Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Social learning strategies regulate the wisdom and madness of interactive crowds


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.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.

Code availability

The browser-based online task was built by Node.js ( and (, and the codes are available from GitHub ( Analyses were conducted in R ( and simulations of individual-based models were conducted in Mathematica (, and the codes for both of these are available from GitHub (

Data availability

Both experimental and simulation data are available in an online repository (


  1. 1.

    Bonabeau, E, Dorigo, M. & Theraulaz, G. Swarm Intelligence: From Natural to Artificial Systems (Oxford Univ. Press, New York, 1999).

  2. 2.

    Camazine, S. et al. Self-Organization in Biological Systems (Princeton Univ. Press, Princeton, 2001).

  3. 3.

    Krause, J., Ruxton, G. D. & Krause, S. Swarm intelligence in animals and humans. Trends Ecol. Evol. 25, 28–34 (2010).

    Article  Google Scholar 

  4. 4.

    Seeley, T. D. The Wisdom of the Hive (Harvard Univ. Press, Cambridge, MA, 1995).

  5. 5.

    Sumpter, D. J. T. Collective Animal Behavior (Princeton Univ. Press, Princeton, 2010).

  6. 6.

    King, A. J. & Sueur, C. Where next? Group coordination and collective decision making by primates. Int. J. Primatol. 32, 1245–1267 (2011).

    Article  Google Scholar 

  7. 7.

    Morand-Ferron, J. & Quinn, J. L. Larger groups of passerines are more efficient problem solvers in the wild. Proc. Natl Acad. Sci. USA 108, 15898–15903 (2011).

    CAS  Article  Google Scholar 

  8. 8.

    Sasaki, T. & Biro, D. Cumulative culture can emerge from collective intelligence in animal groups. Nat. Commun. 8, 1–6 (2017).

    Article  Google Scholar 

  9. 9.

    Shaffer, Z., Sasaki, T. & Pratt, S. C. Linear recruitment leads to allocation and flexibility in collective foraging by ants. Anim. Behav. 86, 967–975 (2013).

    Article  Google Scholar 

  10. 10.

    Reid, C. R. & Latty, T. Collective behaviour and swarm intelligence in slime moulds. FEMS Microbiol. Rev. 40, 798–806 (2016).

    CAS  Article  Google Scholar 

  11. 11.

    Krause, J. & Ruxton, G. D. Living in Groups (Oxford Univ. Press, Oxford & New York, 2002).

  12. 12.

    Mackay, C. Extraordinary Popular Delusions and the Madness of Crowds (Richard Bentley, London, 1841).

  13. 13.

    Kameda, T. & Hastie, R. in Emerging Trends in the Social and Behavioral Sciences: An Interdisciplinary, Searchable, and Linkable Resource 1–14 (Wiley, Hoboken, NJ, USA, 2015).

  14. 14.

    Le Bon, G. The Crowd: A Study of the Popular Mind 4th edn (Unwin, London, 1896).

  15. 15.

    Raafat, R. M., Chater, N. & Frith, C. Herding in humans. Trends Cogn. Sci. 13, 420–428 (2009).

    Article  Google Scholar 

  16. 16.

    Bikhchandani, S., Hirshleifer, D. & Welch, I. A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econ. 100, 992–1026 (1992).

    Article  Google Scholar 

  17. 17.

    Chari, V. V. & Kehoe, P. J. Financial crises as herds: overturning the critiques. J. Econ. Theory 119, 128–150 (2004).

    Article  Google Scholar 

  18. 18.

    Janis, I. L. Victims of Groupthink: A Psychological Study of Foreign Policy (Houghton Mifflin Company, Boston, 1972).

  19. 19.

    Muchnik, L., Aral, S. & Taylor, S. J. Social influence bias: a randomized experiment. Science 341, 647–651 (2013).

    CAS  Article  Google Scholar 

  20. 20.

    Salganik, M. J., Dodds, P. S. & Watts, D. J. Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 854–856 (2006).

    CAS  Article  Google Scholar 

  21. 21.

    Lorenz, J., Rauhut, H., Schweitzer, F. & Helbing, D. How social influence can undermine the wisdom of crowd effect. Proc. Natl Acad. Sci. USA 108, 9020–9025 (2011).

