Evolution and Human Behavior xxx (xxxx) xxx–xxx

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Evolution and Human Behavior
journal homepage: www.elsevier.com/locate/ens

How do we decide when (not) to free-ride? Risk tolerance predicts
behavioral plasticity in cooperation
Hye-rin Kima, Wataru Toyokawab, Tatsuya Kamedaa,
ab⁎Department of Social Psychology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK

A R T I C LE I N FO

A B S T R A C T

Keywords:
Cooperation
Group productivity
Marginally diminishing returns
Producer-scrounger game
Behavioral plasticity
Risk preference

In collaborations, group productivity typically increases with more cooperators, but is also often subject todiminishing returns. This pattern provides a diﬀerent view about cooperation from traditional social dilemmas:
defection is not necessarily the dominant strategy. Rather, a frequency-dependent “anti-conformist” strategy(cooperate if many others defect, and vice versa) is often individually rational. This study addresses humancooperation under such marginally diminishing group productivity, focusing on the plasticity of cooperative
choices. We conducted a two-part “team foraging” experiment, in which the most- or least-cooperative membersin the ﬁrst part were re-grouped separately for the second part. We observed that cooperating and defecting“types” emerged within a group over time but did not completely persist across groups, with some of the mostcooperative members switching to become the least cooperative (and vice versa). Risk attitude was a key factor
in this switching behavior: greater risk-takers showed greater behavioral plasticity. These results imply thathuman cooperation may be more context-dependent and behaviorally plastic than previously thought.

1. Introduction
Large-scale cooperation is a deﬁning yet theoretically challenging
characteristic of human societies (Bowles & Gintis, 2011). In psychology and behavioral economics, group cooperation has often beenstudied using the social dilemma framework (Dawes, 1980; Laury &Holt, 2008; Ledyard, 1995). However, when viewed through the lens ofgroup production, most natural group tasks are not necessarily social
dilemmas (Foster, 2004; Kameda, Tsukasaki, Hastie, & Berg, 2011).
When a group of individuals collaborates to produce some good,
productivity usually increases monotonically with the number of cooperators, but this relationship often falls short of linearity. The biological literature provides a useful starting point for understanding why
such nonlinear relationships are common. For example, the sentinel
behavior of mammals and birds is a collective endeavor with marginally diminishing returns (Bednekoﬀ, 1997; Trivers, 1971). In humanterms, if a group of people are camping, there is a much larger beneﬁt
from the ﬁrst camper keeping watch for bears than from the ﬁfth,
whose contribution adds little to the group's safety. More formally, 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 group with n individuals being on watch
detects the danger successfully is then approximated by 1-(1-p)n⁎(Laughlin, 1980; Lorge & Solomon, 1955). This means that the groupsuccess in vigilance increases monotonically but diminishes at themargin, with an increase in the number of sentinels, n, in the group.
Social foraging, another key survival task, often shares this structure. When a ﬂock of birds is searching for food, discovery of a rich foodpatch by a single bird results in other birds joining to forage in the same
patch (Giraldeau & Caraco, 2000). The group success in locating a richfood patch is thus approximated by the identical function, 1-(1-p)n,where p denotes the average probability of a single bird encountering arich patch and n refers to the ﬂock size (Barnard & Sibly, 1981; see
Foster, 2004 for other examples in the animal kingdom; see Kameda
et al., 2011 for a review of human cases).
The diﬀerence between such situations and typical social dilemmas(in which free-riding always dominates or outperforms cooperation,e.g., linear public goods: Laury & Holt, 2008; Ledyard, 1995) is the
productivity pattern shown in Fig. 1a. The incremental group returnfrom each contribution diminishes with more cooperators. Dependingon the cost of cooperation, this can lead to individual payoﬀ functions
as illustrated in Fig. 1b: if there are many other cooperators in thegroup, one is better oﬀ defecting; if there are no (or only a few) othercooperators, one is better oﬀ cooperating. In contrast to social dilemmas, the payoﬀs to individuals are frequency-dependent, so neither
pure strategy (cooperation or defection) is dominant.

