451863

XXX10.1177/1368430212451863Kameda et al.Group Processes & Intergroup Relations

Group Processes &Intergroup Relations
ArticleIs consensus-seeking unique tohumans? A selective review ofanimal group decision-making and
its implications for (human) social
psychology

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Group Processes & Intergroup Relations
1­–17© The Author(s) 2012
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DOI: 10.1177/1368430212451863
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Tatsuya Kameda,1 Thomas Wisdom,1 Wataru Toyokawa1
and Keigo Inukai1

Abstract
Recent research on animal behavior suggests that group decision-making may not be uniquely human.
Based on Tinbergen’s (1963) “four questions,” this paper proposes that linking biological- and socialscience approaches is important to a better understanding of human group decisions. Toward thisend, we first review some recent findings on collective behavior by social insects (ants and honeybees
in particular). We then argue that several fundamental processes (e.g., positive feedback, nonlinear
responses to social frequency information, and use of quorums) commonly underlie human andnon-human group decision-making under uncertainty, while key prerequisites for the emergence ofcollective intelligence may be more vulnerable to social nuances in human contexts. We sketch somefuture research directions to promote cross-fertilizations between the two fields.
Keywords
group decision making
Paper received 16 August 201; revised version accepted 24 May 2012.
Human societies fundamentally rely on groupdecision-making. Committees and panels are
everywhere, ranging from community-level
town meetings, executive committees in firms,
review panels in science, juries and examination
boards in legal domains, to prefectural, national,
and global political assemblies. The heavy useof committees is observed not only in modern
societies (Hastie, Penrod, & Pennington, 1983;
Kameda, Tindale, & Davis, 2003; Witte & Davis,

1996) but in more traditional societies as well. For
example, by reviewing ethnographic data about
tribal societies, Boehm (1996) showed that tribes
1

Hokkaido University, Japan

Corresponding author:
Tatsuya Kameda, Center for Experimental Research in Social
Sciences, Hokkaido University, Bungakubu, N10 W7,
Kita-ku, Sapporo, Japan.
Email: tkameda@let.hokudai.ac.jp

2		
usually hold meetings composed of adult men
when making important, emergency decisions,such as whether to raid an adjacent tribe to solve
land disputes. “Big man,” the most powerful manin the tribe, typically serves only as a chairpersonof the meeting rather than a dictatorial authority(see also Kameda & Tindale, 2006; Wilson, 1997).
Along with our highly sophisticated faculty for
language, the ubiquity of group decision-making
across many societies may seem to suggest thatgroup decisions are uniquely human. However,recent research on animal behavior suggests that
this is not the case. Group decision-making seems
to be common in the animal kingdom as well,including social insects (e.g., ants, termites, honeybees), fish, and some mammals (Conradt & Roper,
2003, 2005; Seeley, 2010; Sumpter, Krause, James,
Couzin, & Ward, 2008). Recently, Larissa Conradt
and Christian List edited a special issue on “groupdecision making in humans and animals” in aprestigious biology journal, in which they wrote:
Although cross-referencing of natural sciencepublications by social scientists (e.g. List 2004;
Hastie & Kameda 2005), and vice versa (e.g.
Conradt & Roper 2005), has already begun,indicating the mutual interest in interdisciplinary exchange, the language and thinking inthe social and natural sciences are disparate
enough to hamper communication. (Conradt& List, 2009, p. 720)
This paper thus reviews some of recent findings about animal group decision-making andexplores their implications for research on humangroup behaviors. This paper is structured as follows. In the first section, we delineate why linking
the knowledge of biological and social sciences isimportant to understanding human group decisionmaking. Specifically, we base our arguments on
Nikolaas Tinbergen’s (1963) famous distinctionsof “the four questions” (especially the ultimatelevel and proximate-level questions) that should
be asked of any animal behavior, including
humans (van Vugt & Kameda, in press). In thesecond section, we selectively review some of therecent findings on group decision-making byGroup Processes & Intergroup Relations
non-human animals (social insects in particular),and explore their relevance to human endeavors.
In the final section, we sketch future research
directions in this field to facilitate the joint enterprise on group decision-making and to overcome
the terminological and conceptual barriers between
the biological and social-scientific approaches.

Why should social scientists
care about “animal groupdecision-making” (and viceversa)?
What is meant by “animal group decision-making”?
This concept may seem mysterious or perhaps
simply metaphorical to many social scientists––otheranimals do not have the sophisticated language
capacity of humans, nor can they deliberate overchoices or cast votes. How can they make groupdecisions?
However, as we review below, recent research
on animal behavior suggests that, in some nonhuman social animals, specific body postures, ritualized movements or specific vocalizations indeedfunction as ‘votes’ (see Conradt & Roper, 2003).
Furthermore, such voting signals can be integrated in various ways, including by a majority rule(e.g., Byrne, 2000; Stewart & Harcourt, 1994),
accumulating until an intensity threshold (quorum) is reached (e.g., Pratt, Mallon, Sumpter, &Franks, 2002; Seeley & Buhrman, 1999), and averaging over all votes (e.g., Oldroyd, Gloag, Even,
Wattanachaiyingcharoen, & Beekman, 2008).
Thus, the language faculty, though undoubtedly
highly beneficial, does not seem to be the prerequisite for group decision-making in general.
Nikolaas Tinbergen, a Dutch ethologist who
won the 1973 Nobel Prize in Physiology or
Medicine, proposed that four questions are fundamental to understanding any animal behavior(Tinbergen, 1963)––Questions about (1) mechanism (causation); (2) development (ontogeny);(3) evolutionary history (phylogeny); and (4)function (adaptation) of the focal behavior. The
first question concerns the proximate mechanism
underlying the behavior, including what stimuli

