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Title: Crowd control: reducing individual estimation bias by sharing biased social information
Authors: Jayles, Bertrand
Sire, Clément
Kurvers, Ralf H. J. M.
Keywords: Social sciences::General
Issue Date: 2021
Source: Jayles, 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-.
Journal: PLOS Computational Biology 
Abstract: Cognitive 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.
ISSN: 1553-734X
DOI: 10.1371/journal.pcbi.1009590
Research Centres: Institute of Catastrophe Risk Management (ICRM) 
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ICRM Journal Articles

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