Misinterpreting Others and the Fragility of Social Learning

Abstract

We exhibit a natural environment, social learning among heterogeneous agents, where even slight misperceptions can have a large negative impact on long-run learning outcomes. We consider a population of agents who 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 (e.g., tastes or risk attitudes). When agents are correct about the type distribution in the population, they learn the true state in the long run. By contrast, we show, first, that even arbitrarily small amounts of misperception about the type distribution 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. Second, any misperception of the type distribution leads long-run beliefs and behavior to vary only coarsely with the state, and we provide systematic predictions for how the nature of misperception shapes these coarse long-run outcomes. Third, we show that how fragile information aggregation is against misperception depends on the richness of agents' payoff-relevant uncertainty; a design implication is that information aggregation can be improved by simplifying agents' learning environment. The key feature behind our findings is that agents' belief-updating becomes "decoupled" from the true state over time. We point to other environments where this feature is present and leads to similar fragility results.

Dispersed Behavior and Perceptions in Assortative Societies

“…Finally, while the paper has focused on linear best-response coordination games (and their generalizations in Section 5.1.3), we note that assortativity neglect might be of relevance in other population games, both static (e.g., discrete-action games such as political protests) and dynamic (e.g., social learning; see Section 7.2 of Frick, Iijima, and Ishii, 2019).…”

Convergence in models of misspecified learning

“…1 More distantly related, Bohren (2016) and Bohren and Hauser (2020) study long-run beliefs in misspecified social learning environments with a two-point prior and short-lived agents. Also in the context of social learning, Frick et al (2019) analyze the robustness of long-run beliefs with respect to small amounts of misspecification about others' preferences.…”