Kameda et al.
elicit the response, and how the process is regulated at the molecular, physiological, neural, cognitive, or social level. (Most social psychologists
are mainly interested in this level of analysis.) The
second question concerns the development of
the behavior within individuals, including how
the behavior changes with age and what earlyexperiences are necessary for the behavior to
emerge. The third question concerns the phylogeny of the behavior, or how the behavior compares with behaviors in related species. Finally,
the fourth question asks the behavior’s ultimate
functions, i.e., how the behavior helps the agent’s
survival and reproduction. Tinbergen (1963)
argued that failure to distinguish between thefour questions, all of which are essential for acomprehensive understanding of the behavior,could (and did) lead to conceptual confusions.
It is thus important to specify that we will befocusing on the first (proximate mechanism) and
fourth (ultimate function) questions, in order tofind parallels between seemingly disparate species,
which nonetheless have important problems andcapabilities in common. As we will review later,group decision-making is most typically evinced
when non-human animals engage in foraging forfood or other key resources, or when some urgent
collective move is needed (e.g., moving to a newnest site when the old nest has been damaged or
overcrowded). Interestingly, human group decisionmaking also seems to revolve around the sametwo themes. In many modern organizations,examples of resource-foraging groups are abundant (e.g., investment committees), as well as task
forces to manage urgent risks that demand swift
collective action (Reason, 1997). In reviews of
ethnographic records of tribal societies, Boehm(1996) also documented various emergency situations (e.g., heightened conflicts with an adjacenttribe, ecological crises caused by natural disasters),
where consensus was actively sought throughgroup decision-making to mobilize all band members. Thus, from Tinbergen’s fourth, ultimate
function-level perspective, group decision-making
may be seen as a common solution to common
adaptive problems for human and non-humananimals, namely, foraging and urgent mobilization

3of group members among behavioral-choice
options in which uncertainties abound (seeKameda & Tindale, 2006). One function served
by group decision-making in such contexts is toimprove the accuracy or quality of a behavioral
choice beyond other decision mechanisms (e.g.,despotism), under constraints of time and effort(e.g., Gigerenzer, Todd, & the ABC ResearchGroup, 1999; Hastie & Kameda, 2005; Kameda,
Tsukasaki, Hastie, & Berg, 2011). Another function of group decision-making is to maintain theunity of the group. If the group is spatially or
behaviorally divided in emergency situations (e.g.,
split between different destinations), then members can become more vulnerable to predators or
rival groups, as well as losing economies of scaleand specialization in their common efforts. It isthus often important to align members toward thesame direction and maintain group-based advantages (Conradt & Roper, 2005; Sumpter, 2010).
The third, phylogenic-level perspective suggests that, to understand human group decisionmaking, we should be most concerned withcollective behavior by primates, our closest relatives in the animal kingdom. However, we do nottake this strategy in this paper; the focus of ourreview is not on primates but on social insects(ants and honeybees in particular). These animals
are certainly quite distant from humans in thephylogenetic tree, yet they display an impressivearray of group performances as collectives,
sometimes referred to as “swarm intelligence”(Beni & Wang, 1989), despite their limited cognitive capacity as individuals. Related to this phylogenetic distance, the developmental processes ofhumans and social insects are disparate enough tomake Tinbergen’s second question (ontogeny)
outside the scope of this paper as well. In thenext section, we will review how these animals
can achieve high-level group performances ascollectives by focusing on algorithms of individual agent’s choice behaviors (the first mechanismlevel question).
So, using the first and last of Tinbergen’s(1963) “four questions,” it seems plausible to saythat group decision-making is a common but
separately evolved set of mechanisms used by4		
some gregarious species (e.g., humans, ants, termites, honeybees) to perform shared adaptivefunctions efficiently, i.e., solving problems revolving around foraging and urgent group moves
under uncertainty. Given this, we believe thatlinking the knowledge of biological and social
sciences is important to a better understanding ofgroup decision-making under a common theoretical umbrella.

How social insects makegroup decisions: A selective
review
Conradt and Roper (2005) proposed a useful conceptual distinction to classify animal group decisionmaking––combined vs. consensus decisions.
Combined decision-making refers to cases where
animals decide individually, without requiring a
consensus but in a manner that is somehow
dependent on the behavior of other group members; the aggregate results of these individual
decisions critically affect the group as a whole.
Many foraging decisions fall into this category,where foragers seek resources (e.g., nectar, prey)
individually but under social influence (e.g., usingsocial-frequency information) from other foragers. As we will examine in the final section, various
consumer decisions in humans also fall into thiscategory. Consensus decision making, on the other
hand, concerns cases in which group members
make decisions together with the requirement ofreaching a consensus, i.e., all members abiding by
the decision outcome. Moving decisions, including decisions about where and when to migrate toa new nest site, fall into this category. Some foraging decisions (e.g., cooperative hunting by bothhumans and non-humans) are also in this category. In the following sections, we will review
group decision-making by ants and honeybees
respectively, according to these categories.1

Ants
Ants communicate with each other using pheromones. While searching in its environment, a worker

Group Processes & Intergroup Relations
ant will often pause briefly to deposit a small amountof pheromone along its route. Others are attracted
to these pheromone markings, and will often reinforce them while following the trail. As we will see
below, this seemingly simple mechanism provides afoundation for a complex array of coordinatedbehaviors and patterns, including the formation oftrails to food resources and new nest sites, and optimization of these behaviors according to adaptive
principles (Hölldobler & Wilson, 1990; Wilson &Hölldobler, 2005).