Corresponding author at: Department of Social Psychology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
E-mail addresses: khr@l.u-tokyo.ac.jp (H.-r. Kim), wt25@st-andrews.ac.uk (W. Toyokawa), tkameda@l.u-tokyo.ac.jp (T. Kameda).

https://doi.org/10.1016/j.evolhumbehav.2018.08.001
Received 20 June 2018; Received in revised form 31 July 2018; Accepted 1 August 20181090-5138/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Kim, H.-r., Evolution and Human Behavior (2018), https://doi.org/10.1016/j.evolhumbehav.2018.08.001

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Fig. 1. Experimental setup. (a) The marginally diminishing group-return curve of the “treasure-hunt” task. When four participants engage in the task without
mutually coordinating their search activity, mean group productivity (the total number of treasures found) increases monotonically with more cooperators (those
who incur the personal search cost) but shows diminished marginal returns. (b) Mean individual net payoﬀs for choosing to cooperate or to defect. If there are noother cooperators, one is individually better oﬀ cooperating even after subtracting the cooperation (search) cost. But if there is at least one other cooperator in thegroup, one is better oﬀ defecting. Strategy payoﬀs are frequency-dependent, and no pure dominant strategy exists. (c) Time sequence in the treasure-hunt task.

Fehr & Fischbacher, 2004; Fischbacher, Gächter, & Fehr, 2001; Kurzban& Houser, 2001), dictator, and trust games (e.g., Yamagishi et al.,2013). Furthermore, a growing body of evidence (Pletzer et al., 2018;
Yamagishi et al., 2013) suggests that some of these individual diﬀerences in cooperation may be predicted by enduring personality traits
such as social value orientation (SVO: Van Lange, 1999), emotional
abilities (Kaltwasser, Hildebrandt, Wilhelm, & Sommer, 2017) and self
control (Kocher, Martinsson, Myrseth, & Wollbrant, 2017). However,
the extent to which such personality traits can explain the coexistenceof cooperators and defectors in human groups remains unclear.
This study explores the emergence and persistence of individual
diﬀerences in human cooperation for group production. Here we present a two-stage experiment implementing a group situation characterized by marginally diminishing returns. Participants worked on a“team foraging” task in two consecutive parts. Based on previous

The evolutionarily stable state of such social dynamics (aka. The
producer-scrounger game: Giraldeau & Caraco, 2000; Giraldeau, Heeb,& Kosfeld, 2017) is a monomorphic population in which every individual adopts an identical mixed strategy (i.e. cooperating randomly
with a uniform non-zero probability determined by the game payoﬀ;
Motro, 1991), or a polymorphic state where ﬁxed proportions of individuals choose either cooperation or defection consistently (Dall,
Houston, & McNamara, 2004). Although exact conditions for which ofthese states emerges remain to be established, factors such as the possibility of communication or signaling have been suggested to favor astable behavioral polymorphism (Dall et al., 2004; Johnstone, 2001;
Motro, 1991).
In previous behavioral-game research with humans, consistent individual diﬀerences in cooperation propensity have been repeatedly
demonstrated in various laboratory settings such as public goods (e.g.,2Evolution and Human Behavior xxx (xxxx) xxx–xxx

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whose location was randomly shuﬄed in each trial, was worth 40 Japanese yen shared among all 4 group members (i.e., 10 yen each).
In each trial, participants ﬁrst decided individually whether or not
to pay the personal search cost of 20 yen, without any communication(“choice stage” in Fig. 1c). Each participant who paid the search cost
was able to check 14 (of 25) spots individually without coordinatingwhere to search with other members (“search stage”). Those who had
decided not to pay waited for the searchers to ﬁnish their searches.
Treasures found by any searchers in the group were shared evenly
among all four members, irrespective of search cost payment, creating a
producer-scrounger game (Giraldeau et al., 2017; Giraldeau & Caraco,2000) among team members (Fig. 1b). At the end of each trial (“feedback stage”), each participant received feedback about their individualnet payoﬀ (10 yen × total number of treasures found −20 yen if participant paid the search cost) and the number of members who hadsearched (cooperated) in the trial. This experimental setup yielded anonlinear joint payoﬀ in which the mean group productivity (the expected gross return) increased with the number of cooperators(searchers) but diminished at the margin (Fig. 1a). Participants were
given the opportunity to work on ﬁve practice trials without payment,
and then proceeded to the main session.

