Work

Free information disrupts even Bayesian crowds

Year: 2026

Type: article

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 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.

Source: Proceedings of the National Academy of Sciences

DOI: 10.1073/pnas.2518472123

Authors: Jonas Stein, Shannon Cruz, Davide Grossi, Martina Testori

Institutions: University of Groningen, Pennsylvania State University, University of Amsterdam, University of Greenwich

Language: English

Topic: Opinion Dynamics and Social Influence

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid