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Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo

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  • Mitsuru Igami

Abstract

SummaryThis article clarifies the connections between certain algorithms to develop artificial intelligence (AI) and the econometrics of dynamic structural models, with concrete examples of three 'game AIs'. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza is an estimated value function via Rust’s nested fixed-point (NFXP) method. AlphaGo’s 'supervised-learning policy network' is a deep-neural-network implementation of the conditional-choice-probability (CCP) estimation reminiscent of Hotz and Miller's first step; the construction of its 'reinforcement-learning value network' is analogous to their conditional choice simulation (CCS). I then explain the similarities and differences between AI-related methods and structural estimation more generally, and suggest areas of potential cross-fertilization.

Suggested Citation

  • Mitsuru Igami, 2020. "Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 1-24.
    • Handle: RePEc:oup:emjrnl:v:23:y:2020:i:3:p:s1-s24.
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    File URL: http://hdl.handle.net/10.1093/ectj/utaa005
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    Citations

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    Cited by:

    1. Joshua S. Gans, 2023. "Artificial intelligence adoption in a competitive market," Economica, London School of Economics and Political Science, vol. 90(358), pages 690-705, April.
    2. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie 21.14, Université de Lausanne, Faculté des HEC, Département d’économie.
    3. Pedro Afonso Fernandes, 2024. "Forecasting with Neuro-Dynamic Programming," Papers 2404.03737, arXiv.org.
    4. Minkyu Shin & Jin Kim & Minkyung Kim, 2020. "Measuring Human Adaptation to AI in Decision Making: Application to Evaluate Changes after AlphaGo," Papers 2012.15035, arXiv.org, revised Jan 2021.
    5. Philip Marx & Elie Tamer & Xun Tang, 2022. "Parallel Trends and Dynamic Choices," Papers 2207.06564, arXiv.org, revised Aug 2023.
    6. Wen Zhang & Kee-Hung Lai & Qiguo Gong, 2024. "The future of the labor force: higher cognition and more skills," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    7. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation: With an Application to Option Pricing," Papers 2102.09209, arXiv.org.
    8. Pablo S. Castro & Ajit Desai & Han Du & Rodney Garratt & Francisco Rivadeneyra, 2021. "Estimating Policy Functions in Payments Systems Using Reinforcement Learning," Staff Working Papers 21-7, Bank of Canada.
    9. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020. "Deep Learning for Individual Heterogeneity: An Automatic Inference Framework," Papers 2010.14694, arXiv.org, revised Jul 2021.

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