Learning and Equilibrium under Model Misspecification

TL;DR

This paper presents a unified framework for analyzing how agents learn and reach equilibrium when they operate under incorrect models of their environment, covering both statistical foundations and strategic settings.

Contribution

It introduces a comprehensive approach to studying learning and equilibrium under model misspecification, including extensions to environments with action-dependent data.

Findings

Framework unifies misspecified learning analysis

Extends insights to strategic and endogenous data environments

Provides foundations for future research in misspecified models

Abstract

This chapter develops a unified framework for studying misspecified learning situations in which agents optimize and update beliefs within an incorrect model of their environment. We review the statistical foundations of learning from misspecified models and extend these insights to environments with endogenous, action-dependent data, including both single agent and strategic settings.