Free information disrupts even Bayesian crowds
A core tenet underpinning the conception of contemporary information networks, such as social media platforms, is that users should not be constrained in the amount of information they can freely and willingly exchange with one another about a given topic. By means of a computational agent-based model, we show how even in groups of truth-seeking and cooperative agents with perfect information-processing abilities, unconstrained information exchange may lead to detrimental effects on the correctness of the group's beliefs. If unconstrained information exchange can be detrimental even among such idealized agents, it is prudent to assume it can also be so in practice. We therefore argue that constraints on information flow should be carefully considered in the design of communication networks with substantial societal impact, such as social media platforms.
Authors
- Shannon M. Cruz (ORCID: https://orcid.org/0000-0002-5244-5461)
- Davide Grossi (ORCID: https://orcid.org/0000-0002-9709-030X)
- Martina Testori (ORCID: https://orcid.org/0000-0001-7292-7129)
- Jonas Stein (ORCID: https://orcid.org/0000-0003-2714-5624)
Institutions
- Pennsylvania State University (US)
- University of Groningen (NL)
- Center for International Environmental Law (US)
- Global Strategy Group (US)
- Greenwich Public Schools (US)
- Institute of Behavioral Sciences (JP)
- Applied Behavioral Research (United States) (US)
- University of Greenwich (GB)
- University of Amsterdam (NL)
Publication Details
- Journal
- Proceedings of the National Academy of Sciences
- Published
- 2026-04-01
- DOI
- https://doi.org/10.1073/pnas.2518472123
- Primary Topic
- Opinion Dynamics and Social Influence
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Ministerie van Onderwijs, Cultuur en Wetenschap
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- HORIZON EUROPE Framework Programme
- Research Executive Agency