Free Information Disrupts Even Bayesian Crowds
This addresses a foundational issue in communication network design, such as social media, with implications for societal impact, though it is incremental in building on existing models.
The paper tackles the problem of unconstrained information exchange in networks, showing through an agent-based model that even among idealized truth-seeking agents, free information flow can reduce the correctness of group beliefs.
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.