research on the producer-scrounger game with human participants(Kameda et al., 2011; Kameda & Nakanishi, 2002, 2003; Toyokawa,
Kim, & Kameda, 2014), we expected that substantive individual differences in cooperation rates would emerge over time within eachgroup, deviating systematically from the uniform mixed strategy. Using
participants' cooperation levels in Part 1, we then sorted them into newgroups in Part 2, with the most- and least-cooperative individuals assembled separately. If individuals' cooperation propensities are stable,
the gap in productivity between the two newly-sorted extreme groups
should remain unchanged, or may even increase in Part 2. However, if
cooperation is ﬂexible according to group composition, defectors orcooperators will emerge in the most or the least cooperative group respectively, narrowing the initial productivity gap at the group level.
The sorting protocol above thus allows us to examine to what extent
individual diﬀerences in cooperation, which are expected to emerge
within a group, may persist across diﬀerent groups. A similar experimental protocol was applied to birds (nutmeg mannikins) that engage
in social foraging. In a laboratory setting, Morand-Ferron, Wu, andGiraldeau (2011) ﬁrst observed how often each bird exploited (i.e.,
scrounged from) the ﬁndings of other ﬂock members (rather than exploring for new food patches itself), and then sorted them into newgroups according to this scrounging propensity. The overall scroungingfrequency in the “scrounger ﬂock” was no higher than in ﬂocks made up
of intermediate- or low-frequency scroungers, suggesting that individual diﬀerences in social foraging among birds are group-dependent. Here, we modify this experimental protocol to study human cooperation. Our team-foraging task with marginally diminishing returns(Foster, 2004; Kameda et al., 2011) provides an ecologically representative platform to examine a role of frequency-dependent behavioral adjustment in the emergence of individual diﬀerences in humancooperation.

2.4. Main session
There were two parts. After the practice session, participants were
instructed that they would be randomly re-grouped with three otherparticipants as a new team. Part 1 consisted of each group working on
the treasure-hunt task for 15 trials (though this number was not speciﬁed to participants), according to the same protocol as in the practicesession. For Part 2, we ranked the members in each of the four groups
according to their overall cooperation (search) rates in Part 1, andcomposed new 4-person groups by assembling the 1st-, 2nd-, 3rd-, and4th-ranked members separately for Part 2. Participants were only instructed that they were re-grouped with three new members diﬀerent
from Part 1, but were not informed of the details of the composition.
Participants then worked on the treasure-hunt task for 30 trials (onceagain unspeciﬁed to participants) in the new groups according to thesame protocol from Part 1.
After Part 2, participants answered a post-session questionnaire
including several personality scales that seemed relevant to cooperationin the producer-scrounger game. These were: (a) social value orientation (SVO) – measuring social preference for allocating rewards between oneself and another person (Van Lange, 1999); (b) sensation
seeking – measuring preferred level of stimulation (Zuckerman, Kolin,
Price, & Zoob, 1964); (c) risk preferences (Holt & Laury, 2002); (d)resource-allocation preferences as a third party (Kameda, Takezawa,
Ohtsubo, & Hastie, 2010), and (e) perception of interdependence (Jin &Shinotsuka, 1996).
After completing the post-session questionnaire, each participant
was paid according to individual performance in the experiment,
thanked, and dismissed.

2. Material and methods
2.1. Participants
One hundred and sixty undergraduates (121 male, 39 female; mean
age = 19.3 ± 1.2 years) were randomly selected from a subject pool at
Hokkaido University (Sapporo, Japan) to participate in the experiment.
This sample size was based on a previous study of the producerscrounger game (Kameda et al., 2011; Kameda & Nakanishi, 2002,
2003). The study was approved by the Institutional Review Board of theCenter for Experimental Research in Social Sciences at Hokkaido University, and written informed consent was obtained from each participant before the experiment. Participants were compensated for their
participation based on their performance in the experiment(mean = 1168.6 ± 225.6 yen; 1 USD = 100 yen).
2.2. Procedure
In each experimental session, sixteen participants were called to thelaboratory. Each participant was seated in a cubicle with a computer
terminal. All instructions were simultaneously read aloud (via a headset
worn by each participant) and displayed on the screen of each computer. No direct communication was allowed between participants,
who remained completely anonymous to each other throughout theexperiment, and were paid individually after the experiment. The entire
session lasted 90 min. The experimental program was developed using
z-Tree (Fischbacher, 2007).

3. Results
3.1. Mean cooperation frequencies over time
Fig. 2 displays mean cooperation frequencies in Part 1. The mean
number of cooperators in each 4-person group (collapsed across the 15
trials) was 1.73, which was signiﬁcantly higher than the theoreticalequilibrium frequency of one cooperator per group (see Fig. 1b), t(39) = 8.97, p < .001, 95% CI = [1.57, 1.90], M = 1.73, SD = 0.52.
Notice that, in our treasure-hunt task, members' contributions determined group productivity stochastically ― the number of treasures
found could be diﬀerent even if the same number of members cooperated in a group. Participants thus may have behaved cautiously to
avoid the worst possibility of no cooperation (Fig. 1a). In line with this2.3. Experimental task
The sixteen participants in each session were randomly assigned tofour 4-person groups. The four participants in each group worked together on a “treasure hunt” task in which they searched collaboratively
for ﬁve treasures hidden in a 5 × 5 grid (Fig. 1c middle). Each treasure,3

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Fig. 2. Mean cooperation frequencies in Part 1. The horizontal line represents the theoretical equilibrium frequency of one cooperator per group. Error bars show
standard errors of the mean.

