We develop a dynamic model of political competition between two groups that differ in their
subjective model of the data generating process for a common outcome. One group has a …

We exhibit a natural environment, social learning among heterogeneous agents, where
even slight misperceptions can have a large negative impact on long‐run learning …

We study how an agent learns from endogenous data when their prior belief is misspecified.
We show that only uniform Berk–Nash equilibria can be long‐run outcomes, and that all …

This paper develops a general framework to study how misinterpreting information impacts
learning. Our main result is a simple criterion to characterize long‐run beliefs based on the …

We study the macroeconomic implications of narratives, or beliefs about the economy that
affect decisions and spread contagiously. Empirically, we use natural-languageprocessing …

We present an approach to analyse learning outcomes in a broad class of misspecified
environments, spanning both single-agent and social learning. We introduce a novel …

We consider an agent who represents uncertainty about the environment via a possibly
misspecified model. Each period, the agent takes an action, observes a consequence, and …

Many human-facing algorithms--including those that power recommender systems or hiring
decision tools--are trained on data provided by their users. The developers of these …

Toward explaining the persistence of biased inferences, we propose a framework to
evaluate competing (mis) specifications in strategic settings. Agents with heterogeneous …

We establish convergence of beliefs and actions in a class of one‐dimensional learning
settings in which the agent's model is misspecified, she chooses actions endogenously, and …