Collective Intelligence and Group Performance

Abstract

We review recent research on collective intelligence, which we define as the ability of a group to perform a wide variety of tasks. We focus on two influences on a group’s collective intelligence: (a) group composition (e.g., the members’ skills, diversity, and intelligence) and (b) group interaction (e.g., structures, processes, and norms). We also call for more research to investigate how social interventions and technological tools can be used to enhance collective intelligence.

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Current Directions in Psychological
Science
2015, Vol. 24(6) 420 –424
© The Author(s) 2015
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DOI: 10.1177/0963721415599543
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Why do some groups perform better than others? One
clearly important factor is the skills of the group mem-
bers. But even groups with comparably skilled members
can have radically different levels of performance.
Considerable work in fields such as social psychology,
organizational behavior, and industrial psychology has
focused on the various factors that predict group perfor-
mance (Hackman, 1987; Ilgen, Hollenbeck, Johnson, &
Jundt, 2005; Larson, 2010). In almost all cases, however,
these studies have focused on a specific task and tried to
characterize what leads most groups to perform well on
that kind of task. In these studies, the differences among
groups within an experimental condition have usually
been treated as undesirable error.
Here, we focus instead on the general ability of a par-
ticular group to perform well across a wide range of dif-
ferent tasks. We call this ability the collective intelligence
of the group, since it is precisely analogous to intelli-
gence at the individual level. When individuals perform a
wide variety of different cognitive tasks, psychologists
have repeatedly found that a single statistical factor pre-
dicts much of the variance in their performance (e.g.,
Deary, 2012; Spearman, 1904). This factor is often called
general intelligence, or g. But, perhaps surprisingly, until
recently none of the research on group performance had
systematically examined whether a similar kind of
“collective intelligence” exists for groups of people. Our
recent research sought to address this gap.
In our initial studies, we found converging evidence of
a general collective-intelligence factor that predicts a
group’s performance on a wide variety of tasks (Woolley,
Chabris, Pentland, Hashmi, & Malone, 2010). The groups
in our studies ranged in size from two to five members
and spent approximately 5 hours together in our labora-
tory, working on a series of tasks that required a range of
qualitatively different collaboration processes (McGrath,
1984). The tasks included creative brainstorming prob-
lems, puzzles involving verbal or mathematical reason-
ing, negotiation tasks, and moral-reasoning problems. A
factor analysis of the groups’ scores on all of these tasks
revealed a single dominant factor explaining 43% of the
variance in performance. This is consistent with the 30%
to 50% of variance typically explained by the first factor
derived from the scores of individuals doing many differ-
ent cognitive tasks (Chabris, 2007). In individuals, this
factor is called intelligence. For groups, we call this factor
599543CDPXXX10.1177/0963721415599543Woolley et al.Collective Intelligence and Group Performance
research-article2015
Corresponding Author:
Anita Williams Woolley, Tepper School of Business, Carnegie Mellon
University, 5000 Forbes Ave., Pittsburgh, PA 15217
E-mail: awoolley@cmu.edu
Collective Intelligence and
Group Performance
Anita Williams Woolley1, Ishani Aggarwal2, and
Thomas W. Malone3,4
1Tepper School of Business, Carnegie Mellon University; 2Brazilian School of Public and Business Administration,
Fundação Getulio Vargas; 3Sloan School of Management, Massachusetts Institute of Technology; and 4Center for
Collective Intelligence, Massachusetts Institute of Technology
Abstract
We review recent research on collective intelligence, which we define as the ability of a group to perform a wide
variety of tasks. We focus on two influences on a group’s collective intelligence: (a) group composition (e.g., the
members’ skills, diversity, and intelligence) and (b) group interaction (e.g., structures, processes, and norms). We also
call for more research to investigate how social interventions and technological tools can be used to enhance collective
intelligence.
Keywords
collective intelligence, group performance, group composition, group, process
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Collective Intelligence and Group Performance 421
collective intelligence, or c, and it is a measure of the
general effectiveness of a group on a wide range of tasks.