Combined decisions
Deneubourg and colleagues conducted a seriesof experiments to examine how ants, which have
only a limited individual capacity for orientation,
were able to locate food resources efficiently as
collectives. In one experiment, Goss, Aron,
Deneubourg, and Pasteels (1989) placed a bridge
between a nest of ants (Iridomyrmex humilis) and afood source. The bridge had a skewed figure-8
shape. Starting from the nest end, it split into two
branches of different lengths at two different
points, which eventually merged to the same destination where the food was placed. A forager
going in either direction (leaving the nest or leaving the food) had to choose between two paths at
two choice points, which yielded four routes intotal. Results showed that, 5–10 min after placement of the bridge, explorers crossed it and discovered the food. A few minutes later, theshortest path between the nest and the foodsource was followed by a large majority of theants. The ants solved the route-finding problem
correctly as a collective.
How could this happen? This occurredbecause ants traveling the shorter path returned
home faster and thus reinforced the pheromone
markings on the path more frequently (i.e., a pathwhose length is half of the other’s is marked
twice while an ant travels to and from the foodsource, as compared to the other path that could
be marked only once in the same time period),and because others were nonlinearly attracted tothe higher pheromone concentration.

Kameda et al.
In another experiment, Beckers, Deneurourg,and Goss (1993) presented ants (Lasius niger) with
two food sources of different quality, which wereconnected to the nest by a Y-shaped bridge. One
end of the bridge always had a 1 M (mol/L) sugar
source, while the other end had either 1, 0.5, 0.1,
or 0.05 M. Results showed that proportions of
the ants visiting the richer source increased rapidly as the difference in concentration between
the two sources increased, with 86% of the ants
visiting the 1 M source over the 0.1 M source and100% over the 0.05 M source. This occurredbecause each ant laid pheromone trail markingsin proportion to the concentration of sugar solution found (the richer the source, the more pheromone), and because others were nonlinearlydrawn to stronger pheromone markings between
the two ends.
We have summarized other examples of research
on ants’ collective foraging behavior in Table 1.

Consensus decisions
Nest migration requires not only individual
search behaviors as we have seen above, but also
some mechanisms to aggregate individual judgments into a consensus. In gregarious species
such as ants, all members must abide by the consensus outcome whether or not they contributed
to it, in order to maintain group cohesion against
predation and other risks (Conradt & Roper,
2005; Sumpter, 2010). As we see below, “quorum
rules” are usually used in these situations to yield
the group consensus.
Franks, Mallon, Bray, Hamilton, and Mischler(2003) conducted a series of experiments to
examine how ants (Leptothorax albipennis) choose a
new nest from among several options, which had
different values on three attribute dimensions(darkness, height, width). Results showed that anant colony whose nest had been damaged wasable to aggregate the attribute information coherently, choosing the best nest site in terms of overall quality from among as many as five options.
The colony also completed migration (i.e. all individuals transferred) to the new nest site within acouple of hours.

5
Using an agent-based computer simulation,
Pratt, Sumpter, Mallon, and Franks (2005)
showed that such collective intelligence in a colony’s migration can be understood by the following process model. The model assumes that
migration proceeds by four different phases, inwhich ants gradually develop commitment to aparticular nest site. When nest damage is initiallydetected, a subset of workers (about 30% of thecolony) starts an exploration phase, individually
searching for candidate sites. Upon finding a candidate site, an individual ant enters an examination
phase, carrying out an independent quality evaluation of the site, whose duration is inversely proportional to the site’s quality (less time forhigher-quality sites). Once the individual has
accepted the site in terms of quality, she enters acanvassing phase, returning to the old nest to recruit
another ant to the new site (via “tandem-run”).
Each of the recruited ants then makes her own
independent examinations of the new nest, proceeding to further tandem-run canvassing if warranted. Because ants take less time to accept
higher-quality sites, overall recruitment is fasterto such sites. Finally, once the population in the
new nest exceeds some “quorum threshold,” arecruiting ant enters a committed phase. The recruiters stop the relatively slow tandem-runs, andaccelerate the migration process by carrying passive nestmates and brood to the new nest site.
This quorum threshold marks a key feature ofants’ migration as a consensus (and not combined) decision. (See Table 1 for recent work
about how ants can sense such quorums in the
population during nest migration.)

Honeybees
As opposed to the use of pheromones by ants,
honeybees communicate with each other about
movement decisions primarily through a “waggle
dance” with a figure-8 pattern. Waggle dances are
performed by foragers that have located foodresources (nectar, pollen), water resources,or new nest sites. The direction and duration of
the waggle dances are known to be related tothe direction and distance from the hive to theAnts; foraging;experiments andsimulations

Beckers,
Deneubourg,
Goss, &
Pasteels (1990)
Nicolis &Deneubourg(1999)
Detrain &Deneubourg(2002)

Ants; nest migration;
experiments

Ants; nest migration;
simulations

Honeybees; foraging;model analysis

Honeybees; nest
migration; experiments

Pratt (2005)

Pratt &
Sumpter(2006)

Camazine &Sneyd (1991)

Visscher &Seeley (2007)

