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

Integrating information gained by observing others via So-cial Bayesian Learning can be beneficial for an agent’s per-formance, but can also enable population wide information cascades that perpetuate false beliefs through the agent pop-ulation. We show how agents can influence the observation network by changing their probability of observing others, and demonstrate the existence of a population-wide equilib-rium, 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