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Quantifying collective intelligence in human groups

Author

Listed:
  • Christoph Riedl

    (D’Amore–McKim School of Business, Northeastern University, Boston, MA 02115; Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115; Network Science Institute, Northeastern University, Boston, MA 02115; 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)

  • 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)

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.

Suggested Citation

  • Christoph Riedl & Young Ji Kim & Pranav Gupta & Thomas W. Malone & Anita Williams Woolley, 2021. "Quantifying collective intelligence in human groups," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(21), pages 2005737118-, May.
    • Handle: RePEc:nas:journl:v:118:y:2021:p:e2005737118
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    Cited by:

    1. Tang, Ming & Liao, Huchang, 2024. "Group efficiency and individual fairness tradeoff in making wise decisions," Omega, Elsevier, vol. 124(C).
    2. Wilhelm, Oliver & Kyllonen, Patrick, 2021. "To predict the future, consider the past: Revisiting Carroll (1993) as a guide to the future of intelligence research," Intelligence, Elsevier, vol. 89(C).

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