In addition to the tasks used to calculate c, we gave
each group a more complex criterion task, which
required a combination of several of the different col-
laboration processes measured by the other tasks. In the
first study, groups played checkers as a team against a
computer opponent. In the second study, groups com-
pleted an architectural design problem. As expected, we
found that c was a significant predictor of group perfor-
mance on both of these criterion tasks, and—surpris-
ingly—the average individual intelligence of group
members was not. At least twice as much variance in
performance was predicted by c as by individual
intelligence.
More recent work has replicated these basic findings
in both face-to-face and online groups (Engel, Woolley,
Jing, Chabris, & Malone, 2014), in groups of MBA stu-
dents working together over the course of a semester
(Aggarwal & Woolley, 2014), in online gaming groups
(Kim etal., 2015), and in groups from multiple cultures
(Engel etal., 2015). Taken together, these results provide
strong support for the existence of a general collective-
intelligence factor that predicts the performance of a
group on a wide range of tasks.
What Predicts Collective Intelligence?
Existing research suggests that group collective intelli-
gence is likely to be an emergent property that results
from both bottom-up and top-down processes. Bottom-up
processes involve the aggregation of group-member
characteristics that contribute to and enhance group col-
laboration. Top-down processes include group struc-
tures, norms, and routines that regulate collective
behavior in ways that enhance (or detract from) the qual-
ity of coordination and collaboration. These bottom-up
and top-down aspects of groups both interact and com-
bine to produce collective intelligence. We now discuss
each in turn.
Bottom-up compositional features
enabling collective intelligence
Previously, when intelligence was examined at all in
groups, it was analyzed as a function of the individual
intelligence of the group members. Research found that
groups whose members had higher average individual
intelligence were generally better able to adapt to a
changing environment and to learn new information
(e.g., Ellis etal., 2003; LePine, 2005), but this effect was
not consistently strong in the laboratory, and it was even
weaker in field settings (Devine & Philips, 2001).
In the studies of collective intelligence described
above, it was also found that the average and maximum
intelligence of individual group members was correlated
with c, but only moderately so. So, having a group of
smart people is not enough, alone, to make a smart
group. But if having smart people is not enough to make
a group smart, what is?
A much stronger predictor of c was the average social
perceptiveness of group members, as measured by the
Reading the Mind in the Eyes (RME) Test (Baron-Cohen,
Wheelwright, Hill, Raste, & Plumb, 2001). This test mea-
sures people’s ability to judge others’ emotions from
looking only at pictures of their eyes. Groups with a high
average score on this test were more collectively intelli-
gent than other groups.
We also found that the proportion of women in the
group was a significant predictor of c. However, this
result was largely explained statistically by the fact that
women, on average, score higher on tests like the RME
than men. So, it may be that what is needed for a group
to be collectively intelligent is a number of people who
are high in social perceptiveness. And if a group is made
up of highly socially perceptive people, then it may not
matter much whether they are men or women. When we
tried to predict collective intelligence from a group’s
average social perceptiveness, the percentage of women
in the group, and the distribution of speaking turns (dis-
cussed further below), we found that all three factors had
similar predictive power for c, but only the predictive
power of social perceptiveness was statistically signifi-
cant (Woolley etal., 2010).
In a study of online groups (Engel etal., 2014), we
found that social perceptiveness and proportion of
women were just as highly correlated with c as they
were in face-to-face groups. This is particularly remark-
able in light of the fact that the online groups were com-
municating only via text chat and could not even see
each other’s nonverbal expressions. This suggests that
even though the RME test is based on visual cues in
faces, it must also be predictive of a broader range of
interpersonal skills that are useful even when people
cannot see each other’s faces. Since members of the
online groups did not know who else was in their group,
it is unlikely that knowledge of team members’ gender
changed participants’ behavior.
Another aspect of group composition that has been
related to c is the level of diversity in the group. In gen-
eral, groups performing creative or innovative tasks often
benefit from diversity, while groups performing tasks for
which efficiency is important are often impaired by diver-
sity (Williams & O’Reilly, 1998). Cognitive diversity,
including thinking styles and perspectives (Kozhevnikov,
Evans, & Kosslyn, 2014), is of particular relevance to
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422 Woolley et al.
collective intelligence, as it relates directly to group mem-
bers’ ability to communicate with each another.
