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Social learning strategies regulate the wisdom and madness of interactive crowds

Nature Human Behaviour volume 3, pages 183–193 (2019)Cite this article

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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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Fig. 1: Findings of the individual-based model showing the effects of social information use on the average decision accuracy over replications.
Fig. 2: Results from the individual-based model simulations showing the distribution of each group’s mean accuracy before environmental change (t ≤ 40).
Fig. 3: Time evolutions and distributions of decision performance for each condition.
Fig. 4: Model fitting for the three different task’s uncertain conditions (low-, moderate- and high-uncertainty) and the different group sizes.
Fig. 5: Change in fitted values (that is, the median of the Bayesian posterior distribution) of the social learning weight σi,t with time for each positive frequency-dependent individual for each level of task uncertainty.

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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).

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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.

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Authors and 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. Wataru Toyokawa
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  2. Andrew Whalen
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  3. Kevin N. Laland
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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.

Corresponding author

Correspondence to Wataru Toyokawa.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

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

Reporting Summary

Supplementary Video 1

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

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.

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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Toyokawa, W., Whalen, A. & Laland, K.N. Social learning strategies regulate the wisdom and madness of interactive crowds. Nat Hum Behav 3, 183–193 (2019). https://doi.org/10.1038/s41562-018-0518-x

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