• DOI:10.1073/pnas.2005737118
• Corpus ID: 234769649

Quantifying collective intelligence in human groups

@article{Riedl2021QuantifyingCI,
  title={Quantifying collective intelligence in human groups},
  author={Christoph Riedl and Young Ji Kim and Pranav Gupta and Thomas W. Malone and Anita Williams Woolley},
  journal={Proceedings of the National Academy of Sciences},
  year={2021},
  volume={118}
}

• Christoph Riedl, Young Ji Kim, +2 authors A. Woolley
• Published 17 May 2021
• Psychology
• Proceedings of the National Academy of Sciences

Significance Collective intelligence (CI) is critical to solving many scientific, business, and other problems. We find strong support for a general factor of CI using meta-analytic methods in a dataset comprising 22 studies, including 5,279 individuals in 1,356 groups. CI can predict performance in a range of out-of-sample criterion tasks. CI, in turn, is most strongly predicted by group collaboration process, followed by individual skill and group composition. The proportion of women in a group is a significant predictor of group performance, mediated by social perceptiveness. 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.