Free Information Disrupts Even Bayesian Crowds

TL;DR

This paper demonstrates through a computational model that unrestricted information exchange in social networks can harm collective belief accuracy, even among ideal agents, suggesting the need for constraints in real-world platforms.

Contribution

It introduces an agent-based model showing that free information flow can negatively impact truth-seeking groups, highlighting the importance of constraints in social media design.

Findings

Unconstrained information exchange can reduce group belief correctness.

Even perfect-information agents are affected by free information flow.

Constraints on information flow are advisable for societal communication networks.

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.