Free Information Disrupts Even Bayesian Crowds
Jonas Stein, Shannon Cruz, Davide Grossi, Martina Testori
TLDR
Unconstrained information exchange can detrimentally affect group beliefs, even among idealized, truth-seeking agents with perfect processing.
Key contributions
- Computational model shows unconstrained info exchange harms group beliefs.
- Even idealized, truth-seeking agents suffer from free information flow.
- Leads to detrimental effects on the correctness of collective beliefs.
- Suggests considering information flow constraints in network design.
Why it matters
This paper challenges the common belief that unconstrained information exchange is always beneficial. It shows that even ideal agents can suffer from degraded collective beliefs, arguing for careful consideration of information flow constraints in social media design.
Original 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.
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