ABSTRACT:
We study to what extent information aggregation in social learning environments is robust to slight misperceptions of others' characteristics (e.g., tastes or risk attitudes). A population of agents obtain information about the state of the world both from initial private signals and by observing a random sample of other agents' actions over time, where agents' actions depend not only on their beliefs about the state but also on their idiosyncratic types. When agents are correct about the type distribution in the population, they learn the true state in the long run. By contrast, our first main result shows that even "arbitrarily small'' amounts of misperception can generate extreme breakdowns of information aggregation, where in the long run all agents incorrectly assign probability 1 to some fixed state of the world, regardless of the true underlying state. This stark discontinuous departure from the correctly specified benchmark motivates independent analysis of information aggregation under misperception.

Our second main result shows that any misperception about the type distribution gives rise to a specific failure of information aggregation where agents' long-run beliefs and behavior vary only "coarsely'' with the state, and we provide systematic predictions for how the nature of misperception shapes these coarse long-run outcomes. Finally, we show that how sensitive information aggregation is to misperception depends on how rich agents' payoff-relevant uncertainty is. An important design implication is that information aggregation can be improved through interventions aimed at ``simplifying'' the agents' learning environment.