In this paper we propose a new model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. We do this by first characterizing what sets team cognition and collectively intelligence apart, and then reviewing the literature on “superforecasting” and the ability for effectively coordinated teams to outperform predictions by large groups. Lastly, we delve into the ways in which teamwork can be enhanced by artificial intelligence through our model, finally highlighting the many areas of research worth exploring through interdisciplinary efforts.
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How Approaches to Animal Swarm Intelligence Can Improve the Study of Collective Intelligence in Human Teams
Researchers of team behavior have long been interested in the essential components of effective teamwork. Much existing research focuses on examining correlations between team member traits, team processes, and team outcomes, such as collective intelligence or team performance. However, these approaches are insufficient for providing insight into the dynamic, causal mechanisms through which the components of teamwork interact with one another and impact the emergence of team outcomes. Advances in the field of animal behavior have enabled a precise understanding of the behavioral mechanisms that enable groups to perform feats that surpass the capabilities of the individuals that comprise them. In this manuscript, we highlight how studies of animal swarm intelligence can inform research on collective intelligence in human teams. By improving the ability to obtain precise, time-varying measurements of team behaviors and outcomes and building upon approaches used in studies of swarm intelligence to analyze and model individual and group-level behaviors, researchers can gain insight into the mechanisms underlying the emergence of collective intelligence. Such understanding could inspire targeted interventions to improve team effectiveness and support the development of a comparative framework of group-level intelligence in animal and human groups.
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- Award ID(s):
- 1910117
- PAR ID:
- 10160163
- Date Published:
- Journal Name:
- Journal of Intelligence
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2079-3200
- Page Range / eLocation ID:
- 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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