3.3. Did behavioral “types” persist across diﬀerent social settings?

interpretation, participants' risk attitudes determined from the postsession survey were generally biased toward risk aversion (M = 4.68,
SD = 1.91, t(148) = −8.45, p < .001, 95% CI = [−1.63, −1.01])
from the risk-neutral point (= 6) of the scale (Holt & Laury, 2002).
More importantly, the mean cooperation frequencies did not deteriorate over time, F(14, 546) = 0.88, p = .580, ηp2 = 0.02. This stationary pattern is markedly diﬀerent from typical social-dilemma experiment results where cooperators decrease in number over iterated
trials within a group (Fischbacher & Gächter, 2010; Horita, Takezawa,
Inukai, Kita, & Masuda, 2017; Keser & Van Winden, 2000), but isconsistent with the frequency-dependent dynamics (cooperate if manyothers defect, and vice versa) of the producer-scrounger game
(Giraldeau & Caraco, 2000; Kameda & Nakanishi, 2002, 2003).

Fig. 4a compares group-level cooperation rates in Part 1 and Part 2.
For Part 2, we simply averaged the cooperation rates of the four participants in each group over the 30 trials. For Part 1 (in which theseparticipants had belonged to diﬀerent groups), we averaged their corresponding cooperation rates as if they had belonged to the same group.
A 4 (Rank: 1st, 2nd, 3rd, or 4th) × 2 (Part: Part 1 or Part 2) repeatedmeasures analysis of variance (ANOVA) of the group-level cooperation
rates revealed no main eﬀect of Part (F(1, 36) = 0.00, p > .99,
ηp2 = 0.00), again conﬁrming that the average cooperation rates did
not deteriorate over time in the producer-scrounger game (Fig. 2). The
main eﬀect of rank was signiﬁcant (F(3, 36) = 101.06, p < .001,
ηp2 = 0.89). As seen in Fig. 4a, all ranks remained the same in bothparts: rank 1 (the most cooperative group) in Part 1 was also rank 1 inPart 2, and the same held for ranks 2 through 4. This indicates that
there were consistent individual diﬀerences in overall cooperation levels across the two parts.
However, the ANOVA also revealed a signiﬁcant Rank × Part interaction eﬀect, F(3, 36) = 14.00, p < .001, ηp2 = 0.54. A pairedsample t-test with Bonferroni correction conﬁrmed that cooperation
rates of the 1st-ranked (most cooperative) groups declined signiﬁcantly
from Part 1 (M = 0.74, SD = 0.06) to Part 2 (M = 0.65, SD = 0.11), t(9) = 3.68, p = .020, 95% CI = [0.04, 0.16], while those of the 4thranked (least cooperative) groups increased signiﬁcantly from Part 1(M = 0.15, SD = 0.07) to Part 2 (M = 0.28, SD = 0.08), t(9) = −4.22,
p = .008, 95% CI = [−0.20, −0.06].
It could be argued that these changes were simply due to a statisticalregression to the mean. To address this possibility, we divided the 30
trials of Part 2 into halves of 15 trials each. Fig. 4b displays changes inthe distributions of cooperative choices in each rank across the threephases: Part 1, and the ﬁrst and the second halves of Part 2. As shown inthis ﬁgure, 100%-free-riders (individuals that cooperated in 0 trials outof 15) emerged in the 1st-ranked (most cooperative) groups, while
100% cooperators emerged in the 3rd- and 4th-ranked groups. Contrary
to an explanation based on regression to the mean, the distributions ofcooperation became more U-shaped over time (with another peakaround 50% cooperation in the 2nd- and 3rd-ranked groups in particular).
Taken together, these patterns suggest that, besides the persistentindividual-level diversity in overall cooperation levels, participants'
cooperative behaviors can also display plasticity in response to groupcomposition. Contrary to the game-theoretical prediction of a uniform
probabilistic strategy, participants displayed substantial individual
diﬀerences in cooperation propensity, at least while they remained

