Journal of Experimental Social Psychology 43 (2007) 168–179
www.elsevier.com/locate/jesp‘‘To eat or not to be eaten?’’ Collective risk-monitoring in groups q
Tatsuya Kameda *, Ryo Tamura
The Center for the Study of Cultural and Ecological Foundations of the Mind, Department of Behavioral Science, Hokkaido University, Japan
Received 3 November 2004; revised 2 September 2005
Available online 29 March 2006
Communicated by Stasser

Abstract
The importance of risk-monitoring has been increasing in many key aspects of our modern lives. This paper examines how individuals
monitor such risks collectively by extending a behavioral ecological model of animal foraging to human groups. Just as animals must
forage for food under predatory risk, humans must divide valuable material and psychological resources between foraging activity
and risk-monitoring activity. We predicted that game-theoretic aspects of the group situation complicate such a trade-oﬀ decision inresource allocation, eventually yielding a mixed equilibrium in a group. When the equilibrium is reached, only a subset of members engage in the risk-monitoring activity while others free-ride, concentrating mainly on their own foraging activity. Laboratory groups engaging
in foraging under moderate risk provided a support to this prediction. When the risk-level was set higher, however, ‘‘herding behavior’’(conforming to the dominant behavior) interfered with the emergence of equilibrium. Implications for risk management are discussed.
 2006 Elsevier Inc. All rights reserved.
Keywords: Risk-monitoring; Foraging; Cooperation; Free-riding; Producer-scrounger game; Uncertainty; Information cascade; Behavioral ecology

Introduction
Most modern risks are collective in nature, aﬀecting
many people’s lives simultaneously. Fragile ﬁnancial markets, moral hazards in international business, and pollution
by toxic substances are all examples of risks that aﬀect
many people at the same time. Anecdotes abound that
insuﬃcient monitoring of these collective risks can cause
serious, sometimes unrecoverable damages to a large
human population (Reason, 1997; Slovic, 1987, 1999). On
the other hand, despite the importance of the problem tomodern societies, few psychological studies have addressedqThis research was supported by the Grant-in-Aid for ScientiﬁcResearch 17330133 from the Ministry of Education, Culture, Sports,
Science and Technology of Japan to the ﬁrst author. We are grateful to
Reid Hastie, R. Scott Tindale, Paul Wehr, Robin M. Cooper, Andreas
Wilke, Keiko Ishii, Taiki Takahashi, Takafumi Tsukasaki, Daisuke
Nakama, and Yoshihisa Nakayama for their helpful comments on an
earlier version of the manuscript.
*
Corresponding author. Fax: +81 11 706 3066.
E-mail address: tkameda@let.hokudai.ac.jp (T. Kameda).
0022-1031/$ - see front matter  2006 Elsevier Inc. All rights reserved.
doi:10.1016/j.jesp.2006.02.003

how such risks are monitored collectively. In this paper, we
examine how people monitor collective risks in a group setting, focusing on the potential free-rider problem in riskmonitoring. Our theoretical perspective is adaptationist
or game-theoretic (cf. Hastie & Kameda, 2005; Kameda& Hastie, 2004; Kameda, Takezawa, & Hastie, 2003,
2005). Speciﬁcally, we extend a behavioral ecological modelof animal vigilance and foraging to human groups underrisk, and test predictions derived from the game-theoretic
model via an interactive, laboratory experiment.
Risk-monitoring as a key element in modern societies
Studies of risk in psychology have been developed on
several major themes. One central theme concerns the elaboration of the notion of risk in judgment and decisionmaking (Kahneman & Tversky, 1979; see Dawes, 1998;
Hastie, 2000 for reviews). Researchers in this ﬁeld have formally reﬁned the notion of risk, yielding important empirical results about its functioning in individual and groupdecision-making (e.g., Davis, Kameda, & Stasson, 1992;

T. Kameda, R. Tamura / Journal of Experimental Social Psychology 43 (2007) 168–179

Kameda & Davis, 1990; Loewenstein, Weber, Hsee, &Welch, 2001). Another key theme is concerned with risk
perception and communication (Fischhoﬀ, Lichtenstein,
Slovic, Derby, & Keeney, 1981; Slovic, 1987). Researchers
have identiﬁed various factors aﬀecting people’s perceptionof risk, and have examined the ways to facilitate eﬀective
communication between experts and lay people about technological or policy issues involving risk (Fischhoﬀ, 1995;
Slovic, 1999).
Along with other applied work on health risks (cf.
Adler, Kegeles, & Genevro, 1992), these developments
have greatly contributed to our understanding of humanbehavior involving various risks. Yet, there seems to be
one glaring omission in previous psychological research,
despite its theoretical and practical signiﬁcance—the studyof risk-monitoring. An episode in international business
may help to illustrate the increasing importance of riskmonitoring in modern societies. In February 1995, Britain’s Barings bank, the oldest merchant bank in the country, collapsed due to the actions of a single trader based ata small oﬃce in Singapore. In just 3 years, the trader, Nick
Leeson, caused a huge, irrevocable loss to the entire group,
amounting to nearly 870,000,000 GBP through a series ofunauthorized trades involving ‘‘error accounts.’’ The mostproblematic aspect of this episode was that the trader’sharmful actions were largely unmonitored. Even thoughsome of the auditors at the Barings Group reported suspicious activity, concerns were largely unheeded at the headoﬃce in London, which was occupied with ‘‘other moreurgent business’’; they failed to systematically redirect theirresources so as to monitor the potential risk at the Singapore branch (for details, see <http://www.riskglossary.com/articles/barings_debacle.htm/>, and Reason,1997).
As illustrated in this example, insuﬃcient risk-monitoring in a group can cause serious consequences, involving
the demise of an entire group or population. Toward a better understanding of risk-monitoring behavior, we
approach this issue from an adaptationist perspective (cf.
Kameda & Hastie, 2004; Kameda & Tindale, 2004), exploring the applicability of a behavioral ecological model ofanimal vigilance to human groups.
Behavioral ecological models of animal vigilance
Although we tend to think of ‘‘risk’’ in humanistic
terms, the notion applies to the entire animal kingdom.
Recently, scholars have made attempts to link theories ofrisk developed in the social sciences (applied mostly tohumans) to theories developed in behavioral ecology(Weber, Shaﬁr, & Blais, 2004).
Behavioral ecology has yielded sophisticated models andempirical data concerning risk-monitoring in the animal
kingdom (cf. Krebs & Davies, 1993, 1997). According tothese models, the lives of many animal species are divided
between foraging for food and avoiding predation by otheranimals. These two activities are often mutually exclusive—

