Psychological Review
2011, Vol. 118, No. 1, 76 –96© 2010 American Psychological Association
0033-295X/10/$12.00 DOI: 10.1037/a0020699

Democracy Under Uncertainty: The Wisdom of Crowds and theFree-Rider Problem in Group Decision Making
Tatsuya Kameda

Takafumi Tsukasaki

Stanford University

Hokkaido University

Reid Hastie

Nathan Berg

University of Chicago

University of Texas at Dallas

We introduce a game theory model of individual decisions to cooperate by contributing personal
resources to group decisions versus by free riding on the contributions of other members. In contrast to
most public-goods games that assume group returns are linear in individual contributions, the present
model assumes decreasing marginal group production as a function of aggregate individual contributions.
This diminishing marginal returns assumption is more realistic and generates starkly different predictions
compared to the linear model. One important implication is that, under most conditions, there exist
equilibria where some, but not all, members of a group contribute, even with completely self-interested
motives. An agent-based simulation confirmed the individual and group advantages of the equilibria in
which behavioral asymmetry emerges from a game structure that is a priori perfectly symmetric for all
agents (all agents have the same payoff function and action space but take different actions in equilibria).
A behavioral experiment demonstrated that cooperators and free riders coexist in a stable manner ingroups performing with the nonlinear production function. A collateral result demonstrated that, compared to a dictatorial decision scheme guided by the best member in a group, the majority/plurality
decision rules can pool information effectively and produce greater individual net welfare at equilibrium,
even if free riding is not sanctioned. This is an original proof that cooperation in ad hoc decision-making
groups can be understood in terms of self-interested motivations and that, despite the free-rider problem,
majority/plurality decision rules can function robustly as simple, efficient social decision heuristics.
Keywords: group decision making under uncertainty, majority/plurality rules, marginally diminishinggroup returns, evolutionary games, behavioral experiment

could allow them to make much more precise decisions about
where to locate a large construction project than any one of theindividuals in these groups could alone. Similarly, in a primitive
setting, five tribesmen could cooperate to decide where to forage
for prey much more effectively than if all worked independently.
Despite such a potential for collective wisdom, there is a fundamental trade-off between selfish, individualistic goals and the moregeneral social welfare. Participation in a group activity is often described as a sacrifice of personal utility. Who has not pondered
whether to blow off preparation for a group assignment (e.g., not tostudy the candidates’ resumes before a hiring committee meeting, toshirk one’s homework before a joint study committee meeting, etc.)
and to free ride on the efforts of those who have fulfilled their social
obligation? Many theoretical analyses of small-group cooperation
conceptualize group enterprises as social dilemmas and treat cooperative behavior as a puzzle (Dawes, 1980). In social dilemma situations, the personal payoff to an individual group member is always
less when she or he cooperates in the group enterprise than it would
be if she or he acted as a free rider, even though the overall groupwelfare is larger when all members cooperate than when nobody
cooperates. Consistent with the sacrifice theme, most behavioral experiments on social dilemmas show that average contributions topublic goods deteriorate significantly after a few iterations if nopunishment opportunity exists (e.g., Fehr & Gächter, 2002; Herrmann, Thöni, & Gächter, 2008; for reviews, see Camerer, 2003; Fehr

Every human society relies on groups to make important decisions because, among other advantages, groups have more
problem-solving resources than any individual member (e.g., Kerr
& Tindale, 2004). Indeed, there are many tasks that can beachieved only by a group effort and that could never be accomplished by one individual or by many individuals working separately. For example, groups of geologists, engineers, demographers, and business executives have complementary skill sets that

This article was published Online First September 6, 2010.
Tatsuya Kameda, Center for Advanced Study in the Behavioral Sciences, Stanford University; Takafumi Tsukasaki, Department of Behavioral Science, Hokkaido University, Sapporo, Japan; Reid Hastie, Center
for Decision Research, Booth School of Business, University of Chicago;
Nathan Berg, School of Economic, Political and Policy Sciences, University of Texas at Dallas.
This study was supported by Grant-in-Aid for Scientific Research17330133 from the Ministry of Education, Culture, Sports, Science andTechnology of Japan to Tatsuya Kameda. This article was prepared while
Tatsuya Kameda was a residential fellow of the Center for Advanced Studyin the Behavioral Sciences at Stanford University, 2008 –2009. Takafumi
Tsukasaki died on January 23, 2005, at age 25.
Correspondence concerning this article should be addressed to Tatsuya
Kameda, who is now at the Department of Behavioral Science, Hokkaido
University, Bungakubu, N10 W7 Kita-ku, Sapporo 060-0810, Japan.
E-mail: tkameda@let.hokudai.ac.jp76DEMOCRACY UNDER UNCERTAINTY& Fischbacher, 2003; Ledyard, 1995). Even with an alternative
public-goods game featuring a preliminary binding commitment
round, which produces more theoretical and empirical support for atleast some players to make social contributions, the prevailing interpretation holds that cooperation is difficult to sustain (Cason, Saijo, &Yamato, 2002; Cason, Saijo, Yamato, & Yokotani, 2004; Saijo &Yamato, 1999). In conventional group-production and problemsolving tasks, social psychological research has provided considerable
evidence of social loafing (Latané, Williams, & Harkins, 1979; seealso Kerr & Tindale, 2004; Williams, Harkins, & Karau, 2003, forreviews). If members of small decision-making groups (e.g., juries,
panels, committees) are playing such a game, the theory predicts thatcooperation is hard to sustain without enforcement mechanisms. Thisframing of group enterprises as social dilemmas leads to a pessimistic
view of groups as problem solvers or decision makers and implies thatpublic goods requiring group cooperation will be severely undersupplied.
The above sketch illustrates a fundamental gap between the two
images of group decision mechanisms—a highly intelligent device
that can achieve collective wisdom versus a defective social process that degrades toward suboptimal performance (Janis, 1972).
How can one reconcile the two contrasting images? The central
puzzle is as follows: How can the “wisdom of crowds” (Surowiecki, 2004) be sustained in face of the free-rider problem? Do
groups require some policing mechanism that enforces members’
contributions toward group enterprises to yield collective wisdom?

Is Group Decision Making Necessarily a Social Dilemma?
To defend the viability of group decision processes, some social
choice theorists have argued that people may feel good, experiencing
expressive benefits (Brennan & Lomasky, 1993), when they contribute to the functioning of a group (Downs, 1957) or when they fulfill
civic duties (Meehl, 1997; Riker & Ordeshook, 1973). Survey datafrom large-scale elections provide modest support for this view, while
identifying other social psychological factors as well, including social
norms (Knack, 1992) and cognitive biases (Opp, 2001). Applying thisview to small-group decision making, we would expect some people
to be cooperative—for example, turning out for group meetings,
engaging in costly information search prior to meetings— driven bythese prosocial motives.
Although prosocial motives underlie some contributions to group
enterprises, such an account begs questions concerning the ultimate
sources of these motives (see Posner, 2000). The present article
proposes an alternative theoretical framework in which to interpret
positive contributions to group enterprises without invoking prosocial
motives. After all, there must be some personal advantage to individual members in cases where no collection of individuals acting independently could achieve even part of the group product that can beachieved by the collective. Even in tasks where contributions are
incremental, we argue that contrary to the social dilemma interpretation, cooperation can in many real-world and theoretical contexts (i.e.,with diminishing marginal group returns to individual contributions)
be both self-interested and beneficial to the group. Such an analysis
would explain the widespread cooperation in human societies as afunction of the benefits of individual cooperation without adding any
new prosocial motives. Our argument draws on a diverse sample ofempirical and theoretical literatures, ranging from theoretical biology
to experimental economics. We first analyze structures of various

77

group tasks in natural settings, in terms of functional relations between members’ inputs and group productivity (McGrath, 1984;
Steiner, 1972). We then discuss the implications of this analysis forcooperation in group decision making.

The Ubiquity of the Marginally Diminishing Returns
Group-Production Function in Naturally
Occurring Tasks
When a group of people collaborates to make a decision or to
produce some other tangible good (e.g., investment committee, strategic planning staff, production line), productivity usually increases
monotonically with increases in group size, at least over some range.
Yet the relationship often falls short of linearity. Although we cannot
conduct a census of all group-production tasks in society, we areconfident that the almost universal relationship between group sizeand productivity is monotonically increasing but with marginallydiminishing returns over an appropriately specified range in groupsize. This can be observed in many natural settings.
The behavioral ecology literature studying animal behavior provides a useful starting point to see why this is the case. Marginallydiminishing returns are common in many systems of the animal
kingdom, including social vertebrates and social insects (Foster,2004). For example, sentinel behavior of many mammals and birds
is a collective endeavor with marginally diminishing returns (Bednekoff, 1997; Trivers, 1971). Think of it this way: If one is
camping with a group of 10 people, there are much larger benefits
from the first and perhaps second person staying awake to warn theother campers about approaching bears than there are from theninth and 10th campers, whose contributions generate virtuallyzero marginal benefits, staying awake.
To illustrate, let us denote the average probability of a single
sentinel detecting an approaching predator as p. Assuming that
there is no process loss (Steiner, 1972), the probability that a groupwith n individuals being on watch detects the danger successfullyis then approximated by 1 ⫺ (1 ⫺ p)n (Laughlin, 1980; Lorge &Solomon, 1955). This means that the group success in vigilance
increases monotonically but diminishes at the margin, with anincrease in the number of sentinels, n, in the group. Social foraging, another key survival task, shares this structure. When a flockof birds is searching for food, discovery of a rich food patch by asingle bird results in other birds joining to forage in the same patch(Giraldeau & Caraco, 2000). The group success in locating a rich foodpatch is thus approximated by the identical function, 1 ⫺ (1 ⫺ p)n,where p denotes the average probability of a single bird encounteringa rich patch and n refers to the flock size (Barnard & Sibly, 1981; see
Foster, 2004, for other examples in the animal kingdom).
Although many core everyday group-production tasks for humans also revolve around foraging and risk monitoring (seeKameda & Tindale, 2006, for review), groups in these animal
examples are surely nothing more than collections of individuals,
where no substantial coordination activities exist. Birds do notdeliberately orchestrate their sentinel or food-searching behavior,and group performance is best described as a probabilistic aggregation of individual outputs rather than the product of a systematicgroup design (e.g., group decision making), which is more typicalof human social coordination.
Interestingly, however, explicit coordination efforts do not necessarily eliminate the marginally diminishing nature of group

KAMEDA, TSUKASAKI, HASTIE, AND BERG

78

production for the following reasons. First, difficulties in coordination among members multiply with group size, and interpersonal
conflicts are also likelier to occur, among other production-cuttingsocial factors (see Steiner, 1972; Thompson, 2004, especially
Chapter 2, for comprehensive reviews). Second and more important, the fundamental structure of many natural tasks mandates
diminishing returns in productivity from later contributions. When
information is redundant or task-relevant skills overlap from member to member, diminishing returns are inevitable even with perfect coordination (Clemen & Winkler, 1999; Makradakis & Winkler, 1983), and when any task is not perfectly divisible into
independent subtasks, individual performances must be somewhat
redundant, producing diminishing productivity as more individuals
are added to the collective enterprise.
For example, consider group judgments that rely on information
aggregation, which represents an essential subtask in group performance (McGrath, 1984). Members collectively estimate a quantity, such as the future price of a stock. Let us presume that thegroup estimate is approximated by the arithmetic mean, or simple
average, of the individual estimates, which is a valid description ofmany behavioral judgment aggregation processes (see Clemen &Winkler, 1999; Hastie, 1986; Kerr, MacCoun, & Kramer, 1996, forreviews). Furthermore, if individual estimates have equal signalto-noise ratios and vary around the true value of the stock pricewith uncorrelated random errors, the arithmetic mean is a statistically optimal aggregation rule (Larrick & Soll, 2006; Surowiecki,2004). For an average computed from a randomly drawn sample,
the law of large numbers tells us the group estimate should
converge on the true value, and well-known calculations of thevariance of the arithmetic mean show explicitly that the precisionof this group estimate improves with each additional observation(i.e., an individual’s judgment in the present example). The reduction in expected squared error by adding one more member to an
n-person group,