3.2. Emergence of “types” among participants
One stable state of the producer-scrounger dynamics is a monomorphic population in which all participants adopt the same mixed
strategy: cooperating randomly with exactly the same probability determined by the game payoﬀ. As shown in Fig. 1b, the individual payoﬀ
curves for cooperation (searching) and for defection in the treasurehunt task intersect when the number of other cooperators is between 0
and 1, which implies that one is better oﬀ cooperating if there are noother cooperators, and better oﬀ defecting if there is at least one othercooperator in the group. Thus, an individually rational strategy for allparticipants in this experiment is cooperating randomly with probability 0.25 (=1/4).
The bar graphs in Fig. 3 show observed frequencies of participants'
cooperative choices (search cost payments) in Part 1. The number ofcooperative choices ranges from 0 (100% free riding) to 15 (100%cooperation) out of 15 trials. The dotted line shows the theoretical binomial distribution assuming that all participants adopt the individually rational probabilistic strategy (cooperating randomly withprobability 0.25). As seen in Fig. 3, participants' behaviors weremarkedly diﬀerent from the probabilistic strategy. The observed distribution is not unimodal with one peak at 25% as predicted from themodel, but has three peaks at 0%, 50%, and 100%. A KolmogorovSmirnov two-sample test indicates that the observed distribution differed signiﬁcantly from the game-theoretical distribution (D = 0.469,
p < .001). Thus, consistent with previous results (e.g., Kameda et al.,2011; Kameda & Nakanishi, 2002, 2003), a group task with marginallydiminishing returns also yielded substantial diversity in propensity forcooperation (“types”) as in other behavioral games (e.g., Fischbacher
et al., 2001; Yamagishi et al., 2013).
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Fig. 3. The theoretical distribution of cooperative choices aspredicted by the individually rational strategy (dotted line)
and the observed distribution (white bars) in Part 1. The
game-theoretical individually rational strategy is cooperatingwith the probability of 0.25. The x-axis refers to the number ofcooperative (search) choices, which could range from 0 (100%free riding) to 15 (100% cooperation).

within the same group (Fig. 3). However, these “types” were notcompletely stable, but were able to change, even to the opposite types,
when they found themselves in new situations (Fig. 4). This suggeststhat group composition (i.e., whom one is grouped with) can play a
critical role in determining one's cooperation level, according to the
producer-scrounger dynamics (Giraldeau & Caraco, 2000; Kameda &Nakanishi, 2002, 2003).

two regression lines. In Part 2, the more risk-seeking participants (toward the right on the horizontal axis) behaved less cooperatively in the
more cooperative groups (1st- and 2nd-ranked), but more cooperatively
in the less cooperative groups (3rd- and 4th-ranked). That is, participants with more risk-seeking attitudes tended to adjust their cooperation levels in the individually rational manner in response to the newgroup composition, lowering their cooperation rates in the overly-cooperative upper-ranked groups while raising their cooperation rates in
the insuﬃciently-cooperative lower-ranked groups. In contrast, participants with more risk-averse attitudes (toward the left on the horizontal axis) tended to maintain their initial cooperation levels fromPart 1 in Part 2. Note that risk attitudes aﬀected how participants adjusted their cooperation levels to new social environments, but not theirinitial cooperation levels per se (see Table 1). No other psychologicalscale yielded a similar pattern.

3.4. Did psychological scales predict participants' cooperation levels in thegroup task?
In the post-session questionnaire, participants answered several
personality scales that seemed relevant to cooperation in the producerscrounger game: SVO, sensation seeking, risk preferences, resource-allocation preferences as a third party, and perception of interdependence. Table 1 displays summary results of participants' responses
to these scales as a function of their cooperation ranks in Part 1 (see thenote accompanying Table 1 for explanations of each scale and its response categories).
In contrast to recent ﬁndings that individual diﬀerences in cooperation may be related to enduring personality traits (Kaltwasser
et al., 2017; Kocher et al., 2017; Pletzer et al., 2018; Yamagishi et al.,2013), we found no statistical diﬀerence among the four cooperation
ranks on any of the psychological scales examined. The male-female
proportions were not diﬀerent among the four ranks either.
However, participants' risk attitudes did aﬀect how they adjusted
their behaviors in response to the new social environments in Part 2
after group re-composition. Fig. 5 displays participants' cooperation
rates during Part 2 as a function of their initial cooperation-ranks (1stand 2nd combined vs. 3rd and 4th combined) and their risk attitudes.
Not surprisingly, cooperation rates were higher in Part 2 among the 1st
and 2nd groups than the 3rd and 4th groups (β =0.33, t(145) = 4.28,
p < .001, 95% CI = [0.23, 0.73]). However, this main eﬀect was
qualiﬁed by a Rank × Risk-attitude interaction eﬀect, β = 0.19, t(145) = 2.42, p = .017, 95% CI = [0.01, 0.11], as represented by the

