Learning and Equilibrium under Model Misspecification
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.
Fields of Study & Subjects
Game Theory and Applications
Economic theories and models
Machine Learning and Algorithms
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 14 January 2026
- Document type
- Preprint