Social Learning in General Information Environments

Abstract

I study a social learning model in which agents make decisions sequentially and learn about an unknown payoff-relevant state through two sources – a signal about the state itself (a state-signal) and a signal about the actions taken by previous agents (an action-signal). Our objective is to provide general conditions on the action-signals that lead the agents to eventually behave as if they know the state, i.e., that lead to information aggregation. When the agents’ action-signals are what I call weakly separating, it is shown that information aggregation occurs when the agents’ state-signals are unboundedly informative in the sense of Smith and Sorensen (2000). This result provides a unifying criterion to evaluate when information aggregation occurs, and shows that it can occur even in very opaque environments. The theory is illustrated with applications to privacy protection on digital platforms and the regulation of third-party information provision in social learning environments.