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Estimation of Games under No Regret: Structural Econometrics for AI

Author

Listed:
  • Niccolo Lomys

    (CSEF and Università degli Studi di Napoli Federico II)

  • Lorenzo Magnolfi

    (Department of Economics, University of Wisconsin-Madison)

Abstract

We develop a method to recover primitives from data generated by artificial intelligence (AI) agents in strategic environments like online marketplaces and auctions. Building on the design of leading online learning AIs, we impose a regret-minimization property on behavior. Under this property, we show that time-average play converges to the set of Bayes coarse correlated equilibrium (BCCE) predictions. We develop an inferential procedure based on BCCE restrictions and convergence rates of regret-minimizing AIs. We apply the method to pricing data in an online marketplace for used electronics. We estimate sellers' cost distributions and find lower markups than in centralized platforms.

Suggested Citation

  • Niccolo Lomys & Lorenzo Magnolfi, 2024. "Estimation of Games under No Regret: Structural Econometrics for AI," Working Papers 24-05, NET Institute.
    • Handle: RePEc:net:wpaper:2405
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    More about this item

    Keywords

    AI Decision-Making; Empirical Games; Regret Minimization; Bayes (Coarse) Correlated Equilibrium; Partial Identification;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L8 - Industrial Organization - - Industry Studies: Services

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