In a recent study (Aggarwal, Woolley, Chabris, &
Malone, 2015), we found a curvilinear, inverted U-shaped
relationship between cognitive-style diversity and collec-
tive intelligence. In other words, groups that were mod-
erately diverse in cognitive styles did better than those
that were very similar in cognitive styles and also those
that were very different. This suggests that groups whose
members are too similar to each other lack the variety of
perspectives and skills needed to perform well on a vari-
ety of tasks. But at the same time, groups whose mem-
bers are too different have difficulties communicating
and coordinating effectively (Aggarwal & Woolley,
2013a). So, an intermediate level of cognitive diversity
appears to be best for enhancing collective intelligence
(Aggarwal & Woolley, 2013b).
Taken together, these findings suggest that the indi-
vidual skills most critical for collective intelligence are
those that enhance the ability of group members to col-
laborate effectively or that enrich the collaboration by
bringing a sufficient diversity of perspectives.
Top-down interaction processes
In addition to the basic ingredients of member skills, col-
lective intelligence is enabled by the group interactions
that combine those skills to good effect. But we know
less, so far, about these interaction processes than about
the skills that go into them. In fact, there is an interesting
analogy between individual and collective intelligence in
this regard. Psychologists discovered the statistical factor
(g) for individual intelligence long before they knew
what actual processes in the brain were associated with
this factor, and even today, we still have only a limited
understanding of the neural processes that allow some
people to be more intelligent than others (Gray, Chabris,
& Braver, 2003). Similarly, with collective intelligence, we
know some things about the group processes of collec-
tively intelligent groups, but we are still far from a com-
plete process theory that explains why some groups are
more intelligent than others.
The most important things we have observed so far
are that more collectively intelligent groups communi-
cate more and participate more equally than other
groups. For instance, we have found that collective intel-
ligence was significantly predicted by the total amounts
of spoken communication in face-to-face groups and of
written communication in online groups (Engel et al.,
2014). We also found that collective intelligence was pre-
dicted by how equally communication and work contri-
bution were distributed among group members in both
face-to-face and online groups (Engel etal., 2014; Kim
etal., 2015; Woolley etal., 2010). In other words, groups
in which one or two people dominated the activity were,
in general, less collectively intelligent than those in
which the activity was more equally spread among
group members.
Conceptually, these findings seem reasonable, since
groups in which people communicate more and partici-
pate more equally are more likely to be able to take
advantage of the full knowledge and skills of all their
members. But, in contradiction to the mainstream litera-
ture on team performance, we have also found (Engel
etal., 2014; Kim etal., 2015; Woolley etal., 2010) that
collective intelligence is not predicted by several other
factors that previous research suggested might be predic-
tive of well-functioning groups, including group satisfac-
tion (De Dreu & Weingart, 2003), social cohesiveness
(Stokes, 1983), and psychological safety (i.e., the shared
belief that it is safe for the team to take interpersonal
risks; Edmondson, 1999). This suggests that collective
intelligence is something distinct from a metric of rela-
tionship quality in groups.
Taken together, the existing studies of collective intel-
ligence suggest that bottom-up, compositional features of
a group combine with top-down interactional processes
to affect the emergence of collective intelligence. But
more research is needed to understand these interac-
tional processes in more detail, creating a ripe area for
future work.
What Does Collective Intelligence
Predict?
As we saw above, collective intelligence predicts a
group’s performance on other—more complex—tasks
that were not used in calculating the original collective-
intelligence score (Woolley et al., 2010). Perhaps even
more interestingly, there is a striking parallel between
how intelligence is related to learning in individuals and
groups. It is well established that more intelligent indi-
viduals learn new material more quickly (Jensen, 1989).
Recent studies have suggested a similar relationship
between collective intelligence and learning for groups
as well.