4. Discussion
Research on behavioral games, including social dilemmas, has revealed that people exhibit substantial individual diﬀerences in theirpropensity to cooperate (e.g., Fehr & Fischbacher, 2004; Fischbacher
et al., 2001; Kurzban & Houser, 2001; Pletzer et al., 2018; Yamagishi
et al., 2013). We started with the observation that most natural group
tasks are not necessarily social dilemmas (Foster, 2004; Kameda et al.,2011). Many collective tasks (e.g., risk monitoring and foraging) innature have marginally diminishing productivity, such that the individually adaptive strategy is not necessarily a defection but often amixed strategy in which one cooperates probabilistically according to
the negatively frequency-dependent payoﬀ structure (Motro, 1991).
This study addressed whether cooperative diversity among individuals
would still emerge in an ecologically representative collective-foraging
situation (Giraldeau & Caraco, 2000), and if so, how robustly individual
diversity would persist across diﬀerent group compositions.
We found that the behavioral results were markedly diﬀerent fromthe typical social dilemma experiments. In social dilemma experiments,
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Fig. 4. Changes in cooperation rates between Part 1 and Part 2. (a) Comparison of the group-level average cooperation rates between Part 1 (black bars) and Part 2(white bars). The horizontal solid line represents the theoretical (individually rational) cooperation rate of 0.25. The horizontal dotted line represents the overall
cooperation rates collapsed across the ranks in Part 1 and Part 2 (both M = 0.43, which were signiﬁcantly higher than the theoretical rate, t(39) = 4.90 in Part 1 andt(39) = 7.28 in Part 2, both p < .001). (b) Changes in the distributions of cooperative choices in each rank (the 1st-, 2nd-, 3rd-, and 4th-ranked groups) across Part 1
and the ﬁrst and the second halves of Part 2.

The observed stable cooperation pattern within a group (Fig. 2) isgenerally in line with the negative frequency-dependent dynamics(Fig. 1b) of the producer-scrounger game. At the same time, the behavioral results also showed several systematic deviations from thecooperation rates usually decline over iterated trials within the samegroup (e.g., Horita et al., 2017), but such a pattern was not observed inthis group task with marginally diminishing productivity (i.e., producer-scrounger game: Giraldeau et al., 2017; Kameda et al., 2011).
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Table 1
Cooperation ranks in Part 1 by psychological scale responses and sex.
Psychological scales

Cooperation rank

Statistical values

1st

2nd

3rd

4th

21.3%
(n = 32)
5.3%(n = 8)18.0%(n = 27)
7.3%(n = 11)16.7%(n = 25)8.0%(n = 12)16.7%(n = 25)
6.7%(n = 10)χ2(3, N = 150) = 1.64, p > .6, Cramer's V = 0.105

5.18(0.43)
4.78(0.35)
6.08(0.28)
2.55(0.26)
18.61(0.89)
4.60(0.33)

6.16(0.44)
4.87(0.35)
5.81(0.28)
3.38(0.27)
20.22(0.90)
4.64(0.31)

5.68(0.43)
4.66(0.35)
6.61(0.28)
2.55(0.27)
19.50(0.89)
4.65(0.32)

5.25(0.44)
5.00(0.36)
6.06(0.28)
2.94(0.27)
19.25(0.91)
4.82(0.31)

F(3, 148) = 1.08, p > .3, ηp2 = 0.022

16.3%(n = 17)10.6%(n = 11)21.5(0.73)

13.5%(n = 14)13.5%(n = 14)21.62(0.74)

12.5%(n = 13)12.5%(n = 13)21.38(0.73)

9.6%(n = 10)11.5%(n = 12)20.66(0.75)

χ2(3, N = 104) = 1.32, p > .7, Cramer's V = 0.112

5.6%(n = 9)19.4%(n = 31)7.5%(n = 12)17.5%(n = 28)

6.9%(n = 11)18.1%(n = 29)

4.4%(n = 7)
20.6%(n = 33)

χ2(3, N = 160) = 2.00, p > .5, Cramer's V = 0.112

aSocial Value Orientation (Van Lange, 1999)
Percentage of Pro-social Respondents
Percentage of Pro-self Respondents
Sensation Seeking Scaleb (Zuckerman et al., 1964)
Thrill and Adventure Seeking (TAS)
Experience Seeking (ES)Disinhibition (DIS)
Boredom Susceptibility (BS)Overall
Risk Attitudec (Holt & Laury, 2002)
Preferences for Distribution Rulesd (Kameda et al., 2010)
Percentage of Egalitarian Rule Endorser
Percentage of Merit (Proportionality) Rule Endorser
Perception of Interdependencye (Jin & Shinotsuka, 1996)
Sex Ratio
Percentage of Females
Percentage of Males