冉

冊

␴2
␴2
␴2
,
⫽
⫺
n共n ⫹ 1兲n⫹1n
is greater than zero, where ␴2 is the variance of the individual
estimates. However, note that the marginal improvements in theaggregate estimate (in terms of smaller random deviations from thetrue value) diminish with increasing group size, n (see Condorcet,
1785/1994, for an analogous proof when the judgment is categorical, e.g., between political candidates).
Indeed, if we turn to classic analyses of group performance andproductivity in social psychology (McGrath, 1984; Steiner, 1972),
we find that many abstract task types imply that group performance indices (amount of goods produced, time to produce, accuracy of estimates) will be related to group size with a monotonically increasing but marginally diminishing returns function. This
includes decisions based on unanimous, majority, and truth-winsgroup decision rules (see Condorcet, 1785/1994; Smoke & Zajonc,
1962); disjunctive tasks (Steiner, 1972) where a success of only
one member is sufficient to achieve a collective goal (e.g., risk
monitoring, resource finding, and other eureka problems; see
Kameda & Tamura, 2007; Laughlin, 1980; Lorge & Solomon,
1955; Taylor & Faust, 1952); and additive tasks (Steiner, 1972)
where members’ inputs are summed to determine an overall groupperformance (e.g., group estimation by averaging, physical tasks

as exemplified by a tug of war; see Hastie, 1986; Ingham, Levinger, Graves, & Peckham, 1974; Kravitz & Martin 1986). The
only clear exceptions to this generalization are conjunctive tasks(Steiner, 1972), where the weakest link member determines theoverall group performance and more members mean poorer performance, and synergistic tasks, where the group-production function would be positively accelerated. We know of no examples ofthe deliberate use of groups to solve conjunctive tasks (unlessinstitutional or situational constraints impose conjunctive task demands), and we know of very few examples of verified synergistic
group performances in the scientific literature (see Larson, 2009,
for a recent comprehensive review).
In summary, a marginally diminishing group-production function seems to apply in many group performance domains in natural
settings. Perhaps because of the mathematical convenience of thelinear group-production function, this widespread diminishingmarginal returns feature is conspicuously missing from most of thegroup performance and public-goods literatures (see also Kerr,
1983, for one of early attempts to link group performance withsocial dilemmas). The linear public-goods model assumes thateach individual’s contribution yields a constant return for thegroup and that the return, which is shared evenly by all members,
is less than the individual cost of cooperation, regardless of thenumber of other contributors. This implies the overly pessimistic
prediction that one should almost never expect to see contributors
to public goods because, at all levels of inputs in the groupproduction function, defection is the dominant strategy (Ledyard,
1995; but see Laury & Holt, 2008).1
1

Laury and Holt (2008) provided a survey of the economics literature
regarding nonlinear public-goods games. To understand the motivation thathas led economists to study nonlinear public-goods games, which is very
different from the present study’s motivation, it is helpful to recall that thestandard linear public-goods game most frequently studied in laboratory
experiments has a unique Nash equilibrium in which all group members
contribute zero. Thus, the theoretical model upon which most public-goods
experiments are based predicts universal and absolute free riding. In
contrast to this theoretical prediction, participants in experiments usuallycontribute significantly more than zero. These contributions typically decline with repeated trials, but contributions remain well above zero evenafter as many as 60 rounds. Interpreting this frequently replicated findingof greater than zero contributions in linear public-goods games remains
difficult, however, because of the statistical difficulty of measuring closeness to a boundary in any action space (in this case, the zero-contribution
Nash equilibrium). At a boundary point in participants’ action space,deviations can occur only in one direction; random error of any kind will
push empirical averages away from their true value, and the law of large
numbers no longer holds. Faced with this statistical problem of measuring
deviations from an equilibrium located at the boundary of the range ofindividual contributions, economists turned to nonlinear public-goods
games as a mechanism for generating incentive structures with Nashequilibria located on the strict interior (0 ⬍ equilibrium frequency ⬍ groupsize) of the range of possible contributions. This is the primary motivation
for most of the existing experimental economics literature on nonlinear
public goods (see Laury & Holt, 2008). In contrast, this article reconsiders
nonlinear public-goods incentive structures as a widespread empiricalregularity and investigates what insights they have to offer to psychology
and the neighboring social science literatures dealing with social dilemmas,
information acquisition, and voting behavior, concerning the commonplaceobservation of heterogeneous groups consisting of both cooperators andfree riders.

DEMOCRACY UNDER UNCERTAINTYEmergence of a Mixed Equilibrium
Given a marginally diminishing production function, is defection still the dominant strategy? The answer turns out to be notnecessarily. A theoretical biologist, Motro (1991), concerned with
abstract foraging problems provided a general powerful framework in which to analyze this question. Let us suppose that wehave a six-person group and that the group-production function(e.g., mapping the number of contributors who search for foodonto the expected quantity of food available for each member ofthe group, humans, nonhuman animals, robots, etc.) takes a marginally diminishing form as shown in Figure 1. As in social
dilemmas, we assume that the group-production benefit is sharedequally by all group members but that production costs are borneby each producing or contributing member individually (Dawes,
1980; Kameda, Takezawa, & Hastie, 2003). The x-axis represents
the number of cooperators, while the y-axis represents the expected
gross return to each member of the group (group gross return/6).
Gross refers to the fact that the costs of contributing are not yet
represented in Figure 1. The production function’s concave increasing form generates marginal returns to individual contributions to the group, labeled ␦n, when the number of cooperators
increases from n to n ⫹ 1, that are indeed diminishing as moreindividuals contribute.
Motro (1991) analytically identified an evolutionarily stablestrategy (ESS) in this situation: Cooperate as long as the increment, ␦, exceeds individual cost (denoted c), but switch to defection otherwise.2 In other words, keep cooperating while the contribution yields an individually positive expected net return. Note
that, even when the increment ␦ representing the marginal individual return to contributing is too small to justify the cost tocontribute for an individual, additional contribution by the individual may still be beneficial to the entire group (i.e., while themarginal individual return, ␦m, by joining m other cooperators in
the group is less than individual cost, c, aggregate group return,6 ⫻ ␦m, may still be greater than the individual cost). This means
that, as in linear social dilemmas (Dawes, 1980), each individual’s
rational action may lead to the inefficient level of contributions at
the group level (Pareto inefficient). However, in the nonlinear case

Expected gross return to eachgroup member

4
3.5
3

δ5

If c <δ, cooperate forδ4group production.

δ3

2.5

δ2

2
1.5

δ1

1

If c >δ, defect.

δ0

0.5
0
0

1

2

3

4

5

6

Number of cooperators in a group

Figure 1. An illustration of a marginally diminishing return curve (unithere is arbitrary) as a function of number of cooperators. The y-axis has
been adjusted to expected gross return to each individual (group gross
return/6). An evolutionarily stable strategy is defined as follows (Motro,
1991): Cooperate as long as the marginal increment (␦) exceeds individual
cost (c), but switch to defection beyond that.

79

depicted in Figure 1, ␦ is not constant but is a function of thenumber of cooperators. As long as c ⬍ ␦n for some small n, themodel predicts that some members in the group will contribute/cooperate; in other words, the important implication of diminishing returns in this empirically more realistic group-production
function is that defection is no longer a universally (i.e., over theentire range of number of cooperators) dominant strategy. Thus,
many naturally occurring tasks with marginally diminishing returncurves do not necessarily constitute social dilemmas.
Figure 2 reexpresses the production function from Figure 1 as anet (individual gross return ⫺ cost) payoff function for a cooperator (solid curve) and as a net payoff function for a defector(dashed curve), both expressed as functions of the number of othergroup members who contribute (individual cooperation cost was
fixed at 0.7 in Figure 2). Note that the two curves intersect at an
equilibrium, specifying the number that is predicted by the theoryof Nash equilibrium of rationally self-interested cooperators in thegroup. As can be seen in the figure, an individual is personallybetter off cooperating when there are few cooperators and betteroff defecting when there are already several cooperators among theother group members. The net benefits of cooperating or defecting
depend on the frequency of the alternative strategy within thegroup; neither strategy is dominant (Laury & Holt, 2008). Toomany players opting for one strategy reduces its relative profitability while increasing the profitability of its alternative, providing an incentive for individuals to switch. Since the two strategies
are mutually constrained in terms of relative profitability, we
expect a mixed equilibrium to emerge (Gintis, 2000; Maynard
Smith, 1982). At equilibrium, the group reaches a stable state inwhich complementary proportions of cooperators and defectors
coexist, achieving an average frequency of cooperators given by
the point at which the two individual net payoff curves intersect. In
Figure 2, the two curves intersect when the number of othercooperators is between one and two, which predicts two cooperators and four defectors on average in the group with the illustrative costs and benefits of cooperation in this example.

Group Decision Making Under Uncertainty
Motro’s (1991) model provides a powerful theoretical benchmark to reconsider realistic cooperation levels in group performance and public-goods provision (Kerr, 1983; Laury & Holt,
2008). We apply this model to a stylized group decision situation,
which constitutes a core group-production task in everyday life.
We have two goals in this article: (a) to examine the degree towhich Motro’s framework captures members’ cooperation for thegroup enterprise and (b) to examine the effects of various voting
2
In game theory, an ESS is a strategy that, if adopted by all players ina population, cannot be invaded (outperformed) by any competing alternative strategy (Gintis, 2000; Maynard Smith, 1982). Suppose a situationin which a group is composed only of individuals with the focal strategy x.
Now a question arises concerning if such an all-x group is robust enoughto block a small 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 otherstrategies in the game, then Strategy x is called an ESS.

KAMEDA, TSUKASAKI, HASTIE, AND BERG

80
4Defector

Expected individual net payoffs

3.5
3
2.5
2

Cooperator

1.5
1
0.5
0

0

1

2

3

4

5

Number of other cooperators in a group
Figure 2. Expected individual net payoffs (individual gross return ⫺cost) for a cooperator (solid curve) and a defector (dashed curve), as afunction of the number of other members who cooperate (derived from thegross return curve in Figure 1 with individual cooperation cost fixed at
0.7).

rules (e.g., majority/plurality, best member rule) on the quality ofgroup decision outcomes when free riding is possible.

Applicability of Motro’s Model to Group
Decision Making
Suppose that a group of six members needs to select a single
location in which they will search together for food. Each member
decides whether to cooperate (produce) by seeking decisionrelevant information, an activity with some personal costs, or tofree ride (defect, scrounge) on the decisions of the other members.
Obviously, if no member decides to seek information, the groupdecision will be uninformed and far from optimal. Yet it is also
true that, if all members seek information, there will be redundancy and a loss of potential production value. Motro (1991)
proved that it is wasteful for all individuals to seek information
precisely because of the diminishing marginal returns of the groupproduction function. We apply this logic to group decision making
under uncertainty, where the objective value of a choice alternative, or truth (Laughlin, 1980), must be inferred through imperfectstochastic information. We predict that group decision making
under uncertainty, which forms the core of modern committee
meetings as well as primordial team foraging, would yield a mixed
equilibrium in which cooperators and free riders coexist, rather
than the all-defect equilibrium in social dilemmas (with the unrealistic linear-additive production function; see also Footnote 1,
above). An equilibrium mixture of cooperators and defectors will
be determined by the Motro function.

Robustness of Majority/Plurality Group Decision Rule
Although the argument so far has depicted group decision
making as if it were a uniform concept, each group decision setting

entails specific design features. Even if we limit our focus toconsensual decision making (e.g., juries, committees, panels),
there are numerous variations about how to implement the groupdecision system. This includes choices of quorum rules, polling
procedures, aggregation rules, and so on (e.g., Hastie, Penrod, &Pennington, 1983; Kameda, Tindale, & Davis, 2003; Regenwetter,
Grofman, Marley, & Tsetlin, 2006). How do these design features
affect members’ cooperation levels and consequently determine
the quality of final group decision outcomes? In this article, we test
whether the majority/plurality rule, whereby the option in thegroup’s choice set with the most votes becomes the group’s final
choice, can sustain members’ cooperation and serve as a robust
truth-seeking decision procedure in uncertain environments(Hastie & Kameda, 2005; Sorkin, Hays, & West, 2001).
Using computer simulations and a behavioral experiment,
Hastie and Kameda (2005) evaluated various group decision rules
based on their adaptive accuracy in choosing the mutually mostbeneficial alternative in an uncertain, simulated test bed environment. These aggregation rules included averaging, the best member rule, Condorcet majority, majority/plurality, and so on (seeHastie & Kameda, 2005, for details). When the adaptive successstandard is applied to evaluate the rules (Gigerenzer, Todd, & the
ABC Research Group, 1999; Hammond & Stewart, 2001; Hastie,1986), the majority/plurality rule fares quite well, performing at
levels comparable to much more cognitively taxing rules such as
the averaging rule. The majority/plurality rule also matches thecomputationally demanding Condorcet majority winner that is thecommon standard in evaluations of preferential choice rules(Arrow, 1951; Mueller, 2003; Regenwetter, Ho, & Tsetlin, 2007).
These results indicate that, despite its computational simplicity, themajority/plurality rule can achieve surprisingly high levels of
performance under uncertainty.
In these previous studies, however, it was assumed that everybody would cooperate to support the group enterprise (Hastie &Kameda, 2005; Sorkin et al., 2001). Thus, it is an open question
whether the majority/plurality rule would be theoretically andbehaviorally successful when there is a personal cost to be an
informed voter. Several key questions in the present researchconcern the performance of the majority/plurality rule under more
realistic assumptions about the group-production function andindividual cooperation costs. If a mixed equilibrium holds (Motro,
1991), how efficient is the productivity at the equilibrium underthe majority/plurality group decision rule? Does the majority/
plurality rule degrade into a universal free-riding tragedy of thecommons situation where decisions are made by uninformed voters (Downs, 1957; Mueller, 2003)? How does it compare with thegroup outcome guided by the best and brightest benevolent dictator in a group—a logically coherent decision system that has been
pitted against democratic rules in the social choice literature(Arrow, 1951; Laughlin, 2006) and a solution that is selected insome naturally occurring human groups?
In the following, we first report on an evolutionary computer
simulation (Kameda, Takezawa, & Hastie, 2003; Kenrick, Li, &Butner, 2003; Smith & Conrey, 2007) to determine if there would
be a mixed equilibrium in a group-foraging task with significant
and stable levels of cooperation under different group decisionrules and then compare adaptive success of the different decisionrules at respective equilibria. Next, we report a behavioral experiment that implemented a group-foraging task under uncertainty inDEMOCRACY UNDER UNCERTAINTY

