We propose nonparametric definitions of absolute and comparative naivete. These definitions leverage ex-ante choice of menu to identify predictions of future behavior and ex-post (random) choices from menus to identify actual behavior. The main advantage of our definitions is their...

We introduce and characterize a recursive model of dynamic choice that accommodates naivete about present bias. While recursive representations are important for tractable analysis of in nite-horizon problems, the commonly-used Strotz model of time inconsistency presents well-known technical...

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...

Under dynamic random utility, an agent (or population of agents) solves a dynamic decision problem subject to evolving private information. We analyze the fully general and non-parametric model, axiomatically characterizing the implied dynamic stochastic choice behavior. A key new feature...

Motivated by the fact that people's perceptions of their societies are routinely incorrect, we study the possibility and implications of misperception in social interactions. We focus on coordination games with assortative interactions, where agents with higher types (e.g., wealth, political...

We provide an axiomatic analysis of dynamic random utility, characterizing the stochastic choice behavior of agents who solve dynamic decision problems by maximizing some stochastic process (U_t) of utilities. We show first that even when (U_t) is arbitrary, dynamic random utility imposes new...

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...

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). We consider a population of agents who obtain information about the state of the world both from initial private signals...

We present an approach to analyze learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models, and observe that this makes it possible to partially restore standard...

We study settings in which, prior to playing an incomplete information game, players observe many draws of private signals about the state from some information structure. Signals are i.i.d. across draws, but may display arbitrary correlation across players. For each information structure, we...