Quantifying collective intelligence in human groups.

Collective intelligence (CI) is critical to solving many scientific, business, and other problems, but groups often fail to achieve it. Here, we analyze data on group performance from 22 studies, including 5,279 individuals in 1,356 groups. Our results support the conclusion that a robust CI factor characterizes a group's ability to work together across a diverse set of tasks. We further show that CI is predicted by the proportion of women in the group, mediated by average social perceptiveness of group members, and that it predicts performance on various out-of-sample criterion tasks. We also find that, overall, group collaboration process is more important in predicting CI than the skill of individual members.

The Impact of Cognitive Style Diversity on Implicit Learning in Teams.

Organizations are increasingly looking for ways to reap the benefits of cognitive diversity for problem solving. A major unanswered question concerns the implications of cognitive diversity for longer-term outcomes such as team learning, with its broader effects on organizational learning and productivity. We study how cognitive style diversity in teams-or diversity in the way that team members encode, organize and process information-indirectly influences team learning through collective intelligence, or the general ability of a team to work together across a wide array of tasks. Synthesizing several perspectives, we predict and find that cognitive style diversity has a curvilinear-inverted U-shaped-relationship with collective intelligence. Collective intelligence is further positively related to the rate at which teams learn, and is a mechanism guiding the indirect relationship between cognitive style diversity and team learning. We test the predictions in 98 teams using ten rounds of the minimum-effort tacit coordination game. Overall, this research advances our understanding of the implications of cognitive diversity for organizations and why some teams demonstrate high levels of team learning in dynamic situations while others do not.

Dynamics of collective performance in collaboration networks.

Today, many complex tasks are assigned to teams, rather than individuals. One reason for teaming up is expansion of the skill coverage of each individual to the joint team skill set. However, numerous empirical studies of human groups suggest that the performance of equally skilled teams can widely differ. Two natural question arise: What are the factors defining team performance? and How can we best predict the performance of a given team on a specific task? While the team members' task-related capabilities constrain the potential for the team's success, the key to understanding team performance is in the analysis of the team process, encompassing the behaviors of the team members during task completion. In this study, we extend the existing body of research on team process and prediction models of team performance. Specifically, we analyze the dynamics of historical team performance over a series of tasks as well as the fine-grained patterns of collaboration between team members, and formally connect these dynamics to the team performance in the predictive models. Our major qualitative finding is that higher performing teams have well-connected collaboration networks-as indicated by the topological and spectral properties of the latter-which are more robust to perturbations, and where network processes spread more efficiently. Our major quantitative finding is that our predictive models deliver accurate team performance predictions-with a prediction error of 15-25%-on a variety of simple tasks, outperforming baseline models that do not capture the micro-level dynamics of team member behaviors. We also show how to use our models in an application, for optimal online planning of workload distribution in an organization. Our findings emphasize the importance of studying the dynamics of team collaboration as the major driver of high performance in teams.

Integrated information as a metric for group interaction.

Researchers in many disciplines have previously used a variety of mathematical techniques for analyzing group interactions. Here we use a new metric for this purpose, called "integrated information" or "phi." Phi was originally developed by neuroscientists as a measure of consciousness in brains, but it captures, in a single mathematical quantity, two properties that are important in many other kinds of groups as well: differentiated information and integration. Here we apply this metric to the activity of three types of groups that involve people and computers. First, we find that 4-person work groups with higher measured phi perform a wide range of tasks more effectively, as measured by their collective intelligence. Next, we find that groups of Wikipedia editors with higher measured phi create higher quality articles. Last, we find that the measured phi of the collection of people and computers communicating on the Internet increased over a recent six-year period. Together, these results suggest that integrated information can be a useful way of characterizing a certain kind of interactional complexity that, at least sometimes, predicts group performance. In this sense, phi can be viewed as a potential metric of effective group collaboration.

Reading the Mind in the Eyes or reading between the lines? Theory of Mind predicts collective intelligence equally well online and face-to-face.