81

Given such a dilemma, it seems that uncooperative members areunilaterally better off than cooperative members, and we might
expect them eventually to dominate the population, which would
yield an all-defect equilibrium. On the basis of Motro’s (1991)
model, we posited that a mixed equilibrium would emerge, where
both cooperative and uncooperative individuals would coexist in
the population. We tested this prediction in two populations governed by contrasting decision rules: the majority/plurality rule(based on the winner with the most votes in a one-member– onevote election) versus the best member rule (where the member
with the best long-term ability/accuracy dictates the choice). As abenchmark, we also examined a population operating with therandom member rule (where a randomly chosen member dictates
the choice to the other members), as in Hastie and Kameda (2005).
If a mixed equilibrium did indeed emerge for each population as
predicted by the model, we would consider our second question:
Does the majority/plurality rule produce better outcomes in termsof individual net benefit than the best member rule at the respective equilibrium in each population? Is the wisdom of crowds
sustained over time, or does the majority/plurality rule unravel intoa free-riding world in which decisions are made predominantly byuninformed voters?

an interactive laboratory setup. In both cases, individuals (computer agents in the simulation study and human participants in the
behavioral experiment) were required to decide whether to cooperate or defect before voting on a foraging location.

Evolutionary Computer Simulation
Overview
We relied on an evolutionary simulation model based on Darwinian logic to explore the implications of the diminishing groupreturns model and to evaluate the theoretical viability of a majority/plurality group decision rule when informed participation in thedecision is individually costly. In the evolutionary simulation, we
first specified a set of behavioral strategies and then let theminteract in the same population (e.g., Axelrod, 1984; Gintis, 2000;
Kameda, Takezawa, & Hastie, 2003). The strategies were definedin terms of the basic cooperate (at personal cost) versus defect (atno personal cost) distinction. The Darwinian logic dictates that
more successful strategies in the current population reproduce at
higher rates for the next generation, analogous to biological evolution in an ecological niche. In social scientific applications, suchchanges are not necessarily evolutionary but may reflect, mostnotably, social imitative learning of successful strategies in a group(Gintis, 2000). We observed whether such a change in the population structure would lead to a stable collective state where thepopulation was dominated by a set of strategies (or a strategy) andno further changes would occur. Such a stable end state is called
an evolutionary equilibrium (Gintis, 2000; Maynard Smith, 1982).
In the following simulations, we formulated four behavioral
strategies in terms of the basic distinction between cooperating (ata personal cost) versus defecting (at no personal cost): (a) whether
to pay a cost to acquire the information needed to make wellinformed individual judgments and (b) whether to pay a cost to
vote. Individual cooperation (information search and voting) can
increase the quality of group decisions and thus enhance overall
group return, but it entails a personal cost as well, which presents
a dilemma for the agent in deciding at what level to take part incollective action.

Simulation Method
Simulation platform. To illustrate the basic features of oursimulation, we rely on the metaphor of a primitive forager seeking
resources at locations in an uncertain physical environment (seeHastie & Kameda, 2005, for more details on the procedure). Thistask represents the essential features of a general decision problem
faced by any organism: which option among a set of alternatives tochoose, given noisy information about the payoff contingent onchoosing each option. The simulation world has two major components, environmental events, namely, the amounts of reward(gain or loss) available at various locations, and foragers, whose
fitness depends on accurate judgments of the environmental events(see Figure 3). The state of an environmental event (reward available) is known probabilistically to foragers through proximal,
partially valid cues (i.e., a noisy cue is the true value plus a

Proximal Cues

Cij1

Environmental Events.68

location j’svalue,

.17

Wi.1

Cij2

Wi.2

Foragers
forager i’s estimateof location j’s value,
)

4

Qj

Wi.3

.08

Cij3

Figure 3.

Structure of the simulated judgment environment.

LM

82

KAMEDA, TSUKASAKI, HASTIE, AND BERG

white-noise error term). Thus, each individual faces the adaptive
task of aggregating the information contained in these noisy cues
to infer whether environmental locations are rewarding or punishing. The individual reward judgment process is a direct implementation of Brunswik’s general lens model framework for perception
and judgment (Brunswik, 1956; Cooksey, 1996; Gigerenzer et al.,
1999; Hammond & Stewart, 2001).
Structure of the environment. In the simulation, we set up thestochastic features of environmental events as follows. Let j index
one of 10 possible foraging locations, j 僆 {1, 2, . . . , 10}. To
represent the true value of the food available at each of these 10
locations, we generated random numbers from a normal distribution N(0, 30) denoted Qj (we refer to this quantity as the payoff inthis article). Motivation for the units used (e.g., standard deviation
equal to 30) is discussed below. These true values, however, could
not be directly known; information about each of the 10 values
represented by Qj was available in three noisy cues. These cues
were generated by taking each location’s true resource value, Qj,
and adding normally distributed error to it, creating a cue value
composed of true value ⫹ error. The normally distributed error
terms were specified with standard deviations of 10, 20, and 30.
Therefore, the cues, denoted (C1, C2, C3), differed in validity as
predictors of the true value of each patch, with validities of 0.90,
0.69, and 0.50, respectively, on a proportion-of-varianceaccounted-for metric (i.e., R2). As shown in the left portion of
Figure 3, the optimal linear combination of these cues for estimation (explaining 92% of the variance of Qj) was
E关Qj|C1 ,C2 ,C3 兴 ⫽ 0.68C1 ⫹ 0.17C2 ⫹ 0.08C3 .

(1)Foragers. As is evident from the discussion above, the adaptive goal for each individual forager is to combine the cues, in thesame manner as the optimal linear combination rule, to yield an
estimate for value of the payoff (i.e., expected nutritional value)available at each location. Such an individual estimation process,which we refer to as a judgment policy (Brunswik, 1956; Hammond & Stewart, 2001), can be represented by how the person
weights the three proximal cues to form an estimate. Our simulation implemented this feature by assigning judgment policies toforagers at random as follows (see the right portion of Figure 3).
Member i’s estimation of location j’s value is expressed (i ⫽member, j ⫽ location, and k ⫽ cue)
Estimated Qij ⫽ wi.1 Cij1 ⫹ wi.2 Cij2 ⫹ wi.3 Cij3 ,(2)where wi.1 is the weight forager i gives to his or her perception of
Cue 1 for location j, denoted Cij1. The model allows agents toexperience perceptual errors. In other words, cue values are notusually perceived veridically, and different judges make different
errors. Each perceived cue value, Cijk, has two components: a true
cue value (C.jk) that is common to all members, plus anenvironmental-perceptual error (eijk), associated uniquely witheach member i’s perception of the cue (Cijk ⫽ C.jk ⫹ eijk). The
error component, eijk, is generated randomly from N(0, 20).
We relied on Dawes’s (1979; also Brehmer & Joyce, 1988, andN. H. Anderson, 1981) observation that, in judgment tasks such as
the one in our simulations, people appear to use simplified linear
aggregation rules. Instead of using optimal weights (e.g., Equation1), people judge as though they rely on approximate weights andoften on equal weights, getting the predictive direction right but

only approximating relative cue importance. (Dawes, 1979, also
demonstrated that such improper linear models achieve levels of
accuracy comparable to optimal linear aggregation rules in many
situations; see also Gigerenzer et al., 1999, for analyses of othersimplified estimation rules).
Dawes’s (1979) conclusion implies that most people would
weight the three cues approximately equally in aggregation. On thebasis of this reasoning, our simulation used the following procedure in the implementation of wi.k. For each member of each
foraging group, we generated three random numbers once and thenstandardized them so that their sum equaled one. The standardized
fractions determined the member’s judgment policy which remained identical throughout the group’s 100 hunts. Thus, themodal judgment policy under this procedure is equal cue weighting, (.33, .33, .33), but there is considerable variation in individual
cue-weighting policies. The important point is that a modal forager’s estimates in the basic simulation are not statistically optimal(see Equation 1) but, on average, are based on equal cue weights.
Behavioral strategies of the foragers. Members’ judgment
policies for cue weighting were the only individual differences that
Hastie and Kameda (2005) considered in their simulation platform;
these are cognitive differences among members. To address theissue of the free-rider problem in group decision making, thecurrent simulation introduced members’ motivational differences
for cooperation as a key element in the evolutionary algorithm.
Initially, we focused on two dimensions that often underlie members’ cooperation in group decision making (Downs, 1957; Riker& Ordeshook, 1973): (a) whether to accept the information-searchcost to make well-informed individual judgments (Kameda &Nakanishi, 2002, 2003; Lupia, 2002) and (b) whether to bear thecost of participating (voting) in group meetings (Knack, 1992). As
shown in Table 1, each dimension had two behavioral options,creating four genotypes.
For the search dimension, some agents engage in individual
information search about the environment and personally incursome cost (a simulation parameter to be varied systematically) toobtain that information. These searchers have access to the environmental cues in all 10 locations (see Figure 3) and combine themaccording to their judgment policies. Other agents (nonsearchers)
skip information search to avoid the search cost. Accordingly, they
have no cue information to inform their judgments, and if nonsearchers vote (see below), they endorse one alternative randomly(essentially introducing noise into the group decision). As in many
public-goods situations, group payoffs are equally available to allirrespective of members’ cooperation levels.
We quickly discovered, however, that only two of the four
original strategies could survive at the equilibrium: searcher/voter
hybrids and nonsearcher/abstainer hybrids. The other two hybrids,
Table 1
Four Behavioral Strategies Implemented in the
Evolutionary Simulation
Participate in group meetings?
Engage in individual
information search?

Yes

No

Yes
No

Searcher/voter
Nonsearcher/voter

Searcher/abstainer
Nonsearcher/abstainer

DEMOCRACY UNDER UNCERTAINTY

searcher/abstainer and nonsearcher/voter, disappeared in thecourse of Darwinian selection. These two hybrids are evolutionarily irrational behavioral choices in this model in that they cannot
sustain themselves (searchers who abstain from voting harm themselves by wasting resources expended on information search, while
nonsearchers who vote also harm themselves by degrading theaccuracy of group decisions in terms of average payoff). Thus, we
collapsed the 2 ⫻ 2 system of genotypes into a dichotomous
cooperator (producer) versus defector (scrounger) classification,
which we use from now on in reporting the results of the simulation study. (In the behavioral experiment to be reported subsequently, it remained an empirical question what frequencies ofthese four behavioral genotypes would be observed.)
These strategies were subject to evolutionary selection in thesimulation in that the prevalence of each genotype in the population was adjusted over time based on the behavioral payoffs thatthey received in the past; more successful strategies reproduced at
a higher rate in later generations.3
Evolutionary algorithm. The evolutionary algorithm is summarized in Figure 4. For illustration, let us consider a population
governed by the majority/plurality rule. For each simulation run, we
start with an equal probability (.50) that any member would be
assigned to one of the two behavioral strategies. Next, 12-person
teams are formed by randomly selecting individuals of various genotypes. Using the metaphor of foraging, this 12-person team goes on ahunt together.
For each hunt, members behave according to their assigned
strategies. The group members are first provided an opportunity toacquire information about the current environment. Searcher/
voters engage in individual learning of the three cues for each of
10 locations at some personal cost, which was assumed to beconstant across foragers (cost was varied as a simulation parameter). They rely on these three cues to choose the best alternative
based on their judgment policies (see Figure 3; see also Footnote
3). Nonsearcher/abstainers skip the individual learning, avoid thecost, and do not vote or otherwise influence the group decision.
Searcher/voters’ opinions are then aggregated by the majority/
plurality rule, selecting the location endorsed by the greatest number of voters in the meeting. In the case of a tie, one of the top
alternatives is selected at random. Payoff accruing from thegroup’s collective choice (i.e., the payoff available in the chosen
patch) is equally shared among all members, whereas the corresponding costs are subtracted from the shares for the cooperative
searcher/voters.
The group repeats the same process for 100 different hunts in a
new environment on each hunt. The entire routine runs for 10,000
12-member groups, based upon which we calculated the mean net
benefit (fitness) for each of the two strategies by collapsing their
net behavioral outcomes (individual gross return ⫺ cost) over
1,000,000 (100 ⫻ 10,000) trials.
One generation ends here, and according to the Darwinian logic,
agents with more fit strategies produce slightly more offspring for the
next generation. We formulated the selection mechanism by a standard numerical technique called the replicator dynamic (see Gintis,
2000). In the replicator dynamic, a strategy associated with a fitness
greater than the average fitness in the current generation increases infrequency in the next generation, while a strategy associated with afitness less than the average decreases (see Appendix A for details).