169

extra eﬀort in one reduces the eﬀort available to the other.
Therefore, when an animal forages for food, it must divide
its time and attention between feeding and being vigilant
for predators. Notice that, as illustrated in the demise of
the Barings Group, humans in modern, as well as in primordial societies, constantly face the same adaptive challenge, to strike a balance between foraging/intake activity
and risk-monitoring.
The behavioral ecology literature suggests that many
animals’ behavior under such a trade-oﬀ may be approximated by a cost-beneﬁt model (Lima, Valone, & Caraco,
1985; Milinski & Heller, 1978). Laboratory experiments
and ﬁeld observations of many species (some rodents andbirds, for example) suggest that, if the animals live solitary
lives, individual optimization models essentially approximate their allocation decisions. The times allotted for being
vigilant and feeding yield approximately a maximum jointﬁtness to the individuals most of the time (see Houston,
McNamara, & Hutchinson, 1993, for general results about
the trade-oﬀ between gaining energy and avoiding
predation).
On the other hand, game-theoretic aspects complicate
allocation decisions for social species (Pulliam, Pyke, &Caraco, 1982). Often, animals that forage together canenjoy ‘‘aggregation economies’’, or beneﬁts associated withgrouping that are unavailable to solitary foragers. In agroup, there are many more eyes to watch for predators,
allowing each animal to devote a relatively greater proportion of their time to foraging for food. However, it is exactly these features that yield an incentive for free-riding—If
there are already a suﬃcient number of watchers engaged,
why should not one choose to forego vigilance and forage
exclusively. Giraldeau and Caraco (2000) named such aninterdependent structure (including the vigilance-foraging
situation) a ‘‘producer-scrounger’’ game. In the producerscrounger game, if there are many ‘‘producers’’ of public(or collective) goods that are beneﬁcial to others as wellas oneself (e.g., monitoring for predators), each individualis better oﬀ exploiting the eﬀorts of others (e.g., eating
100% of the time). However, if there are too many‘‘scroungers’’ on another’s monitoring eﬀorts, each individual is better oﬀ switching to producing. If no one serves asa watcher, the gain from one’s own risk-monitoring
exceeds its cost; under these circumstances, reducing the
likelihood of predation is a better option than eating.
Notice that, in contrast to the social dilemma game
(Dawes, 1980), defection is not a dominant strategy in the
producer-scrounger game. The net beneﬁt of one strategy
is not ﬁxed (i.e., neither strategy is dominant), but depends
on the frequency of the alternative strategy within thegroup; too many players opting for one strategy simultaneously reduces its proﬁtability while increasing that ofits alternative, providing an incentive for individuals toswitch. Since the two strategies are mutually constrainedin terms of proﬁtability, we can expect a mixed Nash equilibrium to eventually emerge (Gintis, 2000; Maynard
Smith, 1982). At equilibrium, the group reaches a stable

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state in which producers and scroungers coexist. In a context of foraging under risk, the group is composed of two
types of individuals in a stable manner, those who engage
mainly in risk-monitoring at the expense of foraging, andthose who exploit the monitoring-eﬀorts of others and primarily concentrate on foraging.
Applicability of the behavioral ecological model to collectiverisk-monitoring in humans
Although the topic of risk-monitoring is conceptuallyimportant to the social sciences (see Reason, 1997, for a
detailed analysis of organizational accidents accruing from
insuﬃcient risk-monitoring), there has been almost no
attempt to apply behavioral ecological models to collective
vigilance in humans. Possible exceptions we are aware ofinclude ﬁeld studies by Wirtz and Wawra (1986), Wawra(1988), Dunbar, Cornah, Daly, and Bowyear (2002). Thesebehavioral ecologists studied people’s vigilance in contemporary city environments including a large refectory-stylecafeteria and an open park. Although interesting and innovative, these studies do not provide data pertaining tohuman risk-monitoring speciﬁcally. Instead, their treatment of vigilance consisted of individuals’ ‘‘lookingaround’’ in general and was not limited to risk avoidanceas we conceive of it—for instance, both mate searching
and guarding were included in vigilant behavior. (See also
Treves, 2000, for a review of vigilance behavior, viz., looking-around, by non-human primates.)
Given the novelty of the adaptationist approach in social
psychology (cf. Kameda & Hastie, 2004; Kameda & Tindale, 2004), some considerations may be in order about its
applicability to human groups. More speciﬁcally, how relevant are the notions of behavioral ecology to explain humanbehavior? Of course, this is ultimately an empirical questionthat can only be answered by programmatic research. However, we should emphasize that the adaptationist perspective, as well as sophisticated formal models derived fromthis approach (e.g., evolutionary games: Gintis, 2000; Maynard Smith, 1982), have been vitally important to synthesize
an understanding of animal behavior across multiple taxa
(Alcock, 2001). Although still few in social psychology,
recent attempts have successfully extended its scope tohuman social behavior (e.g., Barkow, Cosmides, & Tooby,
1992; Buss, 1989; Daly & Wilson, 1988), including our own
work on the development of social norms under uncertainty(Kameda et al., 2003, Kameda, Takezawa, & Hastie, 2005),‘‘fast and frugal’’ decision heuristics in groups (Hastie &Kameda, 2005), and the function of social/cultural learning
in a non-stationary, uncertain environment (Kameda &Nakanishi, 2002, 2003).
Our hypotheses
Extending these developments, this paper explores collective risk-monitoring in humans from the adaptationist perspective. For this purpose, we created a laboratory test-bed