Ants; foraging; review

Ants; foraging; model
analysisAnimals, task situation,and methods

Study
Presented with two food sources of equal quality, a group of workers settled on one of them rather than
exploiting both equally, owing to an initial random imbalance between the two which was magnified throughthe positive feedback of pheromone attraction. If a low-quality food source had been settled on first, a
newly-introduced high-quality food source was discovered, but the group did not switch to exploit it instead.
If multiple sources of food are available, a group of ants generally choose just one to exploit. However,if the colony is relatively small or the number of sources is very large, multiple sources may be exploitedequally.
Ants’ simple individual-level food recruitment rules lead to a variety of adaptive group-level behavioral
patterns according to differences in individual characteristics and environmental factors. For prey scavenging,
the strength of an individual’s recruitment pheromone trail is inversely proportional to ability to move theprey. This rule effectively encodes information about prey size, shape, and weight, as well as environmental
features that hinder retrieval and the presence of sufficient helpers. Simple iteration and feedback leads
to appropriate recruitment and colony-level responses for smaller prey (e.g., retrieval by a contingentof small workers) or immobile large prey (e.g., recruitment of larger workers for on-site processing andconsumption). A similar rule applies for recruitment in exploitation of liquid nutritional resources: A return
trail is only laid if a forager is able to ingest a “desired volume,” whose value varies across individuals. This
rule flexibly tunes the colony’s response according to resource size, renewal or depletion, crowding at the
food site, and colony nutritional needs.
In order to switch to the final stage of nest site migration, individual workers detect the quorum thresholddensity at the site simply via the rate of direct physical encounters with nestmates at the site. This thresholdvaries across individuals, but the mechanism appears robust to this variation.
Ants seem to use a single decision algorithm to respond adaptively to two different problems: Quickly
finding a new nest in a crisis and deliberatively selecting the best available new home. By varying the rates atwhich they (a) leave the nest to search for new nests and (b) begin recruiting others to an acceptable nest,
the colony-level response adaptively favors either speed or accuracy. Varying the quorum threshold primarily
affects the probability of splitting the colony among multiple nests, but does not affect the speed.
A non-linear differential equation model describes the activity of foraging bees. Using parameter estimates
based on previously published data, the modeling results agreed closely with experimental observations.
Selective exploitation of the most profitable nectar sources occurs through an autocatalytic, self-organizing
process.
Shortly before takeoff, bees emit a crescendo of a high-pitched mechanical signal, called worker piping. Only
scouts that have visited the swarm’s chosen nest site produce piping signals.

Summary of findings

Table 1. Selective research examples of combined and consensus decisions by ants and honeybees (see the text for other examples)6		
Group Processes & Intergroup Relations

Kameda et al.
resource (Lindauer, 1961; Seeley, 1995, 2010; vonFrisch, 1967).

Combined decisions
Seeley, Camazine, and Sneyd (1991) conducted aseries of field experiments to test how efficiently
a colony of honey bees (Apis mellifera ligustica)
could exploit nectar sources. These researchers
placed two feeders (one feeder contained moreconcentrated sugar than the other) in opposite
directions (with each being 400 m away) from thehive, and altered the location of the richer feeder
after 4 h. The bees were able to track this change,
and consistently focused their foraging efforts asa colony on the more profitable feeder. Seeley
and colleagues then examined how the colonylevel ability emerged from the behavior of individual bees through a series of ingenious
manipulations. Results showed that the honeybees finely adjusted several components of theirforaging behavior in accordance with nectar
source profitability. When the quality was higher
than some threshold, the bees foraged more
quickly and danced more vigorously, thereby
recruiting other bees to exploit the richer source.
Furthermore, virtually all foragers (e.g., 115 of
117 bees) visited only one of the two feeders during their foraging. This means that the bees
achieved high colony-level performance using
only individual-level calculations of absolute
profitability rather than relative comparison ofmultiple sources. In other words, this process isbased on a decentralized control, whereby acoherent colony-level response to different foodsources emerges from local interactions without
overall consensus being explicitly sought.
(Camazine & Sneyd, 1991, developed a mathematical model for this process; see Table 1.)

Consensus decisions
In late spring to early summer, as a large hive outgrows its nest, a colony of honey bees often
divides itself. The queen leaves with about twothirds of the worker bees to create a new colony,
and a daughter queen stays in the old nest with7
the rest of the worker bees. The swarm leaving
the colony must find a new home in a short time,
which is critical to their survival. The departing
swarm, which is composed of 10,000 or so bees,
typically clusters on a tree branch, while several
hundred scout bees search the neighborhood fora new home. These scout bees fly out to inspectpotential nest sites, and, upon returning to thecolony, perform waggle dances to advertise any
good sites they have discovered (Seeley, 2010).
In an experiment, Seeley and Buhrman (2001)
presented honeybees with an array of five nest
boxes, only one of which was a high-quality nestsite while the other four were of medium-quality.
The honeybee swarms chose the best nest site
80% of the time. As in the foraging case, thisswarm-level performance in nest search wasmodulated by a scout bee’s adjustment of waggle
dances in accordance with nest site quality: The
better the site, the stronger the dance. Other
scout bees that have not flown out yet, as well as
those that have stopped dancing, observed thesedances and decided where to visit. In these decisions, the bees were more likely to visit andinspect the sites which have been advertised
strongly by many predecessors. This process constitutes a positive feedback loop, yielding theswarm intelligence displayed in locating the best
nest site.
Different from the foraging case, however, thescout bees must terminate the search phase atsome point and mobilize the entire swarm to the
new nest. Seeley and Visscher (2003) examined
how such consensus decisions, which all members must abide by, were made. Results from aseries of experiments showed that the honeybees
used a quorum rule, where they began preparations for liftoff as soon as enough of the scout
bees (not necessarily all of them, nor in fact anyof the non-scout bees) have approved of one ofthe potential nest sites. When the quorum wasreached, the scout bees used special wing-beatsignals, known as “piping,” to alert other nonscout bees in the swarm to warm up their muscles
in preparation for the entire swarm to lift off andfly to the new nest (Visscher & Seeley, 2007
examined this process in detail; see Table 1).