83

Using this idealized evolutionary process, we observed adjustments inthe population strategy structure over generations.
The simulation repeated the above steps for many generations
until an equilibrium state emerged in the population. The equilibrium refers to a state where no further changes occur in the
distribution of strategies in the population. In the simulation, we
terminated iterations when changes in proportions of the strategies
between two consecutive generations dropped below .0001.
As noted, we created three types of populations (societies), one
governed by the majority/plurality rule; one governed by a best
member rule, where the most competent member (i.e., the member
whose judgment policy was closest to the optimal weighting)among the searcher/voters in each group was initially designatedthe leader and made the decisions for the subsequent 100 hunts;
and the other governed by a random member rule, where one
searcher/voter in each group was initially selected at random andmade decisions for the 100 hunts. For each of the three decisionrule societies, we conducted separate simulation runs to see if anequilibrium state emerged. When the equilibrium was reached ineach society, we compared the three societies (governed by different decision-making rules) in terms of average individual net
benefits (individual fitness).

Simulation Results and Discussion
Emergence of a mixed equilibrium over time. We systematically varied the cost parameters for cooperation (cost parameters
for information search and voting) and the number-of-resourcelocations parameter. We fixed the group size at 12 throughout thesimulation runs. Figure 5 displays equilibrium proportions of thetwo viable strategies (searcher/voters vs. nonsearcher/abstainers)
in each population (majority/plurality rule, best member rule, or
random member rule), as a function of the total cooperation cost(the figure shows simulation results when the number of resource
locations was set at 10; later, we show results from a sensitivity
analysis where the two parameters were varied simultaneously).
As expected, a mixed equilibrium emerged in each of the threesocieties. For example, when the cooperation cost was set at 0.03,
45% were cooperative searcher/voter hybrids in the majority/
plurality rule population, 39% in the best member rule population,and 35% in the random member rule population.4 Rather than
3
Notice that, different from the behavioral strategies, no game theoretic
aspect is involved in a member’s judgment policy. The theoretically best
judgment policy always corresponds to the optimal linear combination model(e.g., Equation 1), which is solely determined by the environmental structure,
independent of other members’ judgment policies in a group. In other words,
a member’s judgment policy is not a strategy in the game theoretic sense.
Thus, after being randomly generated for each group member at the outset(see the text), the judgment policy was fixed and not under evolutionary
control throughout a simulation run. Elsewhere, we have discussed how suchcognitive differences can affect qualities of group decisions under different
aggregation rules (see Hastie & Kameda, 2005, for details).
4
To illustrate, let us suppose that a group picks up a location with 36
resource units, where the value of the resource available at each of 10
locations has been generated randomly from N(0, 30). Because eachmember of the 12-person group receives an equal share, this yields three
resource units per person, which means that the total cost for cooperation
to be subtracted from a cooperator’s share, 0.03, corresponds to 1% of thegross individual payoff.

KAMEDA, TSUKASAKI, HASTIE, AND BERG

84

Population at Generation t
At the start of each simulation run (Generation 1), the prevalence of thetwo behavioral strategies, searcher/voters and nonsearcher/abstainers (Table1), in the (infinite) population was set equal, 50% each.

Sampling of a 12-person group from the population
A 12-person “hunting team” is composed by random sampling.

Opportunity for information search about the environment

Voting and group aggregation
Depending on a given aggregation rule (majority/plurality, best
member, or random member), the group picks one alternative for
hunting. The resource in the chosen patch is shared equally among all
members. For the cooperative searcher/voters, however, the respective
costs are subtracted from their shares.

Calculating mean outcomes
Mean outcome (fitness) for each of the two strategies is calculated bycollapsing their net outcomes (benefit – cost) over 1,000,000 (= 100 x 10,000)
trials.

Go to next generation t + 1

Replication for 10,000 groups

100 “hunts” in new environments

Only searcher/voters engage in individual learning about theenvironment at some personal cost. They can accessthe threeenvironmental cues (Figure 3) and make judgments according to their policies.

Selection
Depending on their mean outcomes (fitness), the two strategies are
selected using a replicator dynamic, with the greater fit strategy yielding slightly
more offspring for the next generation.

Figure 4.

Outline of the evolutionary simulation platform.

being dominated solely by the free-riding nonsearcher/abstainer
hybrids, the cooperative searcher/voter hybrids sustained themselves in a stable manner. These results support our hypothesis thatcooperation in group decision making under uncertainty can beconceptualized as a game where a mixed equilibrium exists (Laury& Holt, 2008; Motro, 1991).
Individual net benefits. Given the emergence of the mixed
equilibrium, we could address our next question: Does the majority/plurality rule produce better results for individuals in terms of
average net benefits than the best member rule when incentives forfree riding exist? Figure 6 displays individual net benefits (individual gross return ⫺ cost) at the respective equilibria in the threesocieties, as a function of total cooperation cost.
Individuals in the majority/plurality rule population were betteroff than those in the best member rule population, who were
substantially better off than the random member rule population,
for the parameter range displayed in Figure 6. However, noticealso that the difference in net benefits between the two key
populations decreased as the cooperation costs increased. Thissuggests that the superiority of the majority/plurality rule over thebest member rule may be eliminated and even reversed whencooperation costs are high. Indeed, this was the case in all thesimulations we ran. When the cooperation cost was 0.27 or higher(see Footnote 4), the relative standings of the two rules werereversed (these points are not displayed in Figure 6 to simplify thegraphic representation).
How can we interpret these patterns? A close inspection of theequilibrium proportions of cooperative and defecting individuals
in the population (see Figure 5) provides some insights. Figure 5
shows that the proportions of searcher/voter hybrids decreased
monotonically with a higher cooperation cost. For example, in the
majority/plurality rule population, the proportion of the cooperative individuals dropped to 27% when the cost was 0.15 (on theright side of the diagram). This implies that, with the increase incooperation cost, the average frequency of searcher/voter hybrids(cooperators) in each 12-person group could eventually fall below
three (⬇ 12 ⫻ 0.27). Notice that three is the minimum number ofvoters for the majority/plurality rule to be meaningfully comparedwith the best member rule and other group decision processes, as
if the frequency of voters is less than three, no majority/plurality
can be defined among the voters, except for the theoretically trivial
case of perfect agreement. In other words, when the cooperation
cost is high, searcher/voter hybrids become so rare that mostdecision-making groups fail to assemble the critical voter quorum(three). In these circumstances, the best member rule beats theindecisive majority/plurality rule.

DEMOCRACY UNDER UNCERTAINTY(a) Majority/Plurality rule

Proportions in the population

100%
90%
80%
70%

(b) Best-Member rule(c) Random-Member rule

100%

100%

90%

90%

80%
55.44

64.15 68.36

60%

80%

60.67

70%
71.12 73.18

60%

66.81 70.1
72.37 74.13

70%

50%

50%

40%

40%

40%

30%

30%

30%
20%

44.56

35.85 31.64

10%

28.88 26.82

39.33

20%
10%

33.19 29.9
27.63 25.87

20%

Cooperation costs (Search +Vote)

Cooperation costs

Non-searcher / Abstainer

27.87 25.96 24.44

0.03

0.06

0.09

0.12

0.15

Cooperation costs

Searcher / Voter

Equilibrium proportions of the strategies in each population.

Sensitivity analysis. To see how robust these findings were,
we conducted a sensitivity analysis varying key parameters: thenumber of choice alternatives (from two to 11) and the cooperation
costs (from 0.03 to 0.27), with group size held constant at 12. A
mixed equilibrium, as we observed in Figure 5, emerged for all
combinations of these parameter values. Given this, we report only
relative standings of the majority/plurality rule to the best member
rule in terms of individual net payoffs at the respective equilibria.
Figure 7 displays differences in individual net benefits between thetwo populations (majority/plurality rule minus best member rule)
as a joint function of the cooperation costs and the number of

3.500

Majority/plurality
Best-member

3.400

72.13 74.04 75.56

0%0.03 0.06 0.09 0.12 0.15

3.450

34.74 30.48

10%

0%

0.03 0.06 0.09 0.12 0.15

Figure 5.

65.26 69.52

60%

50%

0%

Random-member

3.350

Individual net payoff

85

3.300
3.250
3.200
3.150
3.100

alternatives. In the iceberg-shaped surface, the unshaded (abovewater) regions refer to parametric combinations where the majority/plurality rule outperformed the best member rule (i.e., the net
benefits difference score was greater than zero), and the shaded(underwater) regions refer to the reverse situation.
First, as seen in Figure 6, the relative advantage of the majority/
plurality rule over the best member rule decreased monotonicallyand was eventually reversed with higher cooperation costs (represented on the x-axis). As the cooperation costs increased, theequilibrium proportion of searcher/voter hybrids in the population
decreased, and groups operating with the majority/plurality rule
could rarely assemble three voters. Second, the majority/plurality
rule was more successful with greater numbers of choice alternatives (the second abscissa). As can be seen in the figure, theunshaded regions in the graph, where the majority/plurality rule
outperformed the best member rule, were larger with more locations (in general, more elements in the group’s choice set).
To summarize, the sensitivity analysis demonstrated that the majority/plurality rule was more successful than the best member rule
when the cooperation cost was small and when there were more
locations (i.e., choice alternatives in the group’s choice set). (Ofcourse, if cooperation costs are extremely high, no one will cooperate,
and all decision rules will perform at equally low levels.) These results
suggest that, despite the inherent free-rider problems in group decision
making, there exist large parametric regions where the majority/
plurality rule is successful in an uncertain game against nature.

3.050

Behavioral Experiment

3.000

0.03

0.06

0.09

0.12

0.15

Cooperation costs (Search + Vote)
Figure 6. Individual net payoff at the equilibrium in each population.

The next step was to see how these theoretical results fared as
hypotheses about the behavior of human decision-making groups.
For this purpose, we designed a laboratory task where we could
compare the performance of the majority/plurality rule and the best

KAMEDA, TSUKASAKI, HASTIE, AND BERG

86

Majority/plurality > Best

0.05

Best > Majority/plurality
0.04

0.03

Differences in 0.02
individual net
payoffs
0.01(majority/plurality- best)
0
10
-0.01
6
-0.02
0.03

0.06

0.09

0.12

0.15

0.18

0.21

2
0.24

Number ofalternatives(patches)

0.27

Cooperation costs (search + vote)
Figure 7. Sensitivity analysis about the superiority of the majority/plurality rule to the best member rule inindividual net payoff.

member rule, while measuring each member’s cooperation during
the decision-making process. On the basis of the simulation results, we predicted (a) that participants would be divided into oneof the two behavioral types, searcher/voter hybrids (cooperators)
or nonsearcher/abstainer (defectors) hybrids, and (b) that the proportions of these two types of members would stabilize over time,
consistent with the expectation of a mixed equilibrium. We also
predicted (c) that groups governed by the majority/plurality rulewould be better off than those governed by the best member rulein terms of individual net payoffs. Of course, the simulation model
suggests that this prediction is parameter dependent (see Figure 7);
our aim was to provide an empirical demonstration (or an existence proof) of one case where majoritarian decision rule works
well under uncertainty in face of the free-rider problem.

