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Wisdom of crowds versus groupthink: learning in groups and in isolation

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  • Conor Mayo-Wilson
  • Kevin Zollman
  • David Danks

Abstract

We evaluate the asymptotic performance of boundedly-rational strategies in multi-armed bandit problems, where performance is measured in terms of the tendency (in the limit) to play optimal actions in either (i) isolation or (ii) networks of other learners. We show that, for many strategies commonly employed in economics, psychology, and machine learning, performance in isolation and performance in networks are essentially unrelated. Our results suggest that the performance of various, common boundedly-rational strategies depends crucially upon the social context (if any) in which such strategies are to be employed. Copyright Springer-Verlag 2013

Suggested Citation

  • Conor Mayo-Wilson & Kevin Zollman & David Danks, 2013. "Wisdom of crowds versus groupthink: learning in groups and in isolation," International Journal of Game Theory, Springer;Game Theory Society, vol. 42(3), pages 695-723, August.
    • Handle: RePEc:spr:jogath:v:42:y:2013:i:3:p:695-723
    DOI: 10.1007/s00182-012-0329-7
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    References listed on IDEAS

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    Cited by:

    1. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    2. Brian H Spitzberg, 2018. "Framing the Game: An Architectonic Analogue for Meta-Theorizing Academic Activities," Studies in Media and Communication, Redfame publishing, vol. 6(1), pages 11-25, June.
    3. Namjun Cha & Junseok Hwang & Eungdo Kim, 2020. "The optimal knowledge creation strategy of organizations in groupthink situations," Computational and Mathematical Organization Theory, Springer, vol. 26(2), pages 207-235, June.

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