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Social learning strategies regulate the wisdom and madness of interactive crowds

Author

Listed:
  • Wataru Toyokawa

    (University of St Andrews
    Japan Society for the Promotion of Science
    The Graduate University for Advanced Studies)

  • Andrew Whalen

    (University of St Andrews)

  • Kevin N. Laland

    (University of St Andrews)

Abstract

Why groups of individuals sometimes exhibit collective ‘wisdom’ and other times maladaptive ‘herding’ is an enduring conundrum. Here we show that this apparent conflict is regulated by the social learning strategies deployed. We examined the patterns of human social learning through an interactive online experiment with 699 participants, varying both task uncertainty and group size, then used hierarchical Bayesian model fitting to identify the individual learning strategies exhibited by participants. Challenging tasks elicit greater conformity among individuals, with rates of copying increasing with group size, leading to high probabilities of herding among large groups confronted with uncertainty. Conversely, the reduced social learning of small groups, and the greater probability that social information would be accurate for less-challenging tasks, generated ‘wisdom of the crowd’ effects in other circumstances. Our model-based approach provides evidence that the likelihood of collective intelligence versus herding can be predicted, resolving a long-standing puzzle in the literature.

Suggested Citation

  • Wataru Toyokawa & Andrew Whalen & Kevin N. Laland, 2019. "Social learning strategies regulate the wisdom and madness of interactive crowds," Nature Human Behaviour, Nature, vol. 3(2), pages 183-193, February.
  • Handle: RePEc:nat:nathum:v:3:y:2019:i:2:d:10.1038_s41562-018-0518-x
    DOI: 10.1038/s41562-018-0518-x
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    Cited by:

    1. Morin, Olivier & Jacquet, Pierre O. & Vaesen, Krist & Acerbi, Alberto, 2020. "Social information use and social information waste," SocArXiv rqcdf, Center for Open Science.
    2. Liu, Qiujia & Lu, Linjun & Zhang, Yijing & Hu, Miaoqing, 2022. "Modeling the dynamics of pedestrian evacuation in a complex environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    3. Rajiv Sethi & Julie Seager & Emily Cai & Daniel M. Benjamin & Fred Morstatter, 2021. "Models, Markets, and the Forecasting of Elections," Papers 2102.04936, arXiv.org, revised May 2021.
    4. Robin Watson & Thomas J. H. Morgan & Rachel L. Kendal & Julie Van de Vyver & Jeremy Kendal, 2021. "Social Learning Strategies and Cooperative Behaviour: Evidence of Payoff Bias, but Not Prestige or Conformity, in a Social Dilemma Game," Games, MDPI, vol. 12(4), pages 1-26, November.
    5. Li Zhenpeng & Tang Xijin, 2021. "Stimuli strategy and learning dynamics promote the wisdom of crowds," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(12), pages 1-8, December.
    6. Bryce Morsky & Fuwei Zhuang & Zuojun Zhou, 2023. "Social and individual learning in the Minority Game," Papers 2307.11846, arXiv.org, revised Mar 2024.

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