MethodParticipants. Participants were 180 undergraduate students(127 males and 53 females) enrolled in introductory psychology
classes at Hokkaido University (Sapporo, Japan).
Group decision task. We implemented a hunting-underuncertainty task through a local area network in a behavioral laboratory. We told participants that they were members of six-person teams
that must choose one of 10 locations in which to hunt (instead of the
12-person groups assumed in the simulation, we used six-persongroups in the experiment due to practical constraints). Resource levels(e.g., prey values) in each location were generated randomly from anormal distribution N(80, 30) (unit ⫽ 1 yen). The resource levels
could be estimated only on the basis of three stochastic cues thatdiffered in predictive validity and perceptual errors, analogous to themodel in the previous section. The parametric setups for the cue
structure and the perception errors were identical to those used in thesimulation reported above in Figure 3.5
The group decision task was to select the most profitable location,using either the majority/plurality rule or the best member rule. These
rules were assigned to each group at the outset. Participants’ rewards
were made contingent upon their performance in the hunts. Specifically, resources in the chosen patch were divided evenly among all six
members, although cooperative members (who engaged in informa-

5
N(80, 30) was used to generate resource levels in each location in theexperiment instead of N(0, 30), as in the simulation; the change in mean value did
not affect uncertainty level in the foraging task because the standard deviation of
the distribution was held identical. The only exception was that both the true
resource value (Qj) and the true cue value (C.jk) in each of 72 hunts during theexperiment were held to be common across all groups in the experiment. These
common seeds had been generated randomly from the respective normal distributions (identical to those used in the simulation), for once, prior to the experiment.
This procedure was different from the simulation, where the true resource value(Qj) and the true cue value (C.jk) were generated randomly for each hunt by eachgroup. Given the much smaller sample size in the experiment (n ⫽ 3 hunts ⫻ 15
groups per trial as compared to n ⫽ 100 hunts ⫻ 10,000 groups per generation in
the simulation), making the value-generation procedure exactly parallel to that of
the simulation would have introduced too much random noise. Because thischange was theoretically trivial, we chose the current procedure for the experiment.
For each hunt by each group, however, we newly added individual perception error
terms (eijk) to generate perceived cue values (Cijk), as in the simulation. Thus, thecue values that participants actually observed in each hunt could be different fromperson to person.

DEMOCRACY UNDER UNCERTAINTY

tion search and/or voting) incurred personal costs, which were subtracted from their individual accounts.
Procedure. Six participants were randomly assigned to one oftwo decision-rule conditions (majority/plurality or best member) inexperimental sessions lasting approximately 1 hr. There were 15
groups (90 participants) in each condition. Upon arrival, eachparticipant was seated in a private cubicle and received instructions individually via computer displays. After the hunting-underuncertainty task was explained, participants were provided an
opportunity to familiarize themselves about how to use the threestochastic cues (see Figure 3 for the cue structure) displayed on thescreen for each of the 10 locations. For 20 practice trials, eachparticipant made personal judgments as to the most profitablelocation out of 10 alternatives. On each of the 20 practice trials,
participants received feedback about their choice success, in termsof the discrepancy in resource levels between the chosen locationand the most profitable one (the most profitable location was also
identified on the screen). Because this was a practice session, all
the judgments in this phase were individual, without reward.
Majority/plurality rule versus best member rule. After practicing the cue-based judgment-under-uncertainty task, participants
were told they were members of a six-person hunting team seeking
the most profitable location out of 10. They were told that theirrewards in the experiment would be contingent on the success of
their group decisions. Participants in the majority/plurality rulecondition were told that the location endorsed by the greatest
number of voters would be designated as their collective choice by
the computer. Participants in the best member rule condition weretold that the best individual (who was most accurate in the practicesession) among the voters on each hunt would be automaticallyselected by the computer (anonymously) and that that participant’s
choice would be designated the group choice.
Costs for cooperation. Costs (incentives for free riding) wereintroduced as follows: First, members who chose to collect environmental information when forming individual judgments had topay 3 yen. Second, voters who chose to participate in a groupmeeting to express their preferences had to pay another 3 yen.
However, the payoff from the selected location was to be equallyshared among all six members whether they incurred costs or not.
The experiment consisted of 24 trials with three new hunts in eachtrial. Before each of the 24 trials, each participant was asked
whether he or she wanted to pay 3 yen for information search andwhether to pay 3 yen to vote on the trial (these decisions weremade independently).6 Only individuals who had paid theinformation-search cost could access the environmental cues whenforming individual judgments, while those who had not paid thesearch cost could not access the cue information in the three hunts
during that trial. Only individuals who had paid the voting costwere able to express their preferences during that trial. For eachhunt, the group decision was reached by aggregating these voters’
preferences via either the majority/plurality rule or the best member rule (the aggregation was conducted by the computer).
Outcome feedback. After each of the 24 trials, participants
were provided private feedback. The feedback consisted of theirpersonal net benefit earned during the trial (i.e., evenly split shareof the sum of group outcomes from the three hunts minus an
individual’s own cooperation costs) and the number of groupmembers who voted during the trial.7 Through the summary feedback, participants could learn how well they had performed on the87

trial and adjust their cooperation levels on the next trial. Unlike thepractice session, no specific feedback was provided as to personal
accuracy.

Results and Discussion
Emergence of stable cooperation over time. Our evolutionary simulation model suggested that group decision making under
uncertainty would eventually yield a mixed equilibrium, where
cooperative and uncooperative individuals would coexist in astable manner. Specifically, we predicted that participants would
be divided into one of the two behavioral types, searcher/voter
hybrids versus nonsearcher/abstainer hybrids, over time and that
proportions of these two types of members would stabilize.8
Interlocked information-search strategies and the voting strategies. We examined how frequently each participant showed the
behavioral linkage between the choices of information-searchstrategy and voting that we observed in the simulation. For thisanalysis, we divided the 24 trials into three 8-trial blocks. Figure 8
shows mean proportions of the trials in each block, where participants’ strategic choices were coherent, that is, either totally cooperative (searcher/voter hybrid) or totally uncooperative (nonsearcher/abstainer hybrid).
Consistent with the simulation, mean proportions of the coherent choices (searcher/voter hybrids and nonsearcher/abstainer hybrids) increased over time, reaching nearly 100% coherence in thelast block. A 2 (condition) ⫻ 3 (block) ⫻ 15 (group) analysis ofvariance (ANOVA), using a hierarchical linear model, yielded amain effect for block, F(2, 52) ⫽ 36.52, p ⬍ .001. This means that,
if an individual voted in the last block of the experiment, she or he,with probability very close to one, had also engaged in costly
individual information search. Voting by ignorant members intruth-seeking situations (e.g., group foraging) would be selfdefeating in terms of individual net payoff because it would
degrade the overall quality of group decisions while exacting apersonal voting cost (Lupia, 2002). If members are aware of thesecost issues, a behavioral linkage between information-search strategy and voting should emerge voluntarily without social enforce-

6
The total cost for cooperation (information search ⫹ voting) implemented in the experiment (6 yen per trial) may seem small. Yet this was not
the case because, as described below, it actually corresponded to about
10% of the average gross payoff to each individual in the trial.
7
Our own experience tells us that it is hard to determine whether othermembers have actually prepared for committee meetings (e.g., engaged incostly information search); thus, the number of searchers during the trial
was not included in the outcome feedback.
8
It is important to note that the simulation and the behavioral tests involved
different manifestations of what we think are the same basic principles of functional adaptation. In the simulation, an evolutionary algorithm determined thesurviving strategies over thousands of generations using a replicator dynamic.
However, in the behavioral experiment, individual learning and inference processes composed the mechanism through which an adaptive equilibrium was
discovered. We believe that both routes, population cross-generational and individual learning, yield adaptive behavioral strategies, but we do not want to obscurethe differences between the two mechanisms (but see Campbell, 1988).

KAMEDA, TSUKASAKI, HASTIE, AND BERG

88
1.00

Mean proportions of coherent
behavioral choices

0.98
0.96
0.94
0.92
0.90
0.88

Majority/plurality
Best member

0.86
0.84
0.82
0.80
1st

2nd

3rd

Block
Figure 8. Interlocking between the two strategic choices. Participants’behavioral choices of information-search strategy and voting became more
coherent over time. Error bars indicate one standard error (SE).

ment mechanisms in truth-seeking groups, as in the present experiment.
Stabilization of cooperation (searcher/voter hybrids) over time.
Were the cooperative members (search/voter hybrids) able to sustain themselves in actual groups, as predicted by the evolutionary
simulation model? Or was the coherent-choice pattern due to thetendency of uncooperative members (nonsearcher/abstainer hybrids) eventually to dominate groups? For this analysis, we estimated the theoretical equilibrium frequency of cooperators in eachcondition. We first estimated the empirical group and individual
payoff functions by means of an econometric procedure and thenestimated the equilibrium frequency and the Pareto-optimum frequency for the majority/plurality rule (see Appendix B for theestimation procedure).
Figure 9 displays mean frequencies of the cooperative members
in each group across the 24 trials, along with the equilibrium
frequency and the Pareto-optimum (local and global) frequency inthe majority/plurality rule condition.9 Although the mean frequencies of cooperative searcher/voter hybrids slightly decreased overtrials, about half of members remained cooperative on the last trial.
Notice that the mean frequency of cooperative members in the
majority/plurality rule condition was 3.13 on the final trial and wassignificantly greater than the equilibrium frequency, t(14) ⫽ 3.89,
p ⬍ .001. Indeed, the modal number of cooperators across allgroups in all trials in the majority/plurality rule condition was
three, with more than 75% of observed action profiles containing
three or more cooperators. Figure 10 displays the empirical distribution of the numbers of group members who cooperated (N)across 360 observations (15 groups ⫻ 24 trials).
Notice also that variances associated with the mean frequencies,
as depicted by vertical bars in Figure 9, decreased over time inboth conditions. Dividing the 24 trials into three 8-trial blocks, a 2(condition) ⫻ 3 (block) repeated measures ANOVA on the variability index revealed a significant main effect for block, F(2,56) ⫽ 8.02, p ⬍ .001. Thus, most groups stably functioned at a
locally Pareto efficient level of public-goods contributions, withthree contributors and three free riders per group. The unambiguous modal value (see Figure 10) is remarkable in finding a locally

best outcome in group terms despite the fact that the individual
incentives built into the game structure would predict far lesscooperation at N ⫽ 1 (see Appendix B).
Did the stabilization in cooperation rates occur at the aggregate
level or at the individual level? That is, did different individuals
cooperate on each trial, or did the same individuals cooperate (andthe others consistently defect) across trials? Figure 11 displays
how frequently individual participants cooperated during each of
the three blocks in the majority/plurality rule and the best member
rule conditions. Since each block is composed of eight trials, thefrequency of cooperation (search and vote) during a block could
range from zero (a total free rider) to eight (a total cooperator). As
can be seen in Figure 11, the relatively symmetric distribution
pattern in the first block dissolved over time, and the distribution
in the last block was U-shaped, with the 100%-consistent cooperators and 100%-consistent free riders jointly representing 32%(majority/plurality rule) and 42% (best member rule) of the participants. This indicates that, at least when individual adjustment isthe basis for sorting into strategies, there is a trend toward polymorphic role self-assignment, with types of individuals adopting
different consistent strategies.
Taken together, these results indicate that both uncooperative
nonsearcher/abstainer hybrids and cooperative searcher/voter hybrids persisted across trials and that their proportions in each groupstabilized over time.
Did the majority/plurality rule outperform the best member
rule? Given that the rates of cooperation stabilized in each groupover time, we could address our next question: Did the majority/
plurality rule produce better net benefits to group members thanthe best member rule? Figure 12 shows that average per-trial
individual net benefits were higher in the majority/plurality rulecondition than in the best member rule condition across the threeblocks of trials. A 2 (condition) ⫻ 3 (block) ⫻ 15 (group) repeatedmeasures ANOVA using a hierarchical linear model revealed asignificant main effect for the group decision rule, F(1, 28) ⫽11.90, p ⬍ .002.
How does this pattern relate to the theoretical results from theevolutionary computer simulations? Notice that the theoreticalequilibrium frequency of cooperators, estimated by fitting an
econometric model to the data (see Appendix B), was one in the
majority/plurality rule condition (see Figure 9). This means that,theoretically, the majority/plurality rule should yield worse (atleast, no better) outcomes than the best member rule because theminimal effective voting quorum for the majority/plurality rule(three members) could not be assembled. However, as we observed in Figure 9, the mean frequencies of the cooperative,
searcher/voter hybrids stabilized at slightly above three under the9
The equilibrium (Nash) frequency occurs where the marginal individual return is equal to the cost of cooperation (see Figures 1–2; see also
Figures B1–B2 in Appendix B). The Pareto-optimum frequency occurs
where the marginal group return is equal to the cost of cooperation; net
group payoff function is maximized at the Pareto point. In the econometricanalysis reported in Appendix B, we estimated the equilibrium (Nash)frequency and the Pareto-optimum frequency. As shown in Figure B3, theempirical net group payoff function reveals two Pareto-optimal points, a
local maximum, where the number of cooperators in the group is three, anda global maximum at six. At each of these Pareto points, net group payoff
is maximized locally or globally.

