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Estimation of Games under No Regret: Structural Econometrics for AI

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Abstract

We develop a method to recover primitives from data generated by artificial intelligence (AI) agents in strategic environments such as online marketplaces and auctions. Building on how leading online learning AIs are designed, we assume agents minimize their regret. Under asymptotic no regret, we show that time-average play converges to the set of Bayes coarse correlated equilibrium (BCCE) predictions. Our econometric procedure is based on BCCE restrictions and convergence rates of regretminimizing AIs. We apply the method to pricing data in a digital marketplace for used smartphones. We estimate sellers’ cost distributions and find lower markups than in centralized platforms.

Suggested Citation

  • Niccolò Lomys & Lorenzo Magnolfi, 2024. "Estimation of Games under No Regret: Structural Econometrics for AI," CSEF Working Papers 739, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    • Handle: RePEc:sef:csefwp:739
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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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