Dissent enhances the credibility of groups of Bayesian conformist decision-makers
Abstract
When making a decision, individuals use their private information to evaluate the available options. In a group, they can also learn from other group members’ choices, which partially reveal their own private information. Bayesian estimation provides tools for modelling decisions that optimally balance these two sources of information and can incorporate empirically documented cognitive biases, such as the conformity bias, which promotes consensus at the expense of accuracy. We use this framework to analyse a situation in which an uninformed individual judges the accuracy of a group of informed individuals—a situation relevant in human societies, where citizens routinely follow recommendations provided by groups of experts. In the absence of conformity bias, group accuracy increases monotonically with agreement level, suggesting that unanimity is a reliable indicator of trustworthiness. However, a more realistic model that describes groups with different degrees of conformity shows that unanimity is not an indicator of high reliability. Instead, the most reliable groups are those with relatively high levels of agreement, but not reaching total consensus. This result shows that moderate levels of dissent are not only useful as an error-correcting mechanism but are also a characteristic of trustworthy groups.
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BibTeX
@article{boussard2026dissent,
title = {{Dissent enhances the credibility of groups of Bayesian conformist decision-makers}},
author = {Aurèle Boussard and Richard Mann and Alfonso Pérez-Escudero},
journal = {Journal of the Royal Society Interface},
year = {2026},
month = aug,
doi = {10.1098/rsif.2025.0956},
url = {https://hal.science/hal-05795307v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Dissent enhances the credibility of groups of Bayesian conformist decision-makers
AU - Aurèle Boussard
AU - Richard Mann
AU - Alfonso Pérez-Escudero
PY - 2026
DA - 2026/08/05
JO - Journal of the Royal Society Interface
DO - 10.1098/rsif.2025.0956
UR - https://hal.science/hal-05795307v1
AB - When making a decision, individuals use their private information to evaluate the available options. In a group, they can also learn from other group members’ choices, which partially reveal their own private information. Bayesian estimation provides tools for modelling decisions that optimally balance these two sources of information and can incorporate empirically documented cognitive biases, such as the conformity bias, which promotes consensus at the expense of accuracy. We use this framework to analyse a situation in which an uninformed individual judges the accuracy of a group of informed individuals—a situation relevant in human societies, where citizens routinely follow recommendations provided by groups of experts. In the absence of conformity bias, group accuracy increases monotonically with agreement level, suggesting that unanimity is a reliable indicator of trustworthiness. However, a more realistic model that describes groups with different degrees of conformity shows that unanimity is not an indicator of high reliability. Instead, the most reliable groups are those with relatively high levels of agreement, but not reaching total consensus. This result shows that moderate levels of dissent are not only useful as an error-correcting mechanism but are also a characteristic of trustworthy groups.
SN - 1742-5689
SN - 1742-5662
ER -