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Social learning strategies regulate the wisdom and madness of interactive crowds

Nature Human Behaviourvolume 3pages183193 (2019) | Download Citation

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.

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Code availability

The browser-based online task was built by Node.js (https://nodejs.org/en/) and socket.io (https://socket.io), and the codes are available from GitHub (https://github.com/WataruToyokawa/MultiPlayerThreeArmedBanditGame). Analyses were conducted in R (https://www.r-project.org) and simulations of individual-based models were conducted in Mathematica (https://www.wolfram.com), and the codes for both of these are available from GitHub (https://github.com/WataruToyokawa/ToyokawaWhalenLaland2018).

Data availability

Both experimental and simulation data are available in an online repository (https://github.com/WataruToyokawa/ToyokawaWhalenLaland2018).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

This experiment was supported by The John Templeton Foundation (40128 to K.N.L.) and Suntory Foundation research support (2015-311 to W.T.). The computer simulations and computational model analyses were supported by JSPS overseas research fellowships (H27-11 to W.T.). The phenomenological model analyses were supported by JSPS KAKENHI (grant number 17J01559). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Affiliations

  1. School of Biology, University of St Andrews, St Andrews, UK

    • Wataru Toyokawa
    • , Andrew Whalen
    •  & Kevin N. Laland
  2. Japan Society for the Promotion of Science, Tokyo, Japan

    • Wataru Toyokawa
  3. Department of Evolutionary Studies of Biosystems, School of Advanced Sciences, The Graduate University for Advanced Studies, Hayama, Japan

    • Wataru Toyokawa

Authors

  1. Search for Wataru Toyokawa in:

  2. Search for Andrew Whalen in:

  3. Search for Kevin N. Laland in:

Contributions

W.T., A.W. and K.N.L. planned the study and built the computational model. W.T. ran the simulations. W.T. and A.W. made the experimental material, ran the web-based experiment and collected the experimental data. W.T., A.W. and K.N.L. analysed the data and wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Wataru Toyokawa.

Supplementary information

  1. Supplementary Information

    Supplementary Methods, Supplementary Notes, Supplementary Figures 1–10, and Supplementary Tables 1–10.

  2. Reporting Summary

  3. Supplementary Video 1

    Brief instructions at Amazon’s Mechanical Turk, informed consent form, task instructions and tutorial.

  4. Supplementary Video 2

    Waiting room and task onset. The task starts before reaching the maximum waiting time (5 minutes). Group size is 3 in this example.

  5. Supplementary Video 3

    The final rounds of the task and participant feedback. After completing all 70 rounds, participants were presented with a questionnaire. Participants copied the unique confirmation code provided in the results screen and pasted it into Amazon’s Mechanical Turk page so as to complete the HIT.

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DOI

https://doi.org/10.1038/s41562-018-0518-x