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dc.contributor.authorJayles, Bertranden_US
dc.contributor.authorSire, Clémenten_US
dc.contributor.authorKurvers, Ralf H. J. M.en_US
dc.identifier.citationJayles, B., Sire, C. & Kurvers, R. H. J. M. (2021). Crowd control: reducing individual estimation bias by sharing biased social information. PLOS Computational Biology, 17(11), e1009590-.
dc.description.abstractCognitive biases are widespread in humans and animals alike, and can sometimes be reinforced by social interactions. One prime bias in judgment and decision-making is the human tendency to underestimate large quantities. Previous research on social influence in estimation tasks has generally focused on the impact of single estimates on individual and collective accuracy, showing that randomly sharing estimates does not reduce the underestimation bias. Here, we test a method of social information sharing that exploits the known relationship between the true value and the level of underestimation, and study if it can counteract the underestimation bias. We performed estimation experiments in which participants had to estimate a series of quantities twice, before and after receiving estimates from one or several group members. Our purpose was threefold: to study (i) whether restructuring the sharing of social information can reduce the underestimation bias, (ii) how the number of estimates received affects the sensitivity to social influence and estimation accuracy, and (iii) the mechanisms underlying the integration of multiple estimates. Our restructuring of social interactions successfully countered the underestimation bias. Moreover, we find that sharing more than one estimate also reduces the underestimation bias. Underlying our results are a human tendency to herd, to trust larger estimates than one's own more than smaller estimates, and to follow disparate social information less. Using a computational modeling approach, we demonstrate that these effects are indeed key to explain the experimental results. Overall, our results show that existing knowledge on biases can be used to dampen their negative effects and boost judgment accuracy, paving the way for combating other cognitive biases threatening collective systems.en_US
dc.relation.ispartofPLOS Computational Biologyen_US
dc.rights© 2021 Jayles et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.subjectSocial sciences::Generalen_US
dc.titleCrowd control: reducing individual estimation bias by sharing biased social informationen_US
dc.typeJournal Articleen
dc.contributor.researchInstitute of Catastrophe Risk Management (ICRM)en_US
dc.description.versionPublished versionen_US
dc.subject.keywordsDecision Makingen_US
dc.description.acknowledgementB.J. and R.K. were partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2002/1 “Science of Intelligence” – project number 390523135.en_US
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