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 learning via shared news. Each period agents receive the same quantity and
quality of firsthand information and can share it with friends. Some friends (possibly few) …

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 …

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

This paper proposes a tractable model of Bayesian learning on large random networks
where agents choose whether to adopt an innovation. We study the impact of the network …

I study endogenous learning dynamics for people who misperceive intertemporal
correlations in random sequences. Biased agents face an optimal‐stopping problem. They …

This paper studies a sequential social learning problem in which individuals face ambiguity
regarding other people's signal structures. It finds that ambiguity has a significant influence …

We study social learning in several natural, yet under-explored, environments among
people who naively think each predecessor's action reflects solely that person's private …

Agents learn about a changing state using private signals and their neighbours' past
estimates of the state. We present a model in which Bayesian agents in equilibrium use …

We analyze boundedly rational learning in social networks within binary action
environments. We establish how learning outcomes depend on the environment (ie …