{
  "annotation_id": "94b71a67-d436-4406-96db-fc8b3e001c8b",
  "date_created": "2026-02-17T05:53:24.272000Z",
  "date_modified": "2026-02-17T05:53:24.272000Z",
  "file_hash": "b83a632c61086d0771e08d27c36c48736d522c477ad905276d1ca9d0995e582a",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.09891",
    "authors": [
      "Ignacio Esponda",
      "Demian Pouzo"
    ],
    "categories": [
      "econ.TH",
      "math.ST",
      "stat.TH"
    ],
    "journal_ref": "Conference Volume for 2025 World Congress of the Econometric Society, Chapter 6",
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "Learning and Equilibrium under Model Misspecification",
    "url": "https://arxiv.org/abs/2601.09891",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "63e1a97f-02b8-4c7a-9f89-e52e965d49fc",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}