approximating the foraging-vigilance trade-oﬀ. In the laboratory, participants engaged in a ‘‘foraging’’ task (earningmoney through a simple calculation task) in groups of six
under a common potential danger. While performing thetask, participants could observe how others were allocating
their time between foraging and monitoring of the common
risk. If the simulation is an adequate approximation of the
producer-scrounger game (Giraldeau & Caraco, 2000), andassuming that participants respond to the game structure
rationally, we expected a mixed equilibrium to emerge overtime in the group. Thus, our ﬁrst hypothesis is as follows:
Hypothesis 1. Over time, collective risk-monitoring in agroup will approach a stable mixed equilibrium in which a
subset of members will engage in vigilant behavior (monitoring for the common risk) and the remaining groupmembers will behave as scroungers (free-riders), concentrating only on the foraging activity.
Notice that this game-theoretic prediction assumes
rational actors, unaﬀected by ‘‘irrational’’ psychologicalforces. However, social psychological factors may interferewith the emergence of equilibrium. Most notably, if conformity (Asch, 1956; Boyd & Richerson, 1985; Kameda, Tindale, & Davis, 2003) operates under uncertainty, thenmembers may tend to follow the behavior of others. If everyone follows another’s move, we would expect homogeneousgroups consisting entirely of either watchers or foragers to bea frequent occurrence, instead of the mixed groups. Interestingly, economists started to model and empirically examinesuch herding behavior under uncertainty (Anderson & Holt,
1997; Banerjee, 1992; Bikhchandani, Hirshleifer, & Welch,1992). Rational calculation, they argue, should lead to ‘‘information cascades’’ whereby people subsequently conformtheir behavior while simultaneously neglecting their own
perception of the environment.
For example, Hung and Plott (2001) created informationcascades in a Bayesian judgment task. In this study, theexperimenter picked up one of two urns (e.g., one with 70
white and 30 red balls; the other with 30 white and 70 redballs) and then provided each participant an opportunity
to draw privately one random sample from the chosen urn
with replacement. Participants were selected in sequence
and asked to announce publicly as to their judgment of thesource urn from which the sample had been drawn. When
early respondents in a sequential judgment task favored
one urn, subsequent respondents were more likely to ignore
the information gained by their private sample and to conform to the preceding publicly-declared decisions. Ironically,a Bayesian analysis shows that this is exactly what people
should do—ignore their own private information and conform to the prior members’ opinions. Thus, their task has
the insidious property of producing rational (but erroneous)
information cascades when leading members have drawn
misleading samples, a condition that occurs on approximately one-quarter of the trials in Hung and Plott’s experiment.
The information cascade can be seen as ‘‘rational panic’’, derived from the basic fact that conformity often leads

T. Kameda, R. Tamura / Journal of Experimental Social Psychology 43 (2007) 168–179

to statistically correct inferences under uncertainty (seeHenrich & Boyd, 1998; Kameda & Nakanishi, 2002 foran evolutionary game analysis of conformity from this perspective). We conjecture that collective risk-monitoring
under noisy environments may have some parallels with
the Bayesian statistical-inference situation as describedabove. Since risk can only be estimated (with error) under
uncertainty, escalation of vigilant behavior by one’s neighbors may imply that danger is imminent, just as multiple
sensors simultaneously reporting seismic activity suggest
an impending volcanic eruption (cf. Hastie & Kameda,
2005). In other words, people may use the frequency ofhigh vigilant behavior in the group as a statistical cue toinfer the imminence of risk, which leads to herding behavior at the group level. Thus, we have the following alternative hypothesis:
Hypothesis 2. Herding behavior will characterize people’s
collective risk-monitoring under uncertainty. Rather thanapproaching a stable mixed equilibrium where only asubset of members engage in vigilance, the group willﬂuctuate frequently between all vigilance and no vigilance.
In the following, we report an interactive, group experiment designed to test these hypotheses. It is possible thatthe validity of each of the two proposed hypotheses
depends on the level of risk present in the foraging environment, which may mediate the strategy used to monitor
common risk. To explore boundary conditions for hypotheses one and two, we created two conditions diﬀering in
risk-level.
Methods
ParticipantsParticipants were 180 undergraduate students (133males and 47 females) enrolled in introductory psychology
classes at Hokkaido University in Japan.
Overview
We composed random six-person groups and implemented a foraging-under-risk situation in a laboratory connected through a local area network (LAN). As alaboratory foraging task, each participant was asked tosolve individually as many simple calculation problems as
possible during each trial. Participants accumulated 10
yen (about 10 cents) towards their experimental reward
for each correct solution. While performing the calculationtask, a common danger could arise in each trial. If an individual failed to avoid the danger, 30 yen (low-risk condition) or 50 yen (high-risk condition) was subtracted from
his/her accumulated reward. Given average trial length, apenalty of 30-yen was suﬃcient to wipe out all earnings
in the current trial plus a portion of previous earnings; apenalty of 50-yen might wipe out all earnings from the previous two trials. An individual could increase the likelihood

