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.
Why do some groups perform better than others? One clearly important factor is the skills of the group members. 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 performance (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 particular group to perform well across a wide range of different tasks. We call this ability the collective intelligence of the group, since it is precisely analogous to intelligence at the individual level. When individuals perform a wide variety of different cognitive tasks, psychologists have repeatedly found that a single statistical factor predicts 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 laboratory, working on a series of tasks that required a range of qualitatively different collaboration processes (McGrath, 1984). The tasks included creative brainstorming problems, puzzles involving verbal or mathematical reasoning, 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 different cognitive tasks (Chabris, 2007). In individuals, this factor is called intelligence. For groups, we call this factor 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 collaboration 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 completed an architectural design problem. As expected, we found that c was a significant predictor of group performance on both of these criterion tasks, and—surprisingly—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 students 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 intelligence 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 collaboration. Top-down processes include group structures, norms, and routines that regulate collective behavior in ways that enhance (or detract from) the quality of coordination and collaboration. These bottom-up and top-down aspects of groups both interact and combine 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 measures 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 intelligent 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 (discussed further below), we found that all three factors had similar predictive power for c, but only the predictive power of social perceptiveness was statistically significant (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 remarkable in light of the fact that the online groups were communicating 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 general, groups performing creative or innovative tasks often benefit from diversity, while groups performing tasks for which efficiency is important are often impaired by diversity (Williams & O’Reilly, 1998). Cognitive diversity, including thinking styles and perspectives (Kozhevnikov, Evans, & Kosslyn, 2014), is of particular relevance to collective intelligence, as it relates directly to group members’ 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 collective intelligence. In other words, groups that were moderately 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 variety of tasks. But at the same time, groups whose members 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 individual skills most critical for collective intelligence are those that enhance the ability of group members to collaborate 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, collective 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 collectively intelligent groups, but we are still far from a complete 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 communicate more and participate more equally than other groups. For instance, we have found that collective intelligence 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 predicted by how equally communication and work contribution 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 participate 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 literature 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 predictive of well-functioning groups, including group satisfaction (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 relationship quality in groups.
Taken together, the existing studies of collective intelligence 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 interactional 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 individuals 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 management 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 significantly higher scores on their group assignments even though their members did not do any better on the individual assignments. Furthermore, the highly collectively intelligent teams exhibited steady improvement in performance across the series of tests, suggesting that the teams got better at retaining information collectively and applying it to their assignments over time.
In a second study, we measured groups’ collective intelligence and then asked them to play a behavioral-economics game called the minimum-effort tacit coordination 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 intelligence 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 individuals. This collective intelligence emerges from a combination 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 performance, 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 phenomena by providing better ways of controlling for the differences 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 collectively 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 several 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 playing 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.
Footnotes
Acknowledgements
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 sponsorship funding from Cisco Systems, Inc.
Funding
The work described in this article was made possible by financial 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.