Recent research with face-to-face groups found that a measure of general group effectiveness (called "collective intelligence") predicted a group's performance on a wide range of different tasks. The same research also found that collective intelligence was correlated with the individual group members' ability to reason about the mental states of others (an ability called "Theory of Mind" or "ToM"). Since ToM was measured in this work by a test that requires participants to "read" the mental states of others from looking at their eyes (the "Reading the Mind in the Eyes" test), it is uncertain whether the same results would emerge in online groups where these visual cues are not available. Here we find that: (1) a collective intelligence factor characterizes group performance approximately as well for online groups as for face-to-face groups; and (2) surprisingly, the ToM measure is equally predictive of collective intelligence in both face-to-face and online groups, even though the online groups communicate only via text and never see each other at all. This provides strong evidence that ToM abilities are just as important to group performance in online environments with limited nonverbal cues as they are face-to-face. It also suggests that the Reading the Mind in the Eyes test measures a deeper, domain-independent aspect of social reasoning, not merely the ability to recognize facial expressions of mental states.

Evidence for a collective intelligence factor in the performance of human groups.

Psychologists have repeatedly shown that a single statistical factor--often called "general intelligence"--emerges from the correlations among people's performance on a wide variety of cognitive tasks. But no one has systematically examined whether a similar kind of "collective intelligence" exists for groups of people. In two studies with 699 people, working in groups of two to five, we find converging evidence of a general collective intelligence factor that explains a group's performance on a wide variety of tasks. This "c factor" is not strongly correlated with the average or maximum individual intelligence of group members but is correlated with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group.

In praise of the incomplete leader.

Today's top executives are expected to do everything right, from coming up with solutions to unfathomably complex problems to having the charisma and prescience to rally stakeholders around a perfect vision of the future. But no one leader can be all things to all people. It's time to end the myth of the complete leader, say the authors. Those at the top must come to understand their weaknesses as well as their strengths. Only by embracing the ways in which they are incomplete can leaders fill in the gaps in their knowledge with others' skills. The incomplete leader has the confidence and humility to recognize unique talents and perspectives throughout the organization--and to let those qualities shine. The authors' work studying leadership over the past six years has led them to develop a framework of distributed leadership. Within that model, leadership consists of four capabilities: sensemaking, relating, "visioning," and inventing. Sensemaking involves understanding and mapping the context in which a company and its people operate. A leader skilled in this area can quickly identify the complexities of a given situation and explain them to others. The second capability, relating, means being able to build trusting relationships with others through inquiring (listening with intention), advocating (explaining one's own point of view), and connecting (establishing a network of allies who can help a leader accomplish his or her goals). Visioning, the third capability, means coming up with a compelling image of the future. It is a collaborative process that articulates what the members of an organization want to create. Finally, inventing involves developing new ways to bring that vision to life. Rarely will a single person be skilled in all four areas. That's why it's critical that leaders find others who can offset their limitations and complement their strengths. Those who don't will not only bear the burden of leadership alone but will find themselves at the helm of an unbalanced ship.

Bringing the market inside.

During the dot-com boom, many people saw the potential for new communication technologies to enable radically new business models, but they were far too optimistic about the speed with which the revolution would occur. Now, as the bitter disillusionment of the dot-com bust begins to fade, we have a chance to think again--this time more rationally--about how best to take advantage of the remarkable changes these new technologies are gradually making possible. One such change is the ability to create markets inside companies, allowing decision making to be decentralized and introducing some of the efficiency, flexibility, and motivating influence of free markets. In this article, the author examines this nascent form of business organization, exploring the benefits as well as the potential risks. BP, for example, met its goal of reducing the company's greenhouse gas emissions nine years ahead of schedule, not by setting and enforcing targets for each division but by allowing business unit heads to buy and sell emissions permits among themselves using an electronic trading system. And Hewlett-Packard recently experimented with a system that allowed employees to buy and sell predictions about likely printer sales, using a kind of futures contract. The markets ended up predicting the actual printer sales with much more accuracy than official HP forecasts. At a fundamental level, these changes are enabled by the fact that electronic technologies allow information to be widely shared at little cost. This simple fact has a profound implication for organizing businesses. When more people have more information, they can use it to make their own well-informed decisions, appropriate to local circumstances, instead of following orders from above. As a result, even very large companies can benefit from the collective wisdom of their employees.