F(3, 148) = 0.17, p > .9, ηp2 = 0.003
F(3, 148) = 1.47, p = .225, ηp2 = 0.030
F(3, 148) = 2.17, p = .094, ηp2 = 0.043
F(3, 148) = 0.55, p > .6, ηp2 = 0.011
F(3, 148) = 0.09, p > .9, ηp2 = 0.002

F(3, 156) = 0.34, p > .7, ηp2 = 0.007

Note: Unless speciﬁed otherwise, numbers in parentheses refer to standard error.
aMeasure of preference for allocating rewards between oneself and another person. Social value orientation is operationally deﬁned as a combination of weights
attached to the welfare of an interaction partner relative to oneself in resource allocation (Van Lange, 1999). Participants categorized as “pro-socials” assigned
positive weights to both their welfare and their partner's welfare, while “pro-selves” assigned positive weights to their own welfare but no or negative weights to theirpartner's welfare.
b
Measure of preferred level of stimulation (Zuckerman et al., 1964), composed of four subscales (thrill and adventure seeking, experience seeking, disinhibition,and boredom susceptibility).
cStandard measure of risk attitudes in behavioral economics (Holt & Laury, 2002), where participants responded to ten paired lottery-choice decisions diﬀering inrisk levels. In the analysis, we reversed the original scale so the higher score indicating the more risk-seeking attitude; the score for risk-neutral attitude was 6.
d
Measure of preferences for various resource-allocation schemes as a third party for those who diﬀer in contribution levels (Kameda et al., 2010). Participants
were classiﬁed as either endorsers of the egalitarian rule or the merit (proportionality) rule.
e
Measure of subjective importance of direct and indirect reciprocity. Participants were asked to evaluate four questions regarding social interdependency on 7point scales (Jin & Shinotsuka, 1996).

free-riding to cooperation) has recently been reported in non-humanspecies including birds (Morand-Ferron et al., 2011) and ants
(Hasegawa, Ishii, Tada, Kobayashi, & Yoshimura, 2016). While such
behavioral plasticity among animals sometimes takes place over a long
period of time (e.g., developmental time: Aplin & Morand-Ferron,2017), we observed that participants adjusted cooperation levels rather
quickly (within less than an hour) in our experiment. This indicates thathuman cooperation in group production is not necessarily ﬁxed as“cooperative personality” but can be behaviorally plastic in response to
local ecology and social interaction.
We also observed that the more risk-seeking participants revealed
greater behavioral plasticity (adjusting cooperation levels in the individually rational manner to new social environments), whereas themore risk-averse participants tended to maintain their old behaviors.
Several recent studies suggest that exploratory behaviors by humans aswell as animals in unfamiliar environments are closely linked to riskrelated traits like boldness and aggressiveness (Toyokawa, Saito, &Kameda, 2017; Wolf, van Doorn, Leimar, & Weissing, 2007). For example, diﬀerences in risk sensitivity may explain diﬀerentiated foraging
behaviors in birds (Kawamori & Matsushima, 2012 for diet-menu differentiation between species; Wright & Radford, 2010 for foragingniche partitioning within species). For humans, emerging neuralgame-theoretical predictions.
First, cooperation rates (M = 0.43) were higher than the theoreticalrate (0.25). Such inﬂated cooperation rates may reﬂect participants'
risk-averse choices in response to the stochastic nature of our treasurehunt task (Fig. 1c). Second, rather than the unimodal pattern predictedby the uniform probabilistic mixed-strategy, the distribution of cooperative choices revealed three peaks, at 0%, 50%, and 100% cooperation (Fig. 3). The emergence of such individual “types” (e.g., full
defector, full cooperator: see also Kameda & Nakanishi, 2002, 2003;
Kameda et al., 2011; Toyokawa et al., 2014) accords with results fromthe other behavioral games including social dilemmas (e.g., Fehr &Fischbacher, 2004; Fischbacher et al., 2001; Kurzban & Houser, 2001;
Pletzer et al., 2018; Yamagishi et al., 2013).
However, more importantly to our argument, these “types” did notnecessarily persist across diﬀerent group settings. When placed inoverly uncooperative (cooperative) groups, 100% cooperators (free-riders) emerged (Fig. 4b). These patterns imply that individual cooperation is stable within a group but adaptable across diﬀerent settings, in accordance with the producer-scrounger dynamics (“cooperate
if many others free-ride, but defect if many others cooperate”:
Giraldeau & Caraco, 2000; Giraldeau et al., 2017). Interestingly, as
stated in the introduction, similar behavioral plasticity (especially from7