171

of avoiding the danger by elevating his/her vigilance level.
Choosing to do so, however, meant that the individual
must sacriﬁce his/her foraging activity. Hence, a tradeoﬀ
between the two survival activities of risk-monitoring andforaging was simulated in the laboratory. The potential
danger was common in that it aﬀected all six individuals
simultaneously. If one or more group members detected
the danger, their subsequent evasive behavior could alert
remaining members who were initially unaware of its imminence, thus aﬀording them the opportunity to escape as
well, time permitting. Thus, acting as a watcher and maintaining a high level of vigilance could, on average, help
other members who were concentrating on solving the
problems for their own beneﬁt. Hence, a free-rider problem
was simulated in the laboratory with respect to monitoring
the common danger. There were 60 trials in total.
ProcedureSix participants were randomly assigned to one of two
experimental conditions (low- or high-risk), each lasting
approximately one hour. There were 15 groups (90 participants) in each condition.
Upon arrival, each participant was seated in a private
cubicle and received further instructions individually via
computer. Foraging under risk was explained, and the participants were informed that their reward would be contingent upon their performance in the experiment. The
foraging task was a series of simple calculations (additionof two three-digit numbers). Participants earned 10 yen
by typing in a correct answer for each problem appearing
on the computer screen. The duration of each trial was
determined randomly (M = 30, SD = 10 s). Trials ended
when either the time had elapsed or after the common danger arose.
Monitoring the danger
The probability of the common danger occurring during a
trial was 0.3 (unspeciﬁed to the participants). Two sources of
information regarding the current risk-level were available
to participants during each trial: environmental and social.
This information was given to help the participant adjust
his/her vigilance level against the danger (see Fig. 1).
The environmental information alluded to the imminence of the common danger and consisted of a regularly
updated bar-chart displayed on the computer screen. The
environmental information contained random noise(Brunswik, 1956); the cue value (i.e., height of the barchart) at any given moment reﬂected the sum of the truerisk-level with a random parameter. We set the environmental cue so that participants were warned of the imminent danger (when it existed) with a 32% chance on
average. At any time during a trial, participants could elevate their vigilance level to a high alert state, thereby gaining more precise environmental information (i.e., lessnoise), and increasing the average chance of detection to64%. Higher vigilance, however, came with a price—partic-

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T. Kameda, R. Tamura / Journal of Experimental Social Psychology 43 (2007) 168–179

Detecting the danger?

Social information

Environmental information

Probability of detection was .32 (low-vigilant

How many others are actively

How imminent is the common

members) and .64 (high-vigilant members).

monitoring the common danger?

danger in the current trial?

Yes
Successful escaping within time limit?
“Escape button” was activated on the screen forNo

1 to 3 seconds after the emergence of thedanger.

DECISION

Participants had to click the button

within the time limit to escape successfully.

Continuing the calculation task (Foraging)

Yes

or

Switch to a high alert state (High vigilance)

Escape completed
After

Fig. 1. Two sources of information, environmental, and social, wereavailable for the participants to adjust their vigilance level while foraging.

ipants had to cease all foraging activity for the remainderof the trial.
Besides the environmental information, participantsalso had access to social information regarding other groupmembers’ collective risk-monitoring. Regular reports were
provided throughout the trial informing each participant
as to the number of group members who had switched tothe high alert state. Combining the two types of information, participants were able to adjust their own vigilancelevel to defend against the common danger.
Escaping from the danger
To evade the approaching danger, participants had toclick an escape button that became functional for a limited
time once the common threat materialized. The allowable
time for group members to escape was determined randomly from a uniform distribution ranging from 1- to 3-s.
Fig. 2 summarizes the implementation of this process.
As mentioned earlier, individual detection (i.e., being
warned) of the danger was probabilistic. If one or moregroup members detected the danger, their subsequent evasive behavior could alert remaining members who were initially unaware of its imminence. These members were still
able to access the escape button, but only for the time thatremained. Depending on the total length of the allowable
time, and how quickly those who detected the danger had
reacted, it was possible, though not certain, for any member to escape the danger simply by reacting to others in
the group.1

being

alertedtoanother’s escape

Successful escape within
the remaining time?
The“escapebutton”

wasavailable only for the time left.

Yes
Escape completed
Fig. 2. Steps required for each individual to escape from the danger.

Performance feedback
After every ﬁve trials, participants received a summary
table listing each member’s cumulative reward, including
their own. However, because the other members were
unidentiﬁable in the table, it was impossible to associate
an individual’s alert history with his/her earnings. This
feedback allowed participants to gauge the eﬀectivenessof their strategy in terms of their relative performance within the group. The reward (in yen) for each participant wascalculated as follows:½10  ð# SolutionsÞ  ½Penalty  ð# failures to escapeÞ.
Penalties in the low- and high-risk conditions were 30 and
50 yen respectively. There were 60 trials in total (18 ofwhich the common danger arose). After completing theexperiment, participants were paid and dismissed.
Results

1
The presence of high-vigilant members was beneﬁcial to the group in
two ways. First, high-vigilant members detected the common danger more
eﬀectively (.64 versus .32), thus increasing the overall probability ofescape. Second, because it was impossible to forage in the high alert state,high vigilant members’ reaction to the danger alert should have been
relatively rapid, aﬀording low vigilant members more time to reply with
their own escape attempt. Therefore, with the provisioning of a public
beneﬁt comes the impetus for free-riding.

