  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.