8		What have we learned fromgroup decision-making bysocial insects? Implications
for human group decisionmaking
We have reviewed selectively some of the recent
findings on group decision-making by ants andhoneybees, focusing on proximate mechanisms
by which these animals achieve high-level performances as collectives (for a comprehensive expert
review, see e.g., Detrain & Deneubourg, 2008). As
social scientists concerned with human groupdecision-making, what have we learned fromthese animals’ behavior?

Key mechanisms underlying swarm
intelligence
Taken together, the combined or consensus decisions of ants and honeybees when foraging forfood or when migrating to a new nest site, haveseveral key elements in common to yield theirhighly impressive group-level performances. The
key factors include positive feedback along with nonlinear responses to social frequency information(e.g., trail markings by pheromones; the numberof bees engaging in waggle dances). In the foraging case (combined decisions: Conradt & Roper,
2005), the process is started by one forager thatfinds a food source first, which is followed bymore and more foragers over time (positive feedback). As more foragers are recruited, the rate ofrecruitment accelerates further, because foragers
react to the social-frequency information in anonlinear manner (i.e., more accentuated thanproportional responses). Small initial differencesin social frequency between options are thus
amplified, so that the option favored by the greatest frequency is eventually taken by most foragers
in the colony. In the case of nest migration where
mobilization of the entire group is critical (consensus decisions: Conradt & Roper, 2005), thisprocess is further accelerated and cemented by aquorum rule. The probability of performing anaction increases sharply when a certain social frequency, or quorum, is reached. Such a quorum

Group Processes & Intergroup Relations
threshold marks a critical point whereby theentire colony shifts from an exploration phase to
a commitment/action phase.

Equivalent mechanisms in humangroup decision-making
These mechanisms have remarkable similarities
in human group decision-making. As repeatedly
shown since the original classic studies in social
psychology (Asch, 1951; Davis, 1969, 1973;
Festinger, 1954; Latané & Wolf, 1981; Sherif,1932), humans are highly sensitive to social frequency information. Furthermore, human
responses to social frequency information are
nonlinear. In criminal juries for example, an
undecided juror who has just realized that twoothers are endorsing Guilty and six others areendorsing Not Guilty (the remaining four jurors
including self are silent) is likely to join the Not
Guilty faction in due course, with a probability
greater than 75% (= 6/8; e.g., Hastie et al., 1983).
Theoretical analysis has also shown that such disproportionate “conformity bias” in copying isoften functionally adaptive under uncertainty(e.g., Boyd & Richerson, 1985; Henrich & Boyd,
1998; Kameda & Nakanishi, 2002, 2003; but see
also Kameda, Inukai, Wisdom, & Toyokawa,
2012; Kerr & Tindale, 2011 for maladaptive
examples). As in the animal cases we havereviewed, this process often causes positive feedback loops in human groups as well, ranging
from spread of happiness (Fowler & Christakis,2008) and obesity (Christakis & Fowler, 2007)
across a social network, through proliferation ofcrimes in communities (Glaeser, Sacerdote, &Scheinkman, 1997), emergence of a voting trend
in the conclave for election of the Pope (Allen,
2002), to “herd behavior” in financial markets
and the Internet (Akerlof & Shiller, 2009; Raafat,
Chater, & Frith, 2009).
When official consensus is required, humangroups often rely on some aggregation rule,
which is functionally equivalent to the “quorumrule” in animal consensus-decisions (Conradt &Roper, 2005).2 The aggregation rules in use (e.g.,
unanimity, super-majority, or simple majority)Kameda et al.
depend on the importance of the problem, history, conventions, and other social/political factors, which have been a focus of voting theory insocial sciences (Arrow, 1963; Black, 1958; see,e.g., Laughlin, 2011a; Mueller, 2003; for a general
overview). For instance, the constitutionality ofless-than-unanimous jury convictions in criminalcases was disputed in the 1970s at the US Supreme
Court (Apodaca v Oregon, 1972; Johnson v
Louisiana, 1972), and many jurisdictions in the
US today require the jury to reach a unanimousverdict in criminal cases. Interestingly, however,
Davis, Kerr, Atkin, Holt, and Meek (1975), who
compared mock jury deliberation processes
under unanimous or two-thirds majority rules,
found almost no differences in verdict patterns
between the two official rules; overall verdict distributions in the experiment were best predictedby a two-thirds majority (hung otherwise) model––i.e., the group verdict is predicted to be the alternative favored by at least two-thirds of the jurors
at the outset of deliberations; if there is no suchmajority, the jury ends hung. This finding, alongwith similar results in mock jury and other groupdecision-making studies (see Kerr & Tindale,
2004, for a comprehensive review), suggests thatbehavioral “unanimity” in human groups is often
achieved by majority/plurality aggregation (seeDavis, 1973; Hastie et al., 1983; MacCoun, 1989),
as “consensus” in animal groups is guided by
implicit quorum rules. Hastie and Kameda (2005)
argued that the majority/plurality aggregation
often functions as a heuristic under uncertainty(Gigerenzer et al., 1999), which can solve thespeed-accuracy tradeoff in consensus decisions
efficiently if several key conditions, as we will discuss below, are met (Pratt & Sumpter, 2006; seealso Regenwetter, Ho, & Tsetlin, 2007; Sorkin,
Hays, & West, 2001).