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Fig. 5. Participants' cooperation rates in Part 2 as a function of cooperation ranks (1st and 2nd combined vs. 3rd and 4th combined) in Part 1 and risk attitudes. Riskattitude scores ranged from 1 (most risk averse) to 10 (most risk seeking).

emotive processes: Adolphs, 2009) was larger in prosocials and positively correlated with cooperation, while the volume of dorsolateralprefrontal cortex (a region associated with controlled processes) was
larger in proselfs and negatively correlated with cooperation.
However, such a direct linkage may not hold in our case. In the
producer-scrounger game, group production diminishes at the margin;
the resulting negative frequency dependence in individual payoﬀs(Giraldeau & Caraco, 2000; Kameda & Nakanishi, 2002, 2003) may
engage all players in strategic deliberations about their choices. In otherwords, the seemingly minor diﬀerence in the shape of group-production
functions (i.e., linear vs. non-linear) may trigger diﬀerent cognitive
strategies for group cooperation. Furthermore, diﬀerent cooperation
norms may also be evoked in these two cases. While a dominant social
norm in traditional (linear) social dilemmas is full cooperation by all
members (Fehr & Fischbacher, 2004), such a norm may not necessarily
work in the (non-linear) producer-scrounger game, where full cooperation yields redundancy (and thus ineﬃciency) at the group level.
We are currently investigating this possibility empirically, which wehope may provide a useful insight about social evolution of cooperation
norms in humans.
Overall, our results suggest that human cooperation in group production may be more context-dependent than previously thought.
Future research examining core dimensions underlying human behavioral plasticity, including individual risk tolerance and social norms,
seems critical to understanding the large-scale cooperation unique toour species and possibly extending its natural and cultural boundaries
toward the betterment of our societies (Bowles & Gintis, 2011).

evidence suggests that individual risk attitudes may be a core dimension underlying various social and economic decisions (Kameda et al.,2016; Mobbs, Trimmer, Blumstein, & Dayan, 2018; Shenhav & Greene,2010).
Previous research also indicates that learning ability may sustain
ﬂexible behavioral tactics depending on local ecology and social interaction (e.g., Dubois, Morand-Ferron, & Giraldeau, 2010; Ezaki,
Horita, Takezawa, & Masuda, 2016; Katsnelson, Motro, Feldman, &Lotem, 2012). In an evolutionary game analysis of producer-scrounger
behavior, Dubois et al. (2010) showed that learning agents that adjust
behaviors in response to local conditions can invade a population of
non-learning agents that play producer or scrounger with ﬁxed probability. Interestingly, however, the analysis also showed that those
ﬂexible learners cannot dominate the population entirely, often
yielding a mixed population where ﬂexible learners and inﬂexible nonlearners coexist. The theoretical analysis of Dubois et al. (2010) seems
to concur with our observation that risk-seeking “switchers” and riskaverse “keepers” coexist. Along with an ability to learn, risk-seeking
may permit ﬁner tracking of changes in the reward structure of adaptive environments, enabling some individuals to employ more ﬂexible
behavioral tactics. Taken together, risk-tolerance and learning ability
may be keys to ﬂexible behavioral adjustment in both humans andanimals, along with other personality traits such as exploration, boldness, and aggressiveness (Dall et al., 2004; Gosling, 2001).
Lastly, it seems important to consider why social value orientation(SVO: Van Lange, 1999) does not predict participants' cooperation in
the producer-scrounger game, while it does predict individual cooperation in traditional social dilemmas (e.g., Yamagishi et al., 2013).
Rand, Greene, and Nowak (2012) argued that dual (i.e., automatic/
intuitive vs. controlled/deliberative) processes underlie human cooperation and that cooperative choices are often initiated by participants who intuitively employ “social heuristics” that are adaptive ineveryday life (see also Rand et al., 2014). An fMRI experiment by
Fermin et al. (2016) provides some support for this claim, showing thatthe volume of amygdala (a brain region associated with automatic

EthicsThis study was approved by the Institutional Review Board of theCentre for Experimental Research in Social Science at Hokkaido
University. Written informed consent was obtained from all participants
before beginning the task.
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All data are available from the corresponding author upon request.
Competing interest
We have no competing interest.
Acknowledgments
This research was supported by Japan Society for the Promotion of
Science Grant-in-Aid for Scientiﬁc Research JP16H06324 and Japan
Science and Technology Agency CREST Grant JPMJCR17A4(17941861) to the corresponding author. Support from CiSHub at the
University of Tokyo is also appreciated. We thank Tom Wisdom, Keigo
Inukai, and Yukiko Ogura for their helpful comments on the manuscript.
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