Mean frequencies of cooperative members

DEMOCRACY UNDER UNCERTAINTY

89
global Pareto point

6

5

4

local Pareto point32

Equilibrium point

Majority/plurality

1

Best member0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Trial
Figure 9. Mean frequencies of cooperative members (searcher/voter hybrids) across the 24 trials. Vertical lines
indicate one standard error (solid line for the majority/plurality rule condition, dotted line for the best member
rule condition). Empirically estimated Nash point in the majority/plurality rule condition was one, whereas
Pareto-optimal points were three (local Pareto-optimal) and six (global Pareto-optimal). See Appendix B for
details of the estimation procedure.

majority/plurality rule. The minimal quorum (three searcher/voter
hybrids) persisted in the experiment, and consequently, the majority/plurality rule yielded higher individual net payoffs than the best
member rule (shown in Figure 12). These results imply that behaviorally—although not predicted by theory—the parametricrange where the majority/plurality rule outperforms the best member rule may be much wider than that predicted by theory (shownin Figure 7). Even in cases where individual cooperation cost wastheoretically too high to secure the minimal quorum (three) for the
majority/plurality rule, observed frequencies of cooperators ineach group often surpassed the quorum (Laury, Walker, & Williams, 1999; Sefton & Steinberg, 1996).

General Discussion
The present research develops an explanation for the fact thathuman groups that engage in production often contain a share of

Observed frequency

140
120
100
80
60
40
20
0
0

1

2

3

4

5

6

Total number of cooperators in a group (N)Figure 10. Frequency distribution of number of cooperators (searcher/
voter hybrids) in groups in the majority/plurality rule condition across 360
observations (15 groups in 24 trials).

highly cooperative individuals who contribute to public enterprises
at a cost to themselves, such as those who prepare well forcommittee meetings, devote substantive time and energy writing a
joint grant proposal, or stay on the lookout to protect their village
from enemies/predators. It provides a principled alternative to theovergeneralized prediction of the linear model that the only rational choice is to free ride. Recall that the group-production functionin the linear model is often set arbitrarily by researchers. For
example, it is common practice in public-goods game experiments
to multiply an individual’s contribution by some arbitrary number
and then divide it by group size to determine a (constant) personal
return from the contribution, and this personal return is set to beless than the individual cooperation cost, regardless of the numberof contributors in the group (Ledyard, 1995). Although such a
linear model is a useful device for considering some theoreticalproblems concerning cooperation (e.g., effects of punishment;
Fehr & Gächter, 2000), it is a serious misconception to assume thatmost natural group-production tasks fall into this category. The
analysis of the cooperate-or-free-ride, produce-or-scrounge decisionsin terms of the Motro framework, with its plausible assumptions of amarginally diminishing returns group-production function is conceptually compelling, realistic, and supported by theoretical simulations
and a behavioral experiment. Our conceptual framework, based on theubiquity of the marginally diminishing returns group-production function common in natural settings, provides a useful benchmark explanation for realistic levels of cooperation in broad range ofsmall-group activities (McGrath, 1984; Steiner, 1972). It also
seems more convincing than alternate, somewhat contrived explanations that posit special sources of indirect utility satisfaction
such as expressive functions and utility derived from fulfilling
civic duty, though these prosocial motives may be important insome cases (Brennan & Lomasky, 1993).

KAMEDA, TSUKASAKI, HASTIE, AND BERG

90

Best Member Rule condition
Observed frequencies

Observed frequencies

Majority/Plurality Rule condition
25
20
15
10
5
0

0

8

8

0

1st

8

0

2nd

3rd

25
20
15
10
5
0

8

0

1st

8

0

2nd

0

8

3rd

Block

Block

Figure 11. Observed frequencies of individual strategies in the majority/plurality rule condition and the best
member rule condition. Individual strategies could range from full defection (zero) to full cooperation (eight) ineach block of trials.

Mean per-trial individual net payoff (in Yen)

The insight that guides the present research is that group performance is not necessarily a strict social dilemma if group production is a marginally diminishing function of the number of
productive members (as it usually is) and if personal production
costs add up (as they usually do). If the marginal individual return
to contributing is greater than its cost for some members (i.e., c ⬍␦m for some m between zero and group size), the equilibrium is a
mixture of cooperation and free riding. In simpler terms, in mostgroups, it is to the individual member’s personal advantage to
produce (contribute), unless an equilibrium number of other members is already contributing. We believe that group decisionmaking tasks and many other group tasks in natural settings are ofthis type. Perhaps most surprising of all, we found that experimental human decision-making groups yielded a stable behavioral
equilibrium at a locally Pareto efficient level of public-goods
contributions, with three contributors and three free riders pergroup. Finally, we found that majoritarian group decision rules
have an adaptive value under uncertainty, and this result may
explain why majority/plurality rules are popular across the full
spectrum of human groups from hunter-gatherer or tribal societies(Boehm, 1996; Boyd & Richerson, 1985; Wilson, 1994) to modern

80
70
60
50
40
30

Majority/plurality
20

Best member
10
0
1st

2nd

3rd

Block
Figure 12. Mean per-trial individual net payoffs (in yen) in the majority/
plurality rule condition and the best member rule condition.

industrial democracies (Hastie et al., 1983; Kameda, Tindale, &Davis, 2003; Mueller, 2003).
Our results are conceptually relevant to the long-term puzzle inpolitical science about why citizens invest resources to become
well informed and to vote (e.g., Aldrich, 1997; Mueller, 2003;
Riker & Ordeshook, 1973). In an election, the cost associated withvoting is personal and seems to outweigh any individual benefit
that the act of voting can possibly yield. Why would rational
individuals bother to spend time and resources to become wellinformed and go to the polls if the chance of having an impact on
the outcome of the election is virtually zero (the voter’s paradox;
Downs, 1957)?
We should emphasize that there are several key differences
between small-group decision making and elections. First, there is
the obvious difference in the size of electorate, implying that each
vote is much more consequential in small-group decision making
than in a large-scale election. Second, most theoretical analyses ofsmall-group decision making, including our own, have posited that
common values (Feddersen & Pesendorfer, 1998) or truth (Condorcet, 1785/1994) underlie group decisions; members seek themutually most beneficial outcome via group decisions (e.g., finding the most valuable investment, finding the truth in a criminal
jury trial). In contrast, a large-scale election is a competition
among several factions (e.g., parties) with disparate ideologies andpreferences. This difference is important because, in the politicaldomain, two motives operate when each individual decides
whether to vote or to not—free riding and competition (Dhillon &Peralta, 2002; Palfrey & Rosenthal, 1983). Within each faction(e.g., supporters of Candidate A) sharing the same preference, eachindividual is personally better off free riding if a sufficient numberof individuals are already voting for the alternative. However, inthe political domain, competition is also a motive. For one’s party
to win the election, one must assemble more voters than the otherparties. This competition factor is absent in our group decisionmaking situation where members collectively play a game against
nature (Bornstein, 2003).
In this sense, it remains to be seen how exactly the two decisionsituations relate to each other, both theoretically and behaviorally.
One possible extension of our group decision-making paradigm
might be to have two teams of hunters compete for the same
resource. The team with the more accurate group decision monopolizes the resource and distributes it evenly among members on theDEMOCRACY UNDER UNCERTAINTY

winning team, as found in some political domains. How cooperative will members be in situations where the competitive andfree-riding motives conflict? Will a stable equilibrium emerge overtime? Is the majority/plurality rule better in those settings than thebest member rule? These questions are important and provide
another test of the adaptive robustness of majoritarian decision
making under uncertainty.
There are several limitations on the conclusions of this article.
Perhaps most obvious is the assumption that group members
develop individual judgments independent from each other. Although this assumption has been common in the previous theoretical work (Hastie & Kameda, 2005; Sorkin et al., 2001), future
work should relax this condition to see its impact on group decisions. Indeed, if members conform (Asch, 1956) or mimic (Hung& Plott, 2001) each other when forming individual judgments,
majoritarian aggregation could be subject to herding effects, where
erroneous information cascades across individuals to yield defective outcomes (e.g., L. R. Anderson & Holt, 1997; Banerjee, 1992;
Bikhchandani, Hirshleifer, & Welch, 1992; Kameda & Tamura,2007). Because the majority/plurality rule works under uncertainty
via its error-cancellation function (Hastie & Kameda, 2005; Hung
& Plott, 2001), independence among members is a key contributor
to its success (Surowiecki, 2004). One possible extension to address this question is to provide each individual (computer agent orhuman participant) an option to mimic another’s judgments (i.e.,free riders who skip information search can mimic other members
rather than deciding randomly, as assumed in the current model)
and to examine the performance of the majoritarian aggregation
when social learning is possible (Henrich & Boyd, 1998; Kameda& Nakanishi, 2002, 2003).
Second, our results are obviously contingent on the distributionof competence among group members. In the simulation, groupmembers’ modal judgment policies were not statistically optimal(see Equation 1) but were based on the suboptimal equal cue
weighting (Dawes, 1979) with substantive variation; members’
preferences were thus affected not only by random error in cue
perception but also by the systematic biases built into their judgment policies. However, if these modal members had even moredeviating judgment policies while the most competent member hada near-perfect judgment policy, then the parametric range where
the majority/plurality rule outperforms the best member rule would
necessarily become much narrower (Kerr et al., 1996). It remains
to be seen how and under what conditions the accuracy of modalgroup members’ preferences, relative to that of the brightest member, can be guaranteed in naturally occurring settings (Surowiecki,2004).
Third, in our simulation and experiment, group members had no
direct opportunities to coordinate their actions ex ante. However, if
members are allowed to coordinate their behaviors in advance,
they may eventually develop some social norms to produce more
equitable outcomes within a group, rather than allowing somemembers to free ride on others unilaterally (see Figure 11). For
example, such social norms may take the form of turn taking tobear the cost of cooperation equally among all members. Given thecentral importance of inequity aversion in human cooperation(Fehr & Schmidt, 1999), it will be interesting to see how such
egalitarian norms and mutual expectations (Fehr & Fischbacher,
2004; Kameda, Takezawa, Ohtsubo, & Hastie, 2010; Kerr, 1983)

91

may develop in groups working on tasks with marginally diminishing return curves.
Last, the model we have developed in this article is anevolutionary/population-level model whereby ultimate causes(Tinbergen, 1963) for cooperation in group decision making are
considered. It still remains to be seen how each individual actually
computes costs and benefits of his or her cooperation in a grouptask with a marginally diminishing return curve and especially
how individuals coordinate their behaviors. Behavioral ecologists
studying group decision making by nonhuman animals have begun
to address these questions to understand computational algorithms
for collective nest search by honey bees, collective navigation bybaboons, and so on (see the recent special issue of the Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences [Conradt & List, 2009]). Given the robustness ofthe marginally diminishing returns group-production function innature (Foster, 2004), cross-fertilization between human and nonhuman animal researchers will be useful to delineate proximate
mechanisms underlying cooperation and coordination in groupendeavors.
We hope our methodology, starting with a description of thesituation in game theory terms, followed by computer simulation
explorations and then behavioral experiments, is appealing to otherresearchers. We also hope that the virtues of this eclectic approach
are self-evident. Social behavior is often more adaptive and morerational than it sometimes appears at first glance. Thinking about
functional, adaptive, and rational properties of a system will often
reveal a deeper structure that is not apparent to a superficial
descriptive analysis (Barkow, Cosmides, & Tooby, 1992; Gigerenzer et al., 1999; Gintis, 2007; Schelling, 1978). The wisdom of
crowds can arise from fundamental laws of social ecology thatemerge from individually adaptive strategies (Surowiecki, 2004).

References
Aldrich, J. A. (1997). When is it rational to vote? In D. Mueller (Ed.),
Perspectives on public choice: A handbook (pp. 379 –390). Cambridge,
England: Cambridge University Press.
Anderson, L. R., & Holt, C. A. (1997). Information cascades in thelaboratory. American Economic Review, 87, 847– 862.
Anderson, N. H. (1981). Foundations of information integration theory.
New York, NY: Academic Press.
Arrow, K. J. (1951). Social choice and individual values (1st ed.). New
Haven, CT: Yale University Press.
Asch, S. E. (1956). Studies of independence and conformity: A minority of
one against a unanimous majority. Psychological Monographs,
70(Whole No. 416).
Axelrod, R. (1984). The evolution of cooperation. New York, NY: Basic
Books.
Banerjee, A. V. (1992). A simple model of herd behavior. Quarterly
Journal of Economics, 107, 797– 817.
Barkow, J. H., Cosmides, L., & Tooby, J. (Eds.). (1992). The adapted
mind: Evolutionary psychology and the generation of culture. Oxford,
England: Oxford University Press.
Barnard, C. J., & Sibly, R. M. (1981). Producers and scroungers: A generalmodel and its application to captive flocks of house sparrows. Animal
Behaviour, 29, 543–555.
Bednekoff, P. (1997). Mutualism among safe, selfish sentinels: A dynamic
game. American Naturalist, 150, 373–392.
Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads,
fashion, custom, and cultural change as informational cascades. Journalof Political Economy, 100, 992–1026.

