psychological levels ininteractive groups.)
Notice that Festinger’s idea, as sketched above, has a remarkable similarity to the Henrichand Boyd model. As reviewed in Section 1.2., Henrich and Boyd assumed that people facing
uncertainty rely on personally learned information to make behavioral choices, if theenvironmental information is diagnostic enough — ‘‘if physical reality checks are available(or useful)’’ in the terminology of Festinger (1954) — but that when the environmental information is not diagnostic enough, people turn to social information (i.e., social comparison). Furthermore, Henrich and Boyd assumed that, when referring to social information,
people are affected by conformity bias — ‘‘consensual validation’’ in the terminology ofFestinger (1954).
These similarities suggest that it may be possible to recast Festinger’s theory in a modern
evolutionary perspective. Often, the availability of ‘‘physical reality checks’’ is a matter ofchoice or motivation rather than an external premise beyond an actor’s control: If one decides
to expend sufficient time or money, one can access the information to evaluate one’s beliefs
more or less fully. The analysis in this paper suggests that such a motivational featurenecessarily leads to a producer – scrounger structure in the population, and that theeffectiveness of social comparison as a validity check for one’s beliefs is determined
critically by this structure. In this sense, our contribution in this paper may be seen to haveintroduced the interdependent, motivational aspects of information-seeking behavior toFestinger’s theory.
3.1.2. Implications for innovation research
Our results have implications for innovation research as well. On the basis of observationsof the diffusion of new merchandise, agricultural and medical technologies, and so on, Rogers(1983) pointed out that innovations often spread in such a way that the cumulative frequencyof adopters of the novel knowledge follows a sigmoidal pattern over time. (‘‘Epidemiologicalmodels’’ of cultural transmission predict this pattern mathematically; e.g., Cavali-Sforza &Feldman, 1981; see also Boyd & Richerson, 1985.)
This observation suggests what types of psychological or social mechanisms may be
operating in the diffusion process, but Rogers’s theory is silent about why such novel
knowledge is produced occasionally in the society and why a certain number of innovators
exist in the group in the first place; the presence of novel knowledge or innovators in thesociety is simply a premise. There are several possibilities here (Giraldeau, Caraco, & Valone,
1994; Huffman, 1996), but our results may provide one ultimate reason: That the producer–
scrounger dynamic assures that a certain number of innovators emerge or exist in the
population as a stable equilibrium, at least in a changing environment. Of course, as suggested in this paper, the equilibrium proportion of such innovators will depend on the cost ofinnovation; if it is too costly, innovators should be rare. Thus, for expensive innovations tooccur in a society at a desired frequency, some exclusive rights to the novel knowledge’s

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perquisites (e.g., patents) or to other personal rewards (e.g., social prestige) may have to beguaranteed to the innovators.
3.2. Some future directions
In concluding, let us speculate about some future directions, two of which seem to us
potentially fruitful.
3.2.1. Biased modeling in social/cultural learning
One limitation of the present paper may be in our operationalization of social/culturallearning. In both the simulation and the experiment, social/cultural models were selected in an
unbiased manner, with members of the previous generation or round randomly sampled anddesignated ‘‘cultural parents’’ to an individual in the current generation. Although useful as afirst approximation, this feature may be unrealistic. Do our results still hold when individuals
are ‘‘biased’’ about choices of cultural parents?
We have addressed this question partially by extending our simulation in a ‘‘latticestructured population’’ (Nakanishi & Kameda, 2001). Effects of individual modeling bias
associated with geographical locations were of major concern. We ran two conditions in thesimulation: In one, individuals could acquire social information only about eight neighbors
located in adjacent cells, while the other was like the present simulation in that eight members
were chosen randomly for cultural reference. The results supported our thesis: Both the
producer–scrounger equilibrium and the decrement of conformity bias in response to
individual learning cost emerged, whether social/cultural learning was ‘‘local’’ or ‘‘global.’’Future research addressing effects of ‘‘biased modeling’’ (Boyd & Richerson, 1985), with
operationalizations of the bias not limited to the geographical bias as illustrated above, seems
to be a fruitful endeavor (e.g., Henrich & Gil-White, 2001).
3.2.2. Herd behavior in markets: effects of conformity in a frequency-dependent situation
Another interesting venue for future research may be to study ‘‘herd behavior’’ in financial
markets (Eguı́luz & Zimmermann, 2000) — a prime example of a nonstationary uncertain
environment in modern days. Izumi and Ueda (1999) observed that dealers in foreignexchange markets often value ‘‘consensus of the market’’ in making their trade decisions;consensus about future directions of the market among dealers affects own selling or buying
in a substantial manner.
The economic literature has mathematically modeled herd behavior in various contexts(e.g., Banerjee, 1992; Bikhchandani, Hirshleifer, & Welch, 1998; Kirman, 1993). What isparticularly interesting about financial markets is that an adaptive behavioral choice there isfrequency dependent. Recall that this paper has focused on a situation where the adaptive
value of a behavior was uniquely determined by the nature of the physical environment,
regardless of others’ choices: The payoff for a correct decision was constant regardless of
how many others made the same choice. However, this is not the case in financial markets
where a dealer’s decision to buy or sell brings different returns to the dealer, depending onother dealers’ decisions. Notice that, in the market situation, two conceptually related but

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distinct types of frequency dependence may affect individual fitness, one accruing frombehavioral choice per se (e.g., buying or selling), the other from choice of learning strategies(e.g., behaving as an information producer or scrounger). Is conformity bias in such a ‘‘duallyfrequency-dependent’’ situation adaptive?
In this paper, we have combined evolutionary simulations inspired by anthropologicalwork with an experiment to shed some light on adaptive values of social/culturallearning in a nonstationary uncertain environment. Our results provide useful insights
into various social psychological phenomena in modern societies, as well as into theevolutionary forces that have shaped our social minds. Although early social psychologists were well aware of the critical relevance of anthropological theories to their work
(cf. Jacobs & Campbell, 1961; Jones & Gerard, 1967), this link seems to have been
largely lost in modern social psychology. We believe that the evolutionary framework
may change this situation, providing a strong common thread to link both disciplines ina mutually fruitful manner.

Acknowledgments
This research was supported by Grant-in-Aid for Scientific Research 14310048 from the
Ministry of Education, Culture, Sports, Science, and Technology of Japan. We are grateful
to Peter Richerson, Reid Hastie, Yohsuke Ohtsubo, Masanori Takezawa, Itaru Ishiguro, Ryo
Tamura, Mizuho Shinada, and Takafumi Tsukasaki for their helpful comments on an earlier
version of this manuscript. We deeply thank Joseph Henrich and Robert Boyd for allowing
us to use the code of their simulation and Martin Daly and Margo Wilson for their valuable
editorial help.

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