On average, participants earned 1130 yen (about 11 US
dollars) for a one-hour experiment (SD = 499 yen). This
amount was about twice that of the average hourly wage
undergraduate students typically earn working a part time
job. Post-session interviews suggested that the participants
were motivated to maximize their monetary rewards fromthe experiment. In the sections that follow, we ﬁrst provide

T. Kameda, R. Tamura / Journal of Experimental Social Psychology 43 (2007) 168–179

an overview on the participants’ average vigilance rates andthe mean amount of reward they received. We then reportdetailed statistical analyses testing whether those collective
patterns better reﬂect an emergence of a stable mixed equilibrium (Hypothesis 1) or herding behavior under risk
(Hypothesis 2).
Overview: Mean vigilance rates and rewards
Average vigilance-rates
For this analysis, we divided the 60 trials into three
20-trial-blocks. Fig. 3 presents the average proportion ofparticipants in each group who switched to a high alert(vigilant) state.
Not surprisingly, participants were more vigilant in thehigh-risk condition than in the low-risk condition, elevating their risk-monitoring in proportion to penalty incurred
for failure to avoid the common danger. When tested in a 2(condition) · 3 (block) repeated-measures Analysis of variance (ANOVA), the main eﬀect for condition was signiﬁcant [F (1, 28) = 6.15, p < .05]. There was also a maineﬀect for block [F (2, 56) = 24.49, p < .001], indicating thatvigilant activity declined over time. Given that the risk level
was held constant (at 0.3) across the 60 trials, results suggest that the participants adjusted their behavioral strategies of foraging and vigilance over time.
Average reward
To see the eﬀects of such behavioral adjustments on ouroperationalization of ﬁtness (monetary outcome), we
examined the average reward earned by the participants
across the three trial blocks and discovered that the mean
reward increased over time. Collapsed across the lowand high-risk conditions, the mean reward was 340 yen
in the ﬁrst block, 357 yen in the second block, and 433
yen in the third block [F (2, 56) = 43.62, p < .001]. This

increase suggests that the participants came to balance
the two key survival activities (foraging and vigilance)
under risk more eﬃciently over time. Not surprisingly,
there was also a main eﬀect for condition[F (1, 28) = 22.11, p < .001], indicating that the mean
reward was higher when the penalty for failing to detect
the common danger was lower—1327 yen in the low-risk
condition compared to 933 yen in the high-risk condition.
More important to our inquiry was whether the foraging
under risk situation in this study indeed had the incentive
structure of a producer-scrounger game (Giraldeau & Caraco, 2000). Recall that, in a producer-scrounger game, payoﬀ to each strategy is not ﬁxed, but instead depends on thefrequency of the other strategy in the group. Individuals
are better oﬀ exploiting the eﬀorts of a suﬃcient numberof producers, but should switch to producing public goods
themselves if the number of producers is too few.
Is this condition adequately satisﬁed in the current
experiment? Fig. 4 displays the average individual payoﬀ
to a watcher and a forager each trial, as a function of thenumber of other watchers engaged in high vigilance.2
As can be seen in Fig. 4, in both the low-risk and highrisk conditions, watching was more beneﬁcial than foraging when there were too many foragers in a group (seethe left side of the ﬁgure where the number of other watchers was small). Foraging was more beneﬁcial, however,when many others were watching. Thus, our experimental
setting seems consistent with a producer-scrounger situation. As expected, the net beneﬁt of one strategy (watchingor foraging) depended on the frequency of the other strategy in a group; if there were too many players with one
strategy, each participant was better oﬀ switching to adopt
the other strategy.
Emergence of a stable mixed equilibrium over time (test of
Hypothesis 1)
As we saw in Fig. 3, the participants’ use of vigilance
became better adjusted over time in terms of the monetary
outcome that was earned. The central question in the present study was to see whether these collective patterns reﬂect
the emergence of a mixed equilibrium in the group

0.5
0.45

Mean proportions of participatns
switcing to a high alert state

173

0.4
0.35
0.3

2

0.25
0.2
0.15

High Risk
Low Risk

0.1
0.05
0

1st

2nd

3rd

Block
Fig. 3. Mean proportion of individuals in a group switching to the highvigilance state.

This was calculated as follows. For each trial, we examined occurrences of 7 possible group-compositions. A group composition could be(0, 6), (1, 5), (2, 4), (3, 3), (4, 2), (5, 1), or (6, 0), where (n, 6-n) refers to agroup composed of n watchers and 6-n foragers in a trial. Collapsing
across all 15 groups in the low-risk and high-risk conditions, respectively,
we then calculated the average reward that watchers and foragers earnedin each of the 7 group compositions. The means depicted in Fig. 4 weretaken from these calculations. For example, the mean payoﬀ to a watcherwhen no other group member was engaging in high vigilance corresponds
to the average reward for watchers in the trials with (1, 5) composition,and the mean payoﬀ to a forager when no other group member was
engaging in high vigilance corresponds to the average reward for foragers
in the trials with (0, 6) composition; this comparison allowed us to see
which strategy was more beneﬁcial for an individual, to watch or toforage, when no other member was engaging in high vigilance in a trial.

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things to happen: ﬁrst, the proportion of watchers observedin each group should become less variable in later blocks;
second, a division of roles may emerge consisting of hardcore free-riders who concentrate on foraging and altruistic
watchers who commit to risk detection. We will examine
these points in turn.
Variability of vigilance-rates in each group(Hypothesis 1) or are governed by herding behavior(Hypothesis 2).
We have already conﬁrmed that the foraging task had
an incentive structure consistent with a producer-scrounger
game (Giraldeau & Caraco, 2000). Furthermore, Fig. 4
also demonstrates that the payoﬀ functions for watching
and foraging intersect at the point 1.1 in the low-risk condition, and 1.8 in the high-risk condition. This means thatan individual is better oﬀ switching to high vigilance if only
one other member is watching in a group, but should continue foraging if two other members are watching. Thus,
the game-theoretic equilibrium demands that approximately two members in total be monitoring risk during a given
trial. The observed mean rates for watching in the third
block (.26 in the low-risk condition and .37 in the high-risk
condition) were close to the equilibrium proportion(.33 = 2/6) approximated from Fig. 4.
Although these results are in line with the notion of an
equilibrium, closer examination is needed to test Hypothesis 1. Given the temporal nature of this hypothesis, we need
to focus on changes in participants’ behavior over time.
Speciﬁcally, if the collective vigilance level approaches astable mixed equilibrium over time, we might expect two

0.16

Mean Variances in the Watcher
Proportions

Fig. 4. Mean individual payoﬀ to a watcher and a forager each trial, as afunction of how many others were engaging in high vigilance during a trial.

