Stein, Jonas, Cruz, Shannon, Grossi, Davide and Testori, Martina ORCID: https://orcid.org/0000-0001-7292-7129 (2026) Free information disrupts even Bayesian crowds. Proceedings of the National Academy of Sciences (PNAS), 123 (14):e2518472123. ISSN 0027-8424 (Print), 1091-6490 (Online) (doi:10.1073/pnas.2518472123)

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Abstract

A core tenet underpinning the conception of contemporary information networks,such as social media platforms, is that users should not be constrained in the amountof information they can freely and willingly exchange with one another about a giventopic. By means of a computational agent-based model, we show how even in groupsof truth-seeking and cooperative agents with perfect information-processing abilities,unconstrained information exchange may lead to detrimental effects on the correctnessof the group’s beliefs. If unconstrained information exchange can be detrimental evenamong such idealized agents, it is prudent to assume it can also be so in practice. Wetherefore argue that constraints on information flow should be carefully considered inthe design of communication networks with substantial societal impact, such as socialmedia platforms

Item Type:	Article
Uncontrolled Keywords:	wisdom of crowds, homophily, epistemic inequality, social learning, Bayesian agent based model
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > School of Business, Operations and Strategy
Last Modified:	02 Apr 2026 13:07
URI:	https://gala.gre.ac.uk/id/eprint/50197

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