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Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach

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  • Tsutomu Harada

Abstract

Although it is considered that two heads are better than one, related studies argued that groups rarely outperform their best members. This study examined not only whether two heads are better than one but also whether three heads are better than two or one in the context of two-armed bandit problems where learning plays an instrumental role in achieving high performance. This research revealed that a U-shaped correlation exists between performance and group size. The performance was highest for either individuals or triads, but the lowest for dyads. Moreover, this study estimated learning properties and determined that high inverse temperature (exploitation) accounted for high performance. In particular, it was shown that group effects regarding the inverse temperatures in dyads did not generate higher values to surpass the averages of their two group members. In contrast, triads gave rise to higher values of the inverse temperatures than their averages of their individual group members. These results were consistent with our proposed hypothesis that learning coherence is likely to emerge in individuals and triads, but not in dyads, which in turn leads to higher performance. This hypothesis is based on the classical argument by Simmel stating that while dyads are likely to involve more emotion and generate greater variability, triads are the smallest structure which tends to constrain emotions, reduce individuality, and generate behavioral convergences or uniformity because of the ‘‘two against one” social pressures. As a result, three heads or one head were better than two in our study.

Suggested Citation

  • Tsutomu Harada, 2021. "Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
  • Handle: RePEc:plo:pone00:0252122
    DOI: 10.1371/journal.pone.0252122
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    1. Coleman, Stephen, 2004. "The Effect of Social Conformity on Collective Voting Behavior," Political Analysis, Cambridge University Press, vol. 12(1), pages 76-96, January.
    2. Robert Armstrong & Robert Williams & J. Barrett, 2004. "The Impact of Banality, Risky Shift and Escalating Commitment on Ethical Decision Making," Journal of Business Ethics, Springer, vol. 53(4), pages 365-370, September.
    3. Yaniv, Ilan, 1997. "Weighting and Trimming: Heuristics for Aggregating Judgments under Uncertainty," Organizational Behavior and Human Decision Processes, Elsevier, vol. 69(3), pages 237-249, March.
    4. Tanya Menon & Katherine W. Phillips, 2011. "Getting Even or Being at Odds? Cohesion in Even- and Odd-Sized Small Groups," Organization Science, INFORMS, vol. 22(3), pages 738-753, June.
    5. Germain Lefebvre & Maël Lebreton & Florent Meyniel & Sacha Bourgeois-Gironde & Stefano Palminteri, 2017. "Behavioural and neural characterization of optimistic reinforcement learning," Nature Human Behaviour, Nature, vol. 1(4), pages 1-9, April.
    6. Timothy E. J. Behrens & Laurence T. Hunt & Mark W. Woolrich & Matthew F. S. Rushworth, 2008. "Associative learning of social value," Nature, Nature, vol. 456(7219), pages 245-249, November.
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