92

KAMEDA, TSUKASAKI, HASTIE, AND BERG

Boehm, C. (1996). Emergency decisions, cultural-selection mechanics, andgroup selection. Current Anthropology, 37, 763–793.
Bornstein, G. (2003). Intergroup conflict: Individual, group, and collective
interests. Personality and Social Psychology Review, 7, 129 –145.
Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionary process.
Chicago, IL: University of Chicago Press.
Brehmer, B., & Joyce, C. R. B. (Eds.). (1988). Human judgment: The SJTview. Amsterdam, the Netherlands: North-Holland.
Brennan, G., & Lomasky, N. (1993). The pure theory of electoral preference. Cambridge, England: Cambridge University Press.
Brunswik, E. (1956). Perception and the representative design of psychological experiments (2nd ed.). Berkley, CA: University of California
Press.
Camerer, C. F. (2003). Behavioral game theory. Princeton, NJ: Princeton
University Press.
Campbell, D. T. (1988). A general “selection theory,” as implemented inbiological evolution and social belief-transmission-with-modification inscience. Biology and Philosophy, 3, 171–177.
Cason, T. N., Saijo, T., & Yamato, T. (2002). Voluntary participation andspite in public good provision experiments: An international comparison. Experimental Economics, 5, 133–153.
Cason, T. N., Saijo, T., Yamato, T., & Yokotani, K. (2004). Nonexcludable public goods experiments. Games and Economic Behavior,
49, 81–102.
Clemen, R. T., & Winkler, R. L. (1999). Combining probability distributions from experts in risk analysis. Risk Analysis, 19, 187–203.
Condorcet, M. (1994). Essay on the application of probability analyses todecisions returned by a plurality of people. In I. McLean & F. Hewitt(Eds. & Trans.), Condorcet: Foundations of social choice and politicaltheory (pp. 11–36). Brookfield, VT: Edward Elgar. (Original work
published 1785)
Conradt, L., & List, C. (Eds.). (2009). Group decision making in humans
and animals [Special issue]. Philosophical Transactions of the Royal
Society of London, Series B: Biological Sciences, 364(1518).
Cooksey, R. W. (1996). Judgment analysis: Theory, methods, and applications. San Diego, CA: Academic Press.
Dawes, R. M. (1979). The robust beauty of improper linear models.
American Psychologist, 34, 571–582.
Dawes, R. M. (1980). Social dilemmas. Annual Review of Psychology, 31,
169 –193.
Dhillon, A., & Peralta, S. (2002). Economic theories of voter turnout.
Economic Journal, 112, 332–352.
Downs, A. (1957). An economic theory of democracy. New York, NY:
Harper & Row.
Feddersen, T., & Pesendorfer, W. (1998). Convicting the innocent: The
inferiority of unanimous jury verdicts under strategic voting. American
Political Science Review, 92, 23–36.
Fehr, E., & Fischbacher, U. (2003, October 23). The nature of humanaltruism. Nature, 425, 785–791.
Fehr, E., & Fischbacher, U. (2004). Social norms and human cooperation.
Trends in Cognitive Sciences, 8, 185–190.
Fehr, E., & Gächter, S. (2000). Cooperation and punishment in public
goods experiments. American Economic Review, 90, 984 –994.
Fehr, E., & Gächter, S. (2002, January 10). Altruistic punishment in
humans. Nature, 415, 137–140.
Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, andcooperation. Quarterly Journal of Economics, 114, 817– 868.
Foster, K. R. (2004). Diminishing returns in social evolution: The not-sotragic commons. Journal of Evolutionary Biology, 17, 1058 –1072.
Gigerenzer, G., Todd, P. M., & the ABC Research Group. (1999). Simple
heuristics that make us smart. New York, NY: Oxford University Press.
Gintis, H. (2000). Game theory evolving. Princeton, NJ: Princeton University Press.

Gintis, H. (2007). A framework for the unification of the behavioral
sciences. Behavioral and Brain Sciences, 30, 1– 61.
Giraldeau, L.-A., & Caraco, T. (2000). Social foraging theory. Princeton,
NJ: Princeton University Press.
Hammond, K. R., & Stewart, T. R. (2001). The essential Brunswik:
Beginnings, explications, and applications. New York, NY: Oxford
University Press.
Hastie, R. (1986). Review essay: Experimental evidence on group accuracy. In B. Grofman & G. Owen (Eds.), Information pooling and groupdecision making (Vol. 2, pp. 129 –157). Greenwich, CT: JAI Press.
Hastie, R., & Kameda, T. (2005). The robust beauty of majority rules ingroup decisions. Psychological Review, 112, 494 –508.
Hastie, R., Penrod, S. D., & Pennington, N. (1983). Inside the jury.
Cambridge, MA: Harvard University Press.
Henrich, J., & Boyd, R. (1998). The evolution of conformist transmission
and the emergence of between-group differences. Evolution and Human
Behavior, 19, 215–241.
Herrmann, B., Thöni, C., & Gächter, S. (2008, March 7). Antisocial
punishment across societies. Science, 319, 1362–1367.
Hung, A. A., & Plott, C. R. (2001). Information cascades: Replication andan extension to majority rule and conformity-rewarding institutions.
American Economic Review, 91, 1508 –1520.
Ingham, A. G., Levinger, G., Graves, J., & Peckham, V. (1974). The
Ringelmann effect: Studies of group size and group performance. Journal of Experimental Social Psychology, 10, 371–384.
Janis, I. L. (1972). Victims of groupthink. Boston, MA: Houghton Mifflin.
Kameda, T., & Nakanishi, D. (2002). Cost-benefit analysis of social/
cultural learning in a non-stationary uncertain environment: An evolutionary simulation and an experiment with human subjects. Evolution
and Human Behavior, 23, 373–393.
Kameda, T., & Nakanishi, D. (2003). Does social/cultural learning increasehuman adaptability? Rogers’s question revisited. Evolution and Human
Behavior, 24, 242–260.
Kameda, T., Takezawa, M., & Hastie, R. (2003). The logic of social
sharing: An evolutionary game analysis of adaptive norm development.
Personality and Social Psychology Review, 7, 2–19.
Kameda, T., Takezawa, M., Ohtsubo, Y., & Hastie, R. (2010). Are ourminds fundamentally egalitarian? Adaptive bases of different sociocultural models about distributive justice. In M. Schaller, A. Norenzyan,
S. J. Heine, T. Yamagishi, & T. Kameda (Eds.), Evolution, culture, andthe human mind (pp. 151–163). New York, NY: Psychology Press.
Kameda, T., & Tamura, R. (2007). “To eat or not to be eaten?” Collectiverisk-monitoring in groups. Journal of Experimental Social Psychology,
43, 168 –179.
Kameda, T., & Tindale, R. S. (2006). Groups as adaptive devices: Human
docility and group aggregation mechanisms in evolutionary context. In
M. Schaller, J. Simpson, & D. Kenrick (Eds.), Evolution and social
psychology (pp. 317–341). New York, NY: Psychology Press.
Kameda, T., Tindale, R. S., & Davis, J. H. (2003). Cognitions, preferences,and social sharedness: Past, present, and future directions in groupdecision making. In S. L. Schneider & J. Shanteau (Eds.), Emerging
perspectives on judgment and decision research (pp. 215–240). Cambridge, England: Cambridge University Press.
Kenrick, D. T., Li, N. L., & Butner, J. (2003). Dynamical evolutionary
psychology: Individual decision rules and emergent social norms. Psychological Review, 110, 3–28.
Kerr, N. L. (1983). Motivation losses in small groups: A social dilemma
analysis. Journal of Personality and Social Psychology, 45, 819 – 828.
Kerr, N. L., MacCoun, R., & Kramer, G. P. (1996). Bias in judgment:
Comparing individuals and groups. Psychological Review, 103, 687–
719.
Kerr, N. L., & Tindale, R. S. (2004). Group performance and decision
making. Annual Review of Psychology, 55, 623– 655.

DEMOCRACY UNDER UNCERTAINTY
Knack, S. (1992). Civic norms, social norms and voter turn out. Rationality
and Society, 4, 133–156.
Kravitz, D. A., & Martin, B. (1986). Ringelmann rediscovered: The original article. Journal of Personality and Social Psychology, 50, 936 –941.
Larrick, R. P., & Soll, J. B. (2006). Intuitions about combining opinions:
Misappreciation of the averaging principle. Management Science, 112,
111–127.
Larson, J. R. (2009). In search of synergy in small group performance.
New York, NY: Psychology Press.
Latané, B., Williams, K., & Harkins, S. (1979). Many hands make light thework: The causes and consequences of social loafing. Journal of Personality and Social Psychology, 37, 822– 832.
Laughlin, P. R. (1980). Social combination processes of cooperative
problem-solving groups on verbal intellectual tasks. In M. Fishbein(Ed.), Progress in social psychology (Vol. 1, pp. 127–155). Hillsdale,
NJ: Erlbaum.
Laughlin, P. R. (2006). Groups perform better than the best individuals on
letters-to-numbers problems: Effects of group size. Journal of Personality and Social Psychology, 90, 644 – 651.
Laury, S. K., & Holt, C. A. (2008). Voluntary provision of public goods:
Experimental results with interior Nash equilibria. In C. R. Plott & V. L.
Smith (Eds.), Handbook of experimental economics results (Vol. 1, pp.
792– 801). New York, NY: Elsevier.
Laury, S. K., Walker, J., & Williams, A. (1999). The voluntary provisionof a pure public good with diminishing marginal returns. Public Choice,99, 139 –160.
Ledyard, J. (1995). Public goods experiments. In J. Kagel & A. Roth(Eds.), Handbook of experimental economics (pp. 111–173). Princeton,
NJ: Princeton University Press.
Lorge, I., & Solomon, H. (1955). Two models of group behavior in thesolution of eureka-type problems. Psychometrica, 20, 139 –148.
Lupia, A. (2002). Deliberation disconnected: What it takes to improve civic
competence. Law and Contemporary Problems, 65, 132–150.
Makradakis, S., & Winkler, R. L. (1983). Averages of forecasts: Someempirical results. Management Science, 29, 987–996.
Maynard Smith, J. (1982). Evolution and the theory of games. Cambridge,
England: Cambridge University Press.
McGrath, J. E. (1984). Groups: Interaction and performance. Englewood
Cliffs, NJ: Prentice-Hall.
Meehl, P. E. (1997). The selfish voter paradox and the thrown-away voteargument. American Political Science Review, 71, 11–30.
Motro, U. (1991). Co-operation and defection: Playing the field and the
ESS. Journal of Theoretical Biology, 151, 145–154.
Mueller, D. C. (2003). Public choice III. New York, NY: Cambridge
University Press.
Opp, K.-D. (2001). Why do people vote? The cognitive-illusion proposition and its test. Kyklos, 54, 355–378.

93

Palfrey, T., & Rosenthal, H. (1983). A strategic calculus of voting. Public
Choice, 41, 7–53.
Posner, E. A. (2000). Law and social norms. Cambridge, MA: Harvard
University Press.
Regenwetter, M., Grofman, B., Marley, A. A. J., & Tsetlin, I. (2006).
Behavioral social choice. Cambridge, England: Cambridge University
Press.
Regenwetter, M., Ho, M.-H., & Tsetlin, I. (2007). Sophisticated approvalvoting, ignorance priors, and plurality heuristics: A behavioral social
choice analysis in a Thurstonian framework. Psychological Review, 114,
994 –1114.
Riker, W. H., & Ordeshook, P. (1973). An introduction to positive politicaltheory. Englewood Cliffs, NJ: Prentice-Hall.
Saijo, T., & Yamato, T. (1999). A voluntary participation game with a
non-excludable public good. Journal of Economic Theory, 84, 227–242.
Schelling, T. C. (1978). Micromotives and macrobehavior. New York, NY:
Norton.
Sefton, M., & Steinberg, R. (1996). Reward structures in public goods
experiments. Journal of Public Economics, 61, 263–287.
Smith, E. R., & Conrey, F. R. (2007). Agent-based modeling: A new
approach for theory building in social psychology. Personality andSocial Psychology Review, 11, 87–104.
Smoke, W. H., & Zajonc, R. B. (1962). On the reliability of group
judgments and decisions. In J. Criswell, H. Solomon, & P. Suppes(Eds.), Mathematical methods in group processes (pp. 322–333). Stanford, CA: Stanford University Press.
Sorkin, R. D., Hays, C., & West, R. (2001). Signal-detection analysis ofgroup decision making. Psychological Review, 108, 183–203.
Steiner, I. D. (1972). Group process and productivity. New York, NY:
Academic Press.
Surowiecki, J. (2004). The wisdom of crowds. New York, NY: Doubleday.
Taylor, D. W., & Faust, W. L. (1952). Twenty questions: Efficiency inproblem solving as a function of size of group. Journal of Experimental
Psychology, 44, 360 –368.
Thompson, L. L. (2004). Making the team: A guide for managers (2nd ed.).
Upper Saddle River, NJ: Pearson Education.
Tinbergen, N. (1963). On aims and methods in ethology. Zeitschrift für
Tierpsychologie, 20, 410 – 433.
Trivers, R. L. (1971). The evolution of reciprocal altruism. Quarterly
Review of Biology, 46, 35–57.
Williams, K. D., Harkins, S. G., & Karau, S. J. (2003). Social performance.
In M. A. Hogg & J. Cooper (Eds.), Sage handbook of social psychology(pp. 327–346). London, England: Sage.
Wilson, D. S. (1994). Reintroducing group selection to the human behavior
sciences. Behavioral and Brain Sciences, 17, 585– 654.