Within-group variance associated with the proportion ofwatchers should decrease over time as the proportion itself
approaches equilibrium. For instance, the proportion ofvigilant individuals within each six-person group may behighly variable across early trials (e.g., 80% watchers inthe ﬁrst trial, 20% in the second trial, etc.), but should
remain relatively consistent across later trials. To determine if this was true, we calculated the variance associatedwith the proportion of watchers for each group across thethree successive blocks of 20 trials. Fig. 5 displays themeans for the within-group variances of each condition.
In line with Hypothesis 1, ﬂuctuation of the watcherrate in each group decreased over time. A 2 (condition) · 3(block) repeated-measures ANOVA yielded a signiﬁcantmain eﬀect for block [F (2, 56) = 12.09, p < .01], and a maineﬀect for condition [F (1, 28) = 5.78, p < .05]. However, theinteraction eﬀect was also signiﬁcant [F (2, 56) = 4.71,
p < .05] indicating that the main eﬀect for block depended
on the risk level of the situation. We thus conducted a separate one-way ANOVA for each condition. Using theomnibus error term, the block factor was highly signiﬁcantin the low-risk condition [F (2, 56) = 15.64, p < .001], but
not in the high-risk condition [F (2, 56) = 1.16, ns]. Thus,
consistent with the equilibrium hypothesis, the vigilance
rate became more stabilized over time in the low-risk condition. Conversely, the corresponding decrease in the highrisk condition (from .14 in block one to .13 in block three)
was statistically non-signiﬁcant; the vigilance rate remained
equally variable across the blocks.

0.14
0.12
0.10
0.08
0.06

High Risk
Low Risk

0.04
0.02
0.00

1st

2nd
Block

3rd

Fig. 5. Mean within-group variances in the proportion of vigilant
individuals. If a producer-scrounger equilibrium is emerging, variances
should become smaller over time.

T. Kameda, R. Tamura / Journal of Experimental Social Psychology 43 (2007) 168–179

Emergence of role-division over time175

Low-Risk Condition

30

Free riders with no vigilance
25
20
15
10
5
0

0

5

10

1st

0

5 10 15

0

2nd

5

10 15

3rd

Block
High-Risk Condition

35

Observed Frequencies

Theoretically, a mixed equilibrium can be monomorphic
or polymorphic in nature, or (more likely) some intermediate state between the two with the aggregated proportion
corresponding to the equilibrium rate (cf. Gintis, 2000;
Maynard Smith, 1982). A monomorphic equilibrium occurs
when all individuals play the identical mixed strategy. If weapply this notion to the low-risk condition, a monomorphic equilibrium corresponds to a state where each individual behaves as a watcher stochastically in each trial, with aprobability of about .26 (see Fig. 3 for the third block in
the low-risk condition). A polymorphic equilibrium occurs
when a division of roles exists in a group, with around
26% of individuals always behaving as watchers and theremaining 74% always behaving as free-riding foragers in
the present example.
To shed some light on this point, we examined how frequently each participant switched to a high vigilance state
during each of the three blocks in the two conditions. Since
each block was composed of 20 trials, the frequency of
individual vigilance during a block could range from 0(no vigilance) to 20 (full vigilance).
The top panel of Fig. 6 displays distributions of the vigilance-frequency for each block in the low-risk condition.
Notice that the relatively symmetrical distribution in theﬁrst block disappeared over time; free-riders who neverserved as watchers (shown as black bars) increased rapidly
in the later blocks. This implies the emergence of an equilibrium in the low-risk condition, as evidenced by thedecreased variability in the vigilance rates over time
(Fig. 5), was clearly not monomorphic (see Kameda &Nakanishi, 2002, 2003 for related ﬁndings). A Kolmogolov–Smirnov test revealed that the distribution in the third
block was signiﬁcantly diﬀerent from the distribution predicted by a monomorphic equilibrium (p < .001), where
all individuals behave as watchers with a probability of.26. On the other hand, the observed distributions were
not purely polymorphic either, because there was a lackof committed producers in the third block. The overall patterns indicated that the hardcore free-riders did indeed
emerge to become the majority in a group over time; theother group members were, however, more or less mixed
as to their behavioral choices.
We also checked the consistency of each individual’s vigilance-level between any two consecutive blocks. If role-division emerged within the group, each participant’s
vigilance behavior should have become increasingly moreconsistent over time; someone who engaged in high vigilance X times, in the t-th block, should have done so X
times again in the t + 1-th block. To test this, we examined
the proportion of participants in each group who engaged
in high vigilance for exactly the same number of trials
between consecutive blocks (ﬁrst and second block, second
and third block), to determine the level of consistency overtime. Results supported our reasoning; the mean proportion of consistent participants in the low-risk condition

Observed Frequencies

35

30
25
20
15
10
5
0
1st

2nd

3rd

Block
Fig. 6. Distribution of vigilance frequencies in each of the three blocks in
the low-risk and high-risk conditions. The frequency could range from 0(100% foragers) to 20 (100% watchers).

was only .12 between the ﬁrst and second blocks, but this
proportion increased to .30 between the second and third
blocks (the increase was mainly caused by the emergenceof the hardcore free-riders). This increase in the behavioral
consistency over time was signiﬁcant [F (1, 28) = 13.08,
p < .01].
On the other hand, as shown in the bottom panel ofFig. 6 (see also Fig. 5), the pattern observed in the high-risk
condition was inconsistent with the emergence of a divisionof roles; the symmetry of the distribution remained essentially unchanged and each participant’s vigilance frequency
remained variable across three blocks [F (1, 28) = 1.61, ns].
Taken together, the overall results suggest that the equilibrium hypothesis (Hypothesis 1) was supported in the
low-risk condition; within-group vigilance became morestabilized over time with determined free-riders eventually
emerging as the majority in these groups. No corresponding pattern was observed in the high-risk condition,
however.
Herding behavior under risk (test of Hypothesis 2)
As demonstrated above, the vigilance proportion in agroup approached a mixed equilibrium in the low-risk condition, but not in the high-risk condition. Instead, theDiscussion
Just as animals must forage under predatory risk,
humans too must ﬁnd an eﬃcient trade-oﬀ in dividing theirvaluable resources—such as time, money, attention, and1st block9
8