The wisdom of crowds, the Condorcet
Jury Theorem, and the law of large
numbers
The majority/plurality aggregation in human aswell as animal group decision-making, as we have9
observed above, exaggerates any dominant tendency in individual responses at the collective
level (Davis, 1973; Kameda et al., 2003; Kerr,
Stasser, & Davis, 1979). By the multinomial theorem in elementary algebra, it can easily be shownthat such mechanism yields a macro phenomenon known as group polarization in social psychology (Myers & Lamm, 1976), in which a dominantoption at the individual level becomes more dominant at the group level, whereas the non-dominantoptions become even weaker (Davis, 1973;
Kameda & Sugimori, 1995; Kerr, Davis, Meek, &Rissman, 1975; Smoke & Zajonc, 1962). Whenthe number of options in a choice set is two (e.g.,
to migrate or not), this process can be describedby the Condorcet Jury Theorem, formalized by
the Marquis de Condorcet, a French social philosopher of the 18th century (Condorcet,
1785/1994). To illustrate, suppose that a groupwith 2m + 1 members works on some problem
with an objectively true (but unknown) solution.3
The choice set is thus classifiable into binary
behavioral categories, the one correct option vs.
the other incorrect options. Assuming that eachindividual makes a decision independently fromeach other, the probability, PG, that the groupreaches the correct choice by the majority rule isgiven by:PG =2 m+1  2m + 1 ∑ 

n= m + 1

2m − n + 1

 p (1 − p)
n 
n

,

where p is the (average) probability that eachindividual endorses the correct option personally.
And if individual accuracy (p) is greater than .5,
the group accuracy under the majority rule isenhanced above p (i.e., PG > p > .5), a phenomenon known as “the wisdom of crowds”(Surowiecki, 2004). Such a group-level improvement becomes even larger (i.e., PG approaches
nearly 100% accuracy) with an increase in groupsize (e.g., Kameda, 1991; Kerr, MacCoun, &Kramer, 1996; Kerr & Tindale, 2004). Whengroup aggregation is done by averaging (e.g.,
Larrick & Soll, 2006; Oldroyd et al., 2008), a similar group-level improvement is achieved by thelaw of large numbers in statistics (Galton, 1907).

10		
In either case, even if each group member is notvery competent (“many wrongs”: Simons, 2004),
these simple aggregation mechanisms (majority
rule, averaging) can cancel out individual errors
and thus yield more accurate decisions in groups
as compared to isolated individuals (Hastie, 1986;
Hastie & Kameda, 2005; Laughlin, 2011b).

Prerequisites for swarm intelligence
However, this reasoning simultaneously implies
that there are at least three prerequisites for thesimple aggregation properties (majority rule,averaging) to yield good collective results: (a) amoderate level of individual accuracy (e.g., p > .5
above), (b) statistical independence in individual
judgments/evaluations, and (c) mechanisms that
motivate contributions to the collective decision
while discouraging free-riding (Hardin, 1982).
For the first condition (moderate competence ofmembers), group aggregation yields lower-quality
outcomes than the average performance of isolated individuals if group members’ average accuracy is low (e.g., p < .5; Kerr et al., 1996); groupdecision-making is dysfunctional in these cases.
Likewise, if members’ judgments or evaluationsof choice alternatives are constructed understrong mutual influences (violation of the statisticalindependence condition), errors in their judgments will be highly correlated with each other,and mechanisms that would otherwise enhancegroup-level performance over the individual level,
as expected from the Condorcet Jury Theorem or
error cancellation via averaging, will not be effective (Anderson & Holt, 1997; Banerjee, 1992;
Bikhchandani, Hirshleifer, & Welch, 1992). Finally,
if members free-ride on the outcome of others’
efforts without incurring personal costs toimprove the collective endeavor (violation of thecontribution condition), average individual accuracy is lowered and the benefits of grouping may
not materialize (Kameda et al., 2011).
Ants and honeybees seem to be able to solvethese potential problems in collective actions. For
example, as we have seen, ants lay pheromone
trail markings in proportion to the concentrationof sugar solution found (the richer the source,Group Processes & Intergroup Relations
the more pheromone: Beckers et al., 1993); forprey scavenging, the strength of an individual’s
recruitment pheromone trail is inversely proportional to ability to move the prey (Detrain &Deneubourg, 2002). Honeybees also adjust finely
several components of their foraging behavior inaccordance with nectar source profitability: Whenthe quality is higher than some individual thresholds, the bees forage more quickly and dance
more vigorously (Seeley et al., 1991). These finetuned (and genetically acquired) mechanisms
seem to assure that ant/honeybee foragers haveat least moderate individual accuracies (i.e., p > .5)
in most natural cases (though of course suddenchanges in their adaptive environments can work
against such fine-tuned mechanisms).
It should also be noted that ant/honeybee foragers evaluate the quality of food resources ornest sites independently from others. They are certainly affected by other foragers’ actions (pheromone trail makings, waggle dances) in theirdecisions about where to visit. But, simultaneously, they are independent in assessing the quality of the visited site. For example, the durationof the scout bee’s dance, which indexes thestrength of the bee’s preference for the site, is notaffected by others’ waggle dances but determinedsolely by her own perception of the site’s quality(List, Elsholtz, & Seeley, 2009; Seeley, 2010).
Finally, free-riding in collective action is notproblematic in most social insects, as their colonies are largely composed of kin. Helping biological relatives carrying the same genes canincrease inclusive fitness, even if an animal incurs
a cost to own survival and/or reproduction.
Thus, free-riding is much less common in colonies of social insects than in human societies(Hamilton, 1964).