    CAS  Article  Google Scholar 

  22. 22.

    Jayles, B. et al. How social information can improve estimation accuracy in human groups. Proc. Natl Acad. Sci. USA 114, 201703695 (2017).

    Article  Google Scholar 

  23. 23.

    Giraldeau, L.-A., Valone, T. J. & Templeton, J. J. Potential disadvantages of using socially acquired information. Phil. Trans. R. Soc. Lond. B 357, 1559–1566 (2002).

    Article  Google Scholar 

  24. 24.

    Detrain, C. & Deneubourg, J. L. Collective decision-making and foraging patterns in ants and honeybees. Adv. Insect Physiol. 35, 123–173 (2008).

    Article  Google Scholar 

  25. 25.

    List, C., Elsholtz, C. & Seeley, T. D. Independence and interdependence in collective decision making: an agent-based model of nest-site choice by honeybee swarms. Phil. Trans. R. Soc. Lond. B 364, 755–762 (2009).

    Article  Google Scholar 

  26. 26.

    Deneubourg, J. L., Aron, S., Goss, S. & Pasteels, J. M. The self-organizing exploratory pattern of the Argentine ant. J. Insect Behav. 30, 159–168 (1990).

    Article  Google Scholar 

  27. 27.

    Beckers, R., Deneubourg, J. L. D., Goss, S. & Pasteels, J. M. Collective decision making through food recruitment. Insectes Soc. 37, 258–267 (1990).

    Article  Google Scholar 

  28. 28.

    Boyd, R. & Richerson, P. J. Culture and the Evolutionary Process (Univ. Chicago Press, Chicago, 1985).

  29. 29.

    Richerson, P. J. & Boyd, R. Not by Genes Alone (Univ. Chicago Press, Chicago, 2005).

  30. 30.

    Feldman, M. W., Aoki, K. & Kumm, J. Individual versus social learning: evolutionary analysis in a fluctuating environment. Anthropol. Sci. 104, 209–231 (1996).

    Article  Google Scholar 

  31. 31.

    Laland, K. N. Social learning strategies. Anim. Learn. Behav. 32, 4–14 (2004).

    Article  Google Scholar 

  32. 32.

    Kameda, T. & Nakanishi, D. Cost–benefit analysis of social/cultural learning in a nonstationary uncertain environment. Evol. Hum. Behav. 23, 373–393 (2002).

    Article  Google Scholar 

  33. 33.

    Kendal, R. L., Coolen, I. & Laland, K. N. The role of conformity in foraging when personal and social information conflict. Behav. Ecol. 15, 269–277 (2004).

    Article  Google Scholar 

  34. 34.

    Morgan, T. J. H., Rendell, L. E., Ehn, M., Hoppitt, W. & Laland, K. N. The evolutionary basis of human social learning. Proc. Biol. Sci. B 279, 653–662 (2012).

    CAS  Article  Google Scholar 

  35. 35.

    Toyokawa, W., Saito, Y. & Kameda, T. Individual differences in learning behaviours in humans: asocial exploration tendency does not predict reliance on social learning. Evol. Hum. Behav. 38, 325–333 (2017).

    Article  Google Scholar 

  36. 36.

    Webster, M. M. & Laland, K. N. Social learning strategies and predation risk: minnows copy only when using private information would be costly. Proc. R. Soc. B 275, 2869–2876 (2008).

    CAS  Article  Google Scholar 

  37. 37.

    Webster, M. M. & Laland, K. N. Reproductive state affects reliance on public information in sticklebacks. Proc. Biol. Sci. B 278, 619–627 (2011).

    CAS  Article  Google Scholar 

  38. 38.

    Boyd, R. & Richerson., P. J. Social learning as an adaptation. Lect. Math. Life Sci. 20, 1–26 (1989).

    Google Scholar 

  39. 39.

    Bond, R. Group size and conformity. Group Process. Intergroup Relat. 8, 331–354 (2005).

    Article  Google Scholar 

  40. 40.