Low risk

High risk

7
6
5
4
3
2
1
0
0

1

2

3

4

5

6

Number of watchers in a group

Mean observed frequencies

vigilance pattern remained equally variable, both withingroup and within-individual, across the three experimental
blocks in the high-risk condition (cf. Figs. 5 and 6). Next,
let us address whether Hypothesis 2 might better characterize the data.
Hypothesis 2 predicted that the group would shift backand forth between two extreme situations (all vigilance orno vigilance) frequently, due to an individual’s tendency
to follow herd behavior (Hung & Plott, 2001). To see if thiswas the case, we examined the observed frequencies of 7
possible group-compositions in the experiment (cf. footnote 2). A group composition could be (0, 6), (1, 5), (2, 4),(3, 3), (4, 2), (5, 1), or (6, 0), where (n, 6-n) refers to a groupcomposed of n watchers and 6-n foragers in a trial.
Hypothesis 2 predicts that the two extreme combinations[(0, 6) and (6, 0)] should occur more frequently in the experiment than other group compositions.
Fig. 7 displays the mean observed frequencies of eachgroup composition in the low- and high-risk conditions,again broken down into three 20-trial-blocks. The abscissaof each panel refers to the number of watchers in a group—n if using the above notation (n, 6-n).
As depicted in the ﬁgure, diﬀerent patterns wereobserved between the low- and high-risk conditions. The
U-shaped pattern for the high-risk condition is consistent
with the prediction generated from Hypothesis 2, whereas
the U-shaped pattern was less evident in the low-risk condition. A 2 (Condition) · 3 (Block) · 6 (Number-of-watchers) repeated-measures ANOVA revealed a signiﬁcantCondition · Number-of-watchersinteractioneﬀect[F (6, 168) = 2.94, p < .01]—the three-way interaction eﬀect
was not signiﬁcant [F (12, 336) = .90, ns]. Collapsed acrossblock, the (6, 0) cases, in which all group members engaged
in high vigilance, were more frequent in the high-risk condition (M = 3.9 out of 20) than in the low-risk condition(M = 1.4 out of 20), t (28) = 9.45, p < .0001. A regression
analysis of the observed frequency in each condition withlinear and quadratic terms also revealed that the quadratic
coeﬃcient was larger in the high-risk condition (b = 1.21)
than in the low-risk condition (b = 1.01), (t (208) = 9.94,
p < .0001), indicating that homogeneous groups of allwatchers or all foragers were more characteristic of thehigh-risk condition.
Taken together, these results indicate that herding
behavior (Hypothesis 2) was more relevant to the high-risk
condition than to the low-risk condition. Participants who
faced the high-risk situation conformed to others’ behavior
more frequently than those who faced the low-risk situation. Such herding behavior (Hung & Plott, 2001) could
have interfered with the emergence of a strategic mixed
equilibrium in the high-risk condition.

Mean observed frequencies

T. Kameda, R. Tamura / Journal of Experimental Social Psychology 43 (2007) 168–179

2nd block

9
8
7
6
5
4
3
2
1
0
0

1

2

3

4

5

6

Number of watchers in a group
Mean observed frequencies

176

3rd block

9
8
7
6
5
4
3
2
1
0
0

1

2

3

4

5

6

Number of watchers in a group
Fig. 7. Mean observed frequencies of 7 group-compositions in the lowand high-risk conditions. The abscissa of each graph refers to the numberof watchers in a group.

motivation—between foraging and risk-monitoring. If wewere a solitary species, such a trade-oﬀ could be solved interms of an individual optimization problem most of thetime (Houston et al., 1993; Lima et al., 1985; Milinski &Heller, 1978). Yet, as a social species, a simple optimization
solution does not apply to the human case.
Opposite social dynamics operating under risk
Group-living often provides ‘‘aggregation economies’’(Giraldeau & Caraco, 2000; Kameda & Tindale, 2006).
Within a context of risk-monitoring, this means there aremany more pairs of eyes available to survey the landscapefor common risks, which in turn allows individuals living in