Some future research
directions in human groupdecision-making
So how do the three prerequisites for swarm
intelligence fare in human activities? This question seems to warrant serious theoretical andempirical attention in future research on humanKameda et al.
group decision-making. Here, we consider somefuture research directions among the three prerequisites for collective intelligence.

Average individual accuracy innatural contexts
Compared to the fixed, genetically-determinedalgorithms of ants and honeybees, human cognitive faculties are far more flexible, enabling us toinhabit a wide range of natural environments(from the Arctic to the Gobi Desert), through theuse of material and social devices to cope withvarious adaptive challenges (Gibbons, 1998).
Along with our unique language faculty, such flexible cognitive capacity allows us to be far better
individual learners (and problem solvers) in muchbroader contexts than any other species on earth(Mithen, 1996). Given this, it seems rather implausible that average human individuals are incompetent in ecologically representative (natural) task
settings (though of course average performance
could be poor in evolutionarily novel contexts,
such as when learning mathematics; Gigerenzer
et al., 1999; Kameda & Hastie, 2004; Krueger &Funder, 2004). This reasoning suggests that, insome natural cases where average individuals may
indeed perform badly, the inaccuracies are unlikely
to be attributable to cognitive limitations as individual learners per se. Instead, we believe that suchdecreased individual accuracies are most likely toemerge from the “docility” of our minds (Simon,
1983, 1990), which makes us highly sensitive tosocial nuances (Kameda & Tindale, 2006). In
other words, whether or not a moderate level ofindividual accuracy (the first condition for collective intelligence) is sustained seems to be largely
determined by the other two conditions pertaining to our highly social minds. We will examine thispoint below.

Independence in evaluations
Herbert Simon introduced the term “docility”,
meaning a “propensity to behave in sociallyapproved ways and to refrain from behaving in

11ways that are disapproved” (Simon, 1983, p. 65),
which stems from our fundamental motivation to
obtain information from social channels. Classic
studies in social psychology (Asch, 1951; Sherif,1936) have documented that such tendencies are
particularly pronounced in consensus-seeking
situations, in which everyone in a group is susceptible to the same perceptual or cognitive
errors, yielding high interdependencies in members’ judgments. In addition, members’ responses
to conformity pressure depend on specific social/
cultural contexts (e.g., leadership styles or corporate cultures; Janis, 1972, 1982), and are also vulnerable to subtle normative cues and nuances(e.g., Bateson, Nettle, & Roberts, 2006).
All these observations seem to suggest thathumans may be in some ways even more social
than social insects, that is, even more susceptible
to influences emitted from others. Then the focal
question is whether humans can remain only selectively influenced by others, like honeybees thatcombine dependence (in search) and independence (in evaluation) properly to achieve collective
intelligence in foraging (List et al., 2009). Can
humans show “swarm intelligence” in combineddecision cases as do social insects? This questionis central to many modern mass-behavior contexts (e.g., financial markets, internet shopping)that structurally correspond to combined decisioncases in animals (Conradt & Roper, 2005).
Two recent experiments shed some light on
this question. Salganik, Dodds, and Watts (2006)
conducted a large-scale Internet experiment on a“cultural market”, whereby people downloaded
songs from an artificial music site. In the site, participants were provided social frequency information about how others had decided, while they
could also listen to and examine each song individually. The results showed that people’s preferences for music might not reflect a song’s inherent
quality but were strongly affected by others’ preferences. As a result, the overall popularity rankings of the songs in the market were susceptible
to random fluctuations and were often highly
path-dependent. Toyokawa, Kameda, and Kim(2011) extended Salganik et al. (2006) into a

12		
situation where the quality of choice options
could be defined objectively. These researchers
used a “multi-armed bandit” task (e.g., McElreath
et al., 2008; Schlag, 1998), where participants had
to make choices individually between six “slotmachines” whose expected values were different
from each other but unknown to participants.
Participants played this game repeatedly in groupsof five, and the reward for each participant wasdetermined solely by his/her individual choices;thus this was a case of combined decision as in
Salganik et al. (2006). Besides the payoff feedback from their own individual choices, participants could also view social frequency information
about how many individuals in the group choseeach option in the preceding round, along withaverages of ratings that each participant gave tohis/her chosen option on a 5-point scale (asthose found in Amazon or Yelp). Extrapolatingfrom the honeybee’s behavioral algorithms (Listet al., 2009), the availability of the rating opportunity along with social frequency information
could have potentially allowed the human participants to balance dependence (in choice) andindependence (in evaluation) properly to yield“swarm intelligence” collectively. Results, however, showed that average performances in thegroup condition were no better than those in theisolated individual condition; participants in thegroup condition failed to use the rating capability
productively.
Salganik et al. (2008) and Toyokawa et al.
(2011) suggest that, even in commercial situations where conformity pressure toward consensus is much weaker, maintaining independence inindividual evaluations and choices may not beeasy. Future research that delineates conditions inwhich human individuals can separate out independence in evaluation from dependence insearch will be important both theoretically andpractically (see Kameda et al., 2012, for further
discussions).