    Kline, M. A. & Boyd, R. Population size predicts technological complexity in Oceania. Proc. R. Soc. B 277, 2559–2564 (2010).

    Article  Google Scholar 

  41. 41.

    Muthukrishna, M., Shulman, B. W., Vasilescu, V. & Henrich, J. Sociality influences cultural complexity. Proc. R. Soc. B 281, 20132511 (2014).

    Article  Google Scholar 

  42. 42.

    Street, S. E., Navarrete, A. F., Reader, S. M. & Laland, K. N. Coevolution of cultural intelligence, extended life history, sociality, and brain size in primates. Proc. Natl Acad. Sci. USA 114, 1–7 (2017).

    Article  Google Scholar 

  43. 43.

    Nicolis, S. & Deneubourg., J. Emerging patterns and food recruitment in ants: an analytical study. J. Theor. Biol. 198, 575–592 (1999).

    CAS  Article  Google Scholar 

  44. 44.

    Pratt, S. C. & Sumpter, D. J. T. A tunable algorithm for collective decision-making. Proc. Natl Acad. Sci. USA 103, 15906–15910 (2006).

    CAS  Article  Google Scholar 

  45. 45.

    List, C. Democracy in animal groups: a political science perspective. Trends Ecol. Evol. 19, 166–168 (2004).

    Article  Google Scholar 

  46. 46.

    King, A. J. & Cowlishaw, G. When to use social information: the advantage of large group size in individual decision making. Biol. Lett. 3, 137–139 (2007).

    PubMed Central  Article  Google Scholar 

  47. 47.

    Wolf, M. et al. Accurate decisions in an uncertain world: collective cognition increases true positives while decreasing false positives. Proc. R. Soc. B Biol. Sci. 280, 20122777 (2013).

    Article  Google Scholar 

  48. 48.

    Laan, A., Madirolas, G. & Polavieja, G. G. D. Rescuing collective wisdom when the average group opinion is wrong. Front. Robot. AI 4, 1–28 (2017).

    Article  Google Scholar 

  49. 49.

    Aplin, L. M., Sheldon, B. C. & McElreath, R. Conformity does not perpetuate suboptimal traditions in a wild population of songbirds. Proc. Natl Acad. Sci. USA 114, 7830–7837 (2017).

    CAS  Article  Google Scholar 

  50. 50.

    Barrett, B. J., Mcelreath, R. L., Perry, S. E. & Barrett, B. J. Pay-off-biased social learning underlies the diffusion of novel extractive foraging traditions in a wild primate. Proc. R. Soc. B 284, 20170358 (2017).

    Article  Google Scholar 

  51. 51.

    McElreath, R. et al. Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies. Phil. Trans. R. Soc. Lond. B 363, 3515–3528 (2008).

    Article  Google Scholar 

  52. 52.

    Toyokawa, W., Kim, H.-R. & Kameda, T. Human collective intelligence under dual exploration-exploitation dilemmas. PLoS One 9, e95789 (2014).

    PubMed Central  Article  Google Scholar 

  53. 53.

    Kandler, A. & Laland, K. N. Tradeoffs between the strength of conformity and number of conformists in variable environments. J. Theor. Biol. 332, 191–202 (2013).

    Article  Google Scholar 

  54. 54.

    Efferson, C., Lalive, R., Richerson, P. J., McElreath, R. & Lubell, M. Conformists and mavericks: the empirics of frequency-dependent cultural transmission. Evol. Hum. Behav. 29, 56–64 (2008).

    Article  Google Scholar 

  55. 55.

    McElreath, R. et al. Applying evolutionary models to the laboratory study of social learning. Evol. Hum. Behav. 26, 483–508 (2005).

    Article  Google Scholar 

  56. 56.

    Mesoudi, A. An experimental comparison of human social learning strategies: payoff-biased social learning is adaptive but underused. Evol. Hum. Behav. 32, 334–342 (2011).

    Article  Google Scholar 

  57. 57.

    Perreault, C., Moya, C. & Boyd, R. A Bayesian approach to the evolution of social learning. Evol. Hum. Behav. 33, 449–459 (2012).

    Article  Google Scholar 

  58. 58.