(Appendices follow)

KAMEDA, TSUKASAKI, HASTIE, AND BERG

94

Appendix A
Implementation of the Replicator Dynamic in the Evolutionary Computer Simulation
We used the following formula to represent the selection process(Henrich & Boyd, 1998). Let us denote proportion of strategy i (⫽ 1or 2) in the population at generation t as pti and its fitness outcome as
Oti. Then, its proportion in the population at generation t ⫹ 1 ispit⫹1 ⫽

pit共W ⫹ Oit兲

where W is the baseline-fitness constant common to all strategies(we set W ⫽ 30 in the current simulation) and Wt is the averagefitness outcome:

Wt ⫽

,

Wt

冘

pit共W ⫹ Oit兲.

i

Appendix B
Estimation of the Empirical Group and Individual Payoff Functions
We describe a simple model in which the group’s aggregate
payoff as a function of the number of group members who contribute information has diminishing marginal returns. For analytic
tractability, we make standard assumptions that result in an objective function that rewards predictive accuracy by providing a fixed
reward from which the squared error of the group’s prediction is
deducted. Accurate predictions have smaller squared errors and,
consequently, higher payoffs according to this objective function.
A priori, errors can be expected but are not known until ex postvalues of all random variables are realized. It is straightforward toshow that the resulting expected payoff function is increasing in
the quantity of information but concave, implying diminishingmarginal returns to information.
Consider a group trying to predict the unknown outcome x.
Denote its forecast m. The group receives (in the aggregate) a fixed
payoff B if its forecast is perfectly accurate (i.e., m ⫽ x). The
group’s payoff, however, decreases as the group’s squared prediction error grows larger. Denoting the scaling of squared errors into
the units used in the payoff function as ␭, we can assemble thesymbols defined above to form the group’s (aggregate) gross
payoff function:
Gross group payoff ⫽ B ⫺ ␭共m ⫺ x兲 2 .
Let N denote the number of noisy pieces of information that thegroup forecast depends on, so that the functional notation m(N)describes the mapping from quantity of information into groupforecasts. Denoting the cost of information as p per unit, then the
net group payoff function becomesE关x|ε 1 , . . . , ε N兴 ⫽ ␣ ⫹ ␤ 1 共x ⫹ ε 1 兲 ⫹ . . . ⫹ ␤ N 共x ⫹ εN兲.
A simpler and more robust approach (in the absence of enoughstability in the environment to estimate the parameters in the
regression) would be the simple average冘Nm共N兲 ⫽

共x ⫹ ε i兲/N ⫽ x ⫹ 共ε 1 ⫹ ε 2 ⫹ . . . ⫹ ε N兲/N.

i⫽1

We now compute the expected value of the net group payoff
function:E关B ⫺ ␭(m ⫺ x兲 2 ⫺ pN] ⫽B ⫺ ␭E关共ε 1 ⫹ ε 2 ⫹ . . . ⫹ ε N兲 2 兴/N ⫺ pN
⫽ B ⫺ ␭␴ ε2 /N ⫺ pN.
Applying this function as the group’s expected net payoff function, we can examine the individual-level incentives for a single
member of this group who is facing a binary decision of whether
to pay p and provide one more noisy signal to be included in thegroup’s forecast or to free ride on the information acquisition ofothers. We assume, as everywhere else in this article, that grossgroup payoffs are divided evenly among all group members (cooperators and free riders alike) but that the costs of information areborne individually. Thus, if there are n other group members
cooperating in a group with M members in total, after netting out
costs for the cooperator, the individual’s expected net payoff is
Expected net individual payoff for a cooperator

Net group payoff ⫽ B ⫺ ␭共m共N兲 ⫺ x兲 ⫺ pN.
2⫽ 共1/M兲共B ⫺ ␭␴ ε2 /共n ⫹ 1兲兲 ⫺ p,

In general, the best use of N noisy signals, (x ⫹ ε1), . . . , (x ⫹ εN),is to construct the forecast m(N) as the conditional expectation ofx, which is very often assumed to take a linear form:

Expected net individual payoff for a free rider(Appendices continue)⫽ 共1/M兲共B ⫺ ␭␴ ε2 /n兲.

DEMOCRACY UNDER UNCERTAINTY

n* ⫽ ⫺0.5 ⫹ 0.5[1 ⫹ 4␭␴ε2/( pM)] 0.5 .
At integer values to the left of n*, the strategy of cooperation
maximizes individual payoffs even though some other members
are free riding. At integer values to the right of n*, free riding
maximizes individual payoffs. As intuition would suggest, n* is adecreasing function of the price of information, p, holding all else
equal. Also, n* is increasing in ␭ because higher penalties forforecasting error increase the individual rationale for contributing
information. Similarly, n* is increasing in ␴ε2 because, when eachsingle piece of information is lower in quality (i.e., less precise),
the marginal returns from additional units of information diminish
less rapidly, implying a rightward shift in the cooperator–free-rider
crossover point. Finally, holding all else equal, n* is decreasing ingroup size. The incentive to cooperate is present for fewer groupmembers, the larger the group is.
Recall that the expected aggregate net payoff function for thegroup is
Expected aggregate net payoff ⫽ B ⫺ ␭␴ ε2 /N ⫺ pN.
We refer to the total number of cooperators N that maximizes the
expected aggregate net payoff as the Pareto point and denote thisspecial value of N as N* (see Footnote 9 in the main text):
N* ⫽ 共␭␴ ε2 /p兲 0.5 .
Notice that the Pareto point is independent from the parameters B
and M. It depends positively on the penalty for forecast error andon the imprecision of private information and is decreasing in theprice of information.
This model is used to estimate the empirical group and individual payoff functions in the body of the article. The estimation
procedure is as follows. First, we estimate the gross individual
payoff conditional on the reciprocal of the total number of cooperators in each group, 1/N. The notation introduced here uses
subscripts g to index groups and t to index experimental trials.
Because gross payoffs (before netting out the cost of individual
information acquisition) are the same for all group members, thenotation here does not index individuals’ identities, although thiswould be straightforward to add. Using the definitions from above,
any individual in group g receives an individual gross payoff intrial t given by the following expression,of estimated standard errors but not the estimated coefficients.
There were only three observations in which N ⫽ 0. The model isvalid only when N ⬎ 0, and therefore, the regression coefficientswere estimated using the 1,077 observations for which N ranged
between one and six. This produced statistically significant regression coefficients and the following estimated regression line where
coefficients are rounded to the nearest integer:E关y|1/N兴 ⫽ a ⫹ b共1/N兲 ⫽ 60 ⫺ 9/N.
This regression line is plotted in Figure B1 together with meanvalues of y at each value of N, the total number of cooperators in
the group. It is straightforward to estimate expected individual net
payoff for a cooperator and for a free rider as a function of n, thenumber of other group members who cooperate, from Figure B1.
For a free rider, there are no costs to net out, and the grossindividual payoff in Figure B1 is identical to the net payoff, plotted
as a dashed line in Figure B2. For a free rider, the total number ofcooperators in the group is equal to the number of other groupmembers cooperating: N ⫽ n. For a cooperator, however, the total
number of cooperators includes the others plus him or herself, andtherefore, N ⫽ n ⫹ 1, plotted in Figure B2 as the solid line after
subtracting 6 yen for the cost of contributing to the public good.
The figure shows that the two payoff curves cross strictly to theleft of n ⫽ 1, where n represents the number of other groupmembers who cooperate. This implies that cooperation maximizes
individual payoffs when no other group members cooperate (atn ⫽ 0) and that free riding maximizes individual payoffs as soon
as there is at least one other group member cooperating (when n ⬎0). Thus, any Nash equilibrium is a profile of binary actions inwhich there is one cooperator and five free riders.
65

Mean individual gross payoffs

The point at which the individual payoff curves intersect defines
the so-called Nash point (see Footnote 9 in the main text):

95

60

55

50

45

40
0

1

2

3

4

5

6

Total number of group members who contribute (N)Individual’s gross payoff ⫽ E关ygt兩1/Ngt兴
⫽ 共1/M兲共B ⫺ ␭␴ ε2 /Ngt兲,
where g ranges from one to 15 and t ranges from one to 72, for a
total of 15 ⫻ 72 ⫽ 1,080 observations that are obviously notstatistically independent. Because the same group is observed 72
times, the statistical model allows for within-group correlation of
the 72 error terms in the regression model, which affects the size

Figure B1. Fitted regression line for individual gross payoffs (and observed means) as a function of N, the total number of cooperators in thegroup. The regression model is E[individual gross payoff|1/N] ⫽ a ⫹b(1/N) ⫽ 60 ⫺ 9/N, for 1 ⱕ N ⱕ 6. The mean of three payoff observations
for N ⫽ 0 is plotted in the figure as well, although it is not derived fromthe regression model with the restricted range of N. The model was fit with
1,077 observations, and the coefficients were statistically significant at the
5% level.

(Appendices continue)

KAMEDA, TSUKASAKI, HASTIE, AND BERG

Expected net individual payoffs

Figure B3 shows the empirical net aggregate payoff for groups
as a function of the total number of cooperators. The points on thiscurve are empirical averages over 1,080 observations of group
payoffs and numbers of cooperators. The curve has a local maximum at three and a global maximum at six. Thus, three is a local
Pareto point, although a benevolent dictator aiming to maximize
aggregate payoffs would choose the global Pareto point, six.
In linear public-goods games, the dominant strategy is usually tocontribute zero and, if not zero, then the other boundary point of
the action space (i.e., contributing the maximum amount). In
contrast, nonlinear public-goods payoff structures can generate
Nash equilibria on the strict interior of the agents’ action spaces.
Our model’s Nash equilibrium requires one contributor per group,
which falls short of the socially efficient Pareto points of three(local) and six (global) contributors per group. As shown in Figure 10 in the main text, the data revealed a pronounced mode atthree and showed that the groups we observed regularly achieved

340

Empirical net aggregate payoffs

96

330
320
310
300
290
280
270
260
250
0

1

2

3

4

5

6

Total number of group members who contribute (N)Figure B3. Empirical net aggregate payoff for groups as a function of N,
the total number of cooperators in the group (summing over six groupmembers’ individual payoffs after netting out costs for cooperation). The
curve has a local maximum at three and a global maximum at six. Thus,
three is a local Pareto point, although a benevolent dictator aiming tomaximize aggregate payoffs would choose the global Pareto point, six.

60
58
56
54
52
50
48
Cooperator

46
44
42

Free-rider

40
0

1

2

3

4

5

larger aggregate payoffs than the Nash equilibrium would predict. We
found that groups stably functioned at a locally Pareto efficient levelof public-goods contributions, with three contributors and three freeriders per group. The unambiguous modal value is remarkable, in thatparticipants found a locally best social outcome as a group that
required asymmetric action profiles among group members, without
any mechanism to coordinate action. The participants in our study
earned more by cooperating more than is predicted from the individual incentives built into the game structure.

Number of other group members who cooperate (n)Figure B2. Estimated net individual payoffs for cooperators (solid line)
and free riders (dotted line) as a function of the number of other cooperators in the group.

Received January 25, 2010
Revision received June 8, 2010
Accepted June 14, 2010 䡲

Correction to Purcell et al. (2010)
In the article “Neurally Constrained Modeling of Perceptual Decision Making” by Braden A.
Purcell, Richard P. Heitz, Jeremiah Y. Cohen, Jeffrey D. Schall, Gordon D. Logan, and Thomas J.
Palmeri (Psychological Review, 2010, Vol. 117, No. 4, pp. 1113–1143), two grant numbers wereincorrectly cited in the author note, and one correct grant number was omitted. The author note
incorrectly indicated that research was supported by grants RO1-MH55806 and RO1-EY13358. In
addition, grant R01-EY08890 should have been listed.
DOI: 10.1037/a0021906