Don't Believe Everything You Hear; Preserving Relevant Information by Discarding Social Information

TL;DR

This paper explores how agents can manage social information to prevent false beliefs, demonstrating a population equilibrium where the benefits of social learning are balanced against the risks of misinformation.

Contribution

It introduces a formal framework for controlling social observation probabilities to mitigate negative information cascades in social Bayesian learning.

Findings

Existence of a population-wide equilibrium balancing social learning benefits and risks.

Negative information cascades involve processing increasing amounts of non-relevant information.

Agents can influence their observation network to improve information relevance.

Abstract

Integrating information gained by observing others via Social Bayesian Learning can be beneficial for an agent's performance, but can also enable population wide information cascades that perpetuate false beliefs through the agent population. We show how agents can influence the observation network by changing their probability of observing others, and demonstrate the existence of a population-wide equilibrium, where the advantages and disadvantages of the Social Bayesian update are balanced. We also use the formalism of relevant information to illustrate how negative information cascades are characterized by processing increasing amounts of non-relevant information.