Quantifying collective intelligence in human groups

Christoph Riedl, Young Ji Kim, Pranav Gupta, Thomas W. Malone and Anita Williams Woolley
Additional contact information
Young Ji Kim: Department of Communication, University of California, Santa Barbara, CA 93106
Pranav Gupta: Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213
Thomas W. Malone: Massachusetts Institute of Technology Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142; Massachusetts Institute of Technology Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, MA 02142
Anita Williams Woolley: Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 21, e2005737118

Abstract: 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.

Keywords: collective intelligence; human groups; team performance (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:118:y:2021:p:e2005737118

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