Free Information Disrupts Even Bayesian Crowds

2026-04-02

AI Summary

This study investigates how unconstrained information exchange can impair the accuracy of collective beliefs, even among groups of fully rational and cooperative agents. Using a multi-agent computational model that integrates Bayesian inference with information diffusion dynamics, the work demonstrates for the first time that unrestricted information flow may lead to systematic errors in group judgment, despite all individuals possessing perfect Bayesian reasoning capabilities. This finding directly challenges the prevailing assumption that increased communication necessarily enhances collective intelligence. The results provide a theoretical foundation for designing information regulation mechanisms on high-impact platforms such as social media, where unmoderated sharing may paradoxically degrade the epistemic quality of group beliefs.

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.

Problem

• information exchange
• crowd belief
• social media
• information networks
• Bayesian agents

Innovation

• information overload
• Bayesian agents
• agent-based modeling
• belief convergence
• information constraints