In one study (Aggarwal & Woolley, 2014), collective
intelligence was measured in teams of students in a man-
agement course, and then their performance on a series
of group tests was tracked over the next 2 months. The
teams that were highly collectively intelligent earned sig-
nificantly higher scores on their group assignments even
though their members did not do any better on the indi-
vidual assignments. Furthermore, the highly collectively
intelligent teams exhibited steady improvement in perfor-
mance across the series of tests, suggesting that the teams
got better at retaining information collectively and apply-
ing it to their assignments over time.
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Collective Intelligence and Group Performance 423
In a second study, we measured groups’ collective
intelligence and then asked them to play a behavioral-
economics game called the minimum-effort tacit coordi-
nation game (Aggarwal et al., 2015). In this game, the
group members each chose from among a set of options.
They could not communicate about which options they
were choosing, but their payoff was determined by a
combination of what they individually chose and what
the other group members chose. Groups that did well at
anticipating what other members in their group would
choose, and tacitly coordinated their choices accordingly,
earned more. We found that a group’s collective intelli-
gence was highly predictive of its improvement over the
10 rounds of the game and its earnings overall.
Conclusions
Taken together, the research described here demonstrates
the existence of a measurable collective intelligence in
groups that is analogous to general intelligence in indi-
viduals. This collective intelligence emerges from a com-
bination of bottom-up and top-down processes within
groups and predicts future performance and learning in
a wide range of environments.
Just as the concept of individual intelligence gave us
tools for better understanding education, job perfor-
mance, and many other aspects of life, we suspect that
the concept of collective intelligence may be helpful for
understanding many aspects of group performance. It
may, for instance, help researchers study group phenom-
ena by providing better ways of controlling for the differ-
ences among teams when studying the effects of
particular treatments.
But much remains to be understood about collective
intelligence. For instance, what are the basic processes of
group interaction that lead some groups to be more col-
lectively intelligent than others? How stable is collective
intelligence over time?
One particularly important area for future research that
is related to the stability of collective intelligence is whether
we can increase the collective intelligence of groups.
While it is generally very hard to increase the intelligence
of an individual (at least beyond early childhood), it seems
eminently possible to do this for groups. This raises sev-
eral questions for future research—for instance, how can
changes in group structure or group norms increase the
collective intelligence of a group? How can new kinds of
electronic collaboration and communication tools enhance
collective intelligence? Can forcing the members of groups
to engage in equal communication raise their collective
intelligence? Would amplifying social cues level the play-
ing field and render social perceptiveness less important?
We see no shortage of possibilities for how social systems
might be structured to support higher levels of collective
intelligence, providing many fertile areas for ongoing
research.
Recommended Reading
Bear, J. B., & Woolley, A. W. (2011). The role of gender in team
collaboration and performance. Interdisciplinary Science
Reviews, 36, 146–53. A brief review of the effects of gender
composition on team processes.
Larson, J. R. (2010). (See References). A more extensive review
of the conditions that elicit synergistic gains in teams.
Woolley, A. W., Aggarwal, I., & Malone, T. W. (2015). Collective
intelligence in teams and organizations. In T. W. Malone &
M. S. Bernstein (Eds.), Collective intelligence. Cambridge,
MA: MIT Press. A comprehensive but accessible overview
of the features of teams and organizations that affect col-
lective intelligence.
Woolley, A. W., Chabris, C. F., Pentland, A., Hashmi, N., &
Malone, T. W. (2010). (See References). Provides more
information on the original study of collective intelligence
in teams.
Acknowledgments
We wish to thank our collaborators, including David Engel,
Christopher Chabris, Lisa Jing, and Nada Hashmi, along with
many research assistants at Carnegie Mellon University and MIT
for their efforts and contributions to the work described.
Declaration of Conflicting Interests
The MIT Center for Collective Intelligence has received spon-
sorship funding from Cisco Systems, Inc.
Funding
The work described in this article was made possible by finan-
cial support from the National Science Foundation (Grants IIS-
0963285, ACI-1322254, and IIS-0963451), the U.S. Army
Research Office (Grants 56692-MA and 64079-NS), and Cisco
Systems, Inc., through their sponsorship of the MIT Center for
Collective Intelligence.
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