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.

We present an approach to analyze learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. Our main results provide general criteria to determine-without the need to explicitly analyze learning dynamics-when beliefs in a given environment converge to some long-run belief either locally or globally (i.e., from some or all initial beliefs). The key ingredient underlying these criteria is a novel "prediction accuracy" ordering over subjective models that refines existing comparisons based on Kullback-Leibler divergence. We show that these criteria can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to identify and analyze a natural class of environments, including costly information acquisition and sequential social learning,

We take an equilibrium-based approach to study the interplay between behavior and misperceptions in coordination games with assortative interactions. Our focus is assortativity neglect, where agents fail to take into account the extent of assortativity in society.We show, first, that assortativity neglect amplifies action dispersion, both in fixed societies and by exacerbating the effect of social changes. Second, unlike other misperceptions, assortativity neglect is a misperception that agents can rationalize in any true environment.Finally, assortativity neglect provides a lens through which to understand how empirically documented misperceptions about distributions of population characteristics (e.g., income inequality) vary across societies.

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Motivated by the rise of social media, we build a model studying the effect of an economy's potential for social learning on the adoption of innovations of uncertain quality. Provided consumers are forward-looking (i.e. recognize the value of waiting for information), equilibrium dynamics depend non-trivially on qualitative and quantitative features of the informational environment. We identify informational environments that are subject to a saturation effect, whereby increased opportunities for social learning can slow down adoption and learning and do not increase consumer welfare. We also suggest a novel, purely informational explanation for different commonly observed adoption curves (S-shaped vs. concave). (JEL D81, D83, O33)