T. Kameda, R. Tamura / Journal of Experimental Social Psychology 43 (2007) 168–179

groups to divert supplementary time and eﬀort to other key
activities (such as foraging). Also, given that environmental
cues almost always contain some noise (Brunswik, 1956),
the behavior of others can often be used to provide valuable information regarding the imminence of a particularrisk. If many others are on watch, it is likely that some risk
is brewing, as implied by the law of large numbers in statistics. Using the behavior of conspeciﬁcs is an eﬀective heuristic for reducing statistical uncertainty in perception andchoosing an adaptive behavior in the environment, which isavailable only to social species (Hastie & Kameda, 2005;
Kameda & Nakanishi, 2002, 2003).
However, the game theoretic model (Pulliam et al.,1982) suggests that the basis for just such an aggregation
economy could provide an incentive for each individual
to free-ride on the monitoring eﬀorts of group members.
Using Giraldeau and Caraco’s (2000) terminology, the vigilance/foraging situation constitutes one instantiation of
the producer-scrounger game, in which the eﬃcacy ofeither behavior depends on the number of individuals
already pursuing that strategy—if there are enough watchers, continue foraging; if there are too many foragers,
switch to watching.
Notice from the above reasoning that the presence ofwatchers in the group can tempt the individual in opposite
directions. From a statistical perspective (cf. Hung & Plott,
2001; Kameda, Ohtsubo, & Takezawa, 1997), if many others are watching, the risk is likely imminent, and individuals are better oﬀ switching to vigilance themselves.
Conversely, from the game-theoretic perspective (cf. Giraldeau & Caraco, 2000), if many others are on watch, individuals are better oﬀ exploiting their risk-monitoring
eﬀorts to focus on foraging for themselves. Theoretically,
opposite social dynamics may thus be triggered from thepresence of other watchers in the group. We formulated
these two social dynamics as the equilibrium hypothesis(Hypothesis 1), and as the herding hypothesis (Hypothesis
2). We then tested whether these two dynamics could characterize people’s collective risk-monitoring behavior underdiﬀerent risk-levels.
Results suggested that the risk-level in the foraging environment may moderate these social dynamics. When therisk-level was low, participants’ collective vigilance level
became more stabilized (i.e., less variable) over time, aspredicted by the equilibrium hypothesis (Hypothesis 1).
Moreover, a division of roles emerged after a time in the
low-risk condition; hardcore free-riders emerged to perpetually exploit high vigilance members’ eﬀorts. On the other
hand, when the risk-level was high, such an equilibrium did
not emerge. Instead, the groups shifted back and forth frequently between high vigilance and no vigilance acrossexperimental trials (Hypothesis 2). In other words, participants in the high-risk condition seem to have used others’
behavior as a statistical cue to infer the state of the environment (risky or safe), as observed in some experiments
investigating information cascades (e.g., Hung & Plott,2001).

177

From the adaptationist perspective, it seems highly reasonable to switch our behavioral strategies in response to
the risk-level of the environment—using the presence ofwatchers as a cue to begin free-riding in low-risk environments, but as a statistical cue suggesting imminent danger
in high-risk environments. Each of these strategies may
be generally adaptive depending on the current level of riskin the environment. Along with formal modeling of people’s collective risk-monitoring in general (cf. Kameda
et al., 2003, 2003), future empirical work addressing thisinteraction of risk-level seems quite promising. Speciﬁcally,studies that manipulate risk-level by a diﬀerent operationalization (e.g., diﬀerences in risk probability) from theone used here (diﬀerences in the amount of loss) will be
informative.
Limitations and implications of the present study
Motivated by the importance of risk management inmodern societies (Reason, 1997; Slovic, 1987, 1999), thisexperiment was a ﬁrst step toward an integrative understanding of social psychological mechanisms about riskmonitoring. For this purpose, we have taken an adaptationist perspective (cf. Barkow et al., 1992); speciﬁcally,
we have applied a behavioral ecological model of animal
foraging and vigilance (Giraldeau & Caraco, 2000) to
risk-monitoring in human groups. Behavioral ecologicalmodels of animal behavior (cf. Krebs & Davies, 1993,1997) have many merits for social scientiﬁc research,
including their heuristic value for formulating novel
hypotheses of human behavior (see Kameda & Hastie,
2004; Kameda & Tindale, 2004, 2006, for fuller discussionsof these merits). Yet, there are several limitations as well(cf. Gould & Lewontin, 1979). In the following, we willconsider some of the limitations of the present study alongwith its implications for future research.
First, to understand the social dynamics underlying
human collective risk-monitoring, the present experimentfocused on macro behavioral-patterns, such as the emergence of a mixed equilibrium (Hypothesis 1) or herding
behavior in groups (Hypothesis 2). Such macro-patterns
are predicted to manifest as an accumulation of eachindividual’s adaptive (rational) behavioral decisions
under risk, and they have been a major focus in the
behavioral ecological literature (cf. Giraldeau & Caraco,2000). However, at this point, we do not have enoughinformation on the proximate psychological mechanismsthat enable each individual to conduct the necessary
rational calculations and to use the adaptive heuristics
involved in risk-monitoring. The moderating inﬂuenceof risk-level that we have identiﬁed from the overall
results of this experiment may serve as a useful guide
to explore these mechanisms, including the proximate
cues that trigger diﬀerent behavioral strategies under risk.
Future work addressing these psychological processes isessential for a fuller understanding of human collectiverisk-monitoring.

178

T. Kameda, R. Tamura / Journal of Experimental Social Psychology 43 (2007) 168–179

Second, the group used in this experiment was minimal
in nature, a collection of unrelated individuals. There wasno common group goal governing their behavior; participants essentially tried to maximize their individual outcomes while working within a group structure. However,in many ‘‘real’’ groups (teams, organizations), it is often
the case that the groups have speciﬁc goals, and are structured accordingly with associated social norms (Moreland& Levine, 1982). In such cases, a division of roles often
exists, including oﬃcial roles designated to engage inrisk-monitoring (e.g., auditors), as was the case in the Barlings Group. It is important to examine how the issue weraised in this paper applies to more structured organizations. Indeed, one way to interpret the stable mixed equilibrium, as observed in the low-risk condition, is that it may
provide an infrastructure for oﬃcial divisions of roles toemerge in risk-monitoring. It would be interesting to see
how such formal social roles may emerge over time in agroup where no such structure was built in initially (cf.
Moreland & Levine, 1982).
Lastly, in this paper, we used a laboratory method totest two hypotheses of human collective risk-monitoring
derived from behavioral ecology. Although the laboratory
method is particularly suited for theory-testing with rigorous control, it may well be argued that it sometimes lacks
ecological validity. This criticism is well taken in the present study, because we assumed that money was a valid
currency (Krebs & Davies, 1993, 1997) for stimulating ﬁtness-maximization and trade-oﬀs between vigilance andforaging. However, there may be a more appropriate
resource for tendering risk-monitoring in general, or
which may in fact vary as a function of the risk itself.
Together with laboratory experiments such as the present
one, ﬁeld observations in natural settings would provide
valuable information to better understand human riskmonitoring in modern societies, both theoretically andempirically.
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