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Learning and Equilibrium under Model Misspecification

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  • Ignacio Esponda
  • Demian Pouzo

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.

Suggested Citation

  • Ignacio Esponda & Demian Pouzo, 2026. "Learning and Equilibrium under Model Misspecification," Papers 2601.09891, arXiv.org.
    • Handle: RePEc:arx:papers:2601.09891
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    References listed on IDEAS

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