Free-riding in collective action
In contrast to most social insects, human groups
often contain non-kin members, which makes theGroup Processes & Intergroup Relations
issue of free-riding central in any collective actionin our societies. Theoretical and empiricalresearch have repeatedly shown that maintaining
high-level cooperation poses major challenges inhuman groups (e.g., Hardin, 1982; Ostrom, 1990).
How does the free-riding problem interfere withfeasibility of the wisdom of crowds in humangroups in particular (Surowiecki, 2004)? This
constitutes a core problem for human collective
intelligence. Conventional mechanisms that havebeen proposed to deal with free-riding in a group
include sanctions against norm-violating freeriders (e.g., Fehr & Fischbacher, 2004a, 2004b),
and reputation systems that promote members’
cooperation (e.g., Nowak & Sigmund, 2005). Yet,
how these conventional mechanisms are applicable to consensus-seeking situations in relation to
emergence of collective intelligence in groups
remains unclear.
Recently, Kameda et al. (2011) approached thefree-rider problem from an unconventional perspective. The task situation they studied was parallel to animal consensus decision cases (Conradt &Roper, 2005), in which six-person groups had toreach a binding group decision under uncertainty(as in the aforementioned multi-armed bandit
situation: Schlag, 1998). Examining incentive(game) structures of the group task, Kameda
et al. (2011) argued that many natural groupdecision tasks are not necessarily social dilemmas(as assumed in the previous literature: Kerr, 1983;
Ledyard, 1995) where free-riding is a dominant
strategy. Instead, they argued that most decisionsituations can be understood as a “producerscrounger game” (Giraldeau & Caraco, 2000)
where cooperators and defectors coexist in agroup in a stable manner. That is, some individuals remain cooperative in contributing to thegroup endeavor (e.g., engaging in costly information search, bearing voting costs) for selfish(rational) reasons, while others loaf; neither cooperation nor free-riding is dominant in the producerscrounger game, yielding a mixed equilibrium in agroup (see Foster, 2004; Motro, 1991 for analytic
details). Kameda et al. (2011) then argued that,even if no sanctioning or reputation systems exist(Fehr & Fischbacher, 2004a, 2004b; Nowak &Kameda et al.
Sigmund, 2005), thanks to those rational cooperators, group decision-making can outperform
other decision mechanisms (e.g., despotism
where the best and brightest individual makes adecision for the group, or decision-making by isolated individuals) under uncertainty. They tested
this prediction by a series of computer simulations and a behavioral experiment. Results supported their prediction, indicating thatmajoritarian decision-making can beat other decision mechanisms in a broad parametric range
under uncertainty. Kameda et al. (2011) called
such superb performances of majoritarian groupdecision-making “democracy under uncertainty.”
These results are suggestive, yet are silent
about how conventional control mechanisms(e.g., sanction or reputation systems) may function in human group decision-making in relation
to collective intelligence. Future research addressing these questions seems to be promising, whichhighlights one of the key differences of humangroups from groups of social insects which arelargely composed of kin.
Research on groups in social psychology has
yielded many key insights about human collective
behavior. Simultaneously, as we have reviewed inthis paper, interests in animal collective behavior
are rapidly growing in biology and related disciplines including information sciences, complex
systems, and so on (Sumpter, 2010). Yet, the language and thinking in the social and biologicalsciences are still disparate enough to hamper
fruitful communication (Conradt & List, 2009).
This state is unfortunate, and it is time for us
social scientists to think about how our knowledge about human collective behavior may belinked to developments in research on animal collective behavior. Such cross-fertilizations havegreat potential for addressing various urgent
problems in our societies, where inaccuracy,interdependence, and free-riding among agentsare leading to new crises and dilemmas (e.g.,
Akerlof & Shiller, 2009).
Notes
Writing of this article was supported by the Grantin-Aid for Scientific Research 17330133 from the13
Ministry of Education, Culture, Sports, Science andTechnology of Japan to the first author. We are grateful to Reid Hastie for his helpful comments on an earlier version of this manuscript.
1	Conradt and Roper (2005) defined group decision
broadly as “a decision made by the animals within agroup” (p. 449, emphasis added), and then proposed the conceptual distinction, combined versus consensus decision, as described above. Noticethat such conceptual distinction along with thebroad definition of group decisions has some parallels in social sciences, particularly in the public
choice literature (e.g., Mueller, 2003). In this literature, the former are often described as interactive
decisions in which people make social decisions(i.e., decisions that affect welfares of others as well
as self) in an interactive manner but with no
explicit binding mechanism. These cases are often
the subject of game theory and have been conceptualized with specific game structures (e.g., public
goods, prisoner’s dilemmas, chicken, stag huntgame). In contrast, the latter are described as
aggregate decisions, which have been the subjectof social choice theory (Arrow, 1963) and ofgroup decision making research in social psychology (Davis, 1973). In these cases, members’ preferences are aggregated into group decisions
explicitly by some institutional mechanism (e.g.,
elections, jury deliberation), and members mustabide by the outcomes even if their preferences
are not consistent with them; behavioral unity as agroup is enforced.
2	In law and other social sciences, a “quorum”
refers to the minimum number of members of adeliberative body necessary to conduct the business of the group. If the quorum is not reached,
the meeting is not official; when the quorum ispresent, decision of the meeting, be it reachedunanimously, through voting by a 2/3 majority
rule, or by a simple majority/plurality rule among
members at the meeting, becomes the official decisionof the entire, deliberative body. It seems thatsome usages of the term “quorum rule” in the
animal group decision-making literature (e.g.,
Sumpter, 2010) may be closer to the aggregation
rule rather than to the quorum rule in the legal
sense. For example, in cases where nest sites
endorsed by smaller factions of scout bees are“outvoted” while the nest site with the greatest
majority of scout bees is chosen as the next home
for the entire swarm (Seeley, 2010), “aggregation

14		
rule” seems to better describe the decision situation than does “quorum rule” in the social scientific usage.
3	In the case where a group has 2m members, we
also have to assume that, if there is an equal split,
the group decision is made randomly between thecorrect and incorrect options. This additional
assumption, however, does not change the general
conclusion to be described in the text.

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