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

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 problem...

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Autor principal:	Tsutomu Harada
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f97c4484c26e43789bb180ad738e75e7
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spelling	oai:doaj.org-article:f97c4484c26e43789bb180ad738e75e72021-11-25T06:23:30ZThree heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach.1932-620310.1371/journal.pone.0252122https://doaj.org/article/f97c4484c26e43789bb180ad738e75e72021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252122https://doaj.org/toc/1932-6203Although 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.Tsutomu HaradaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252122 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tsutomu Harada Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach.
description	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.
format	article
author	Tsutomu Harada
author_facet	Tsutomu Harada
author_sort	Tsutomu Harada
title	Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach.
title_short	Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach.
title_full	Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach.
title_fullStr	Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach.
title_full_unstemmed	Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach.
title_sort	three heads are better than two: comparing learning properties and performances across individuals, dyads, and triads through a computational approach.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f97c4484c26e43789bb180ad738e75e7
work_keys_str_mv	AT tsutomuharada threeheadsarebetterthantwocomparinglearningpropertiesandperformancesacrossindividualsdyadsandtriadsthroughacomputationalapproach
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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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