    Rendell, L. et al. Why copy others? Insights from the social learning strategies tournament. Science 328, 208–213 (2010).

    CAS  PubMed Central  Article  Google Scholar 

  59. 59.

    Jolles, J. W., Laskowski, K. L., Boogert, N. J. & Manica, A. Repeatable group differences in the collective behaviour of stickleback shoals across ecological contexts. Proc. R. Soc. B 285, 13–16 (2018).

    Article  Google Scholar 

  60. 60.

    Michelena, P., Jeanson, R., Deneubourg, J.-L. & Sibbald, A. M. Personality and collective decision-making in foraging herbivores. Proc. R. Soc. B 277, 1093–1099 (2010).

    Article  Google Scholar 

  61. 61.

    Planas-Sitjà, I., Deneubourg, J.-L., Gibon, C. & Sempo, G. Group personality during collective decision-making: a multi-level approach. Proc. R. Soc. B 282, 20142515 (2015).

    Article  Google Scholar 

  62. 62.

    Mesoudi, A., Chang, L., Dall, S. R. X. & Thornton, A. The evolution of individual and cultural variation in social learning. Trends Ecol. Evol. 31, 215–225 (2016).

    Article  Google Scholar 

  63. 63.

    Barrett, B. J. Equifinality in empirical studies of cultural transmission. Behav. Process. (in the press).

  64. 64.

    Biro, D., Sasaki, T. & Portugal., S. J. Bringing a time-depth perspective to collective animal behaviour. Trends Ecol. Evol. 31, 550–562 (2016).

    Article  Google Scholar 

  65. 65.

    Hoppitt W. & Laland K. N. Social Learning: An Introduction to Mechanisms, Methods, and Models (Princeton Univ. Press, Princeton, NJ, USA, 2013).

  66. 66.

    Toelch, U., Bruce, M. J., Meeus, M. T. H. & Reader, S. M. Humans copy rapidly increasing choices in a multiarmed bandit problem. Evol. Hum. Behav. 31, 326–333 (2010).

    Article  Google Scholar 

  67. 67.

    Bahrami, B. et al. Optimally interacting minds. Science 329, 1081–1085 (2010).

    CAS  PubMed Central  Article  Google Scholar 

  68. 68.

    Daw, N. D., O’Doherty, J. P., Dayan, P., Seymour, B. & Dolan., R. J. Cortical substrates for exploratory decisions in humans. Nature 441, 876–879 (2006).

    CAS  PubMed Central  Article  Google Scholar 

  69. 69.

    Hergueux, J. & Jacquemet, N. Social preferences in the online laboratory: a randomized experiment. Exp. Econ. 18, 251–283 (2015).

    Article  Google Scholar 

  70. 70.

    Dandurand, F., ShultzEmail, T. R. & Onishi, K. H. Comparing online and lab methods in a problem-solving experiment. Behav. Res. Methods 40, 428–434 (2008).

    Article  Google Scholar 

  71. 71.

    Becker, J., Brackbill, D. & Centola, D. Network dynamics of social influence in the wisdom of crowds. Proc. Natl Acad. Sci. USA 114, E5070–E5076 (2017).

    CAS  Article  Google Scholar 

  72. 72.

    Lorge, I., Fox, D., Davitz, J. & Brenner, M. A survey of studies contrasting the quality of group performance and individual performance, 1920–1957. Psychol. Bull. 55, 337–372 (1958).

    CAS  Article  Google Scholar 

  73. 73.

    Hastie, R. & Kameda, T. The robust beauty of majority rules in group decisions. Psychol. Rev. 112, 494–508 (2005).

    Article  Google Scholar 

  74. 74.

    Payzan-Lenestour, E. & Bossaerts, P. Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings. PLoS Comput. Biol. 7, e1001048 (2011).

    CAS  PubMed Central  Article  Google Scholar 

  75. 75.

    Sutton, R. S. & Barto, A. G. Reinforcement Learning: an Introduction (MIT Press, Cambridge, MA, 1998).

  76. 76.

    Ahn, W. Y. et al. Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users. Front. Psychol. 5, 849 (2014).

    PubMed Central  Article  Google Scholar 

Download references


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




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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing