Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo
Abstract
Summary: This 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.
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Igami laid the foundation for 'Structural Econometrics for XAI' through work about connections between machine learning and econometrics.
“The work of Igami [184] about the connections between machine learning and econometrics laid the foundation for “Structural Econometrics for XAI.””
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [184]
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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al. (2018) · Science · doi:10.1126/science.aar6404 · Cited by 3,562 · see citing works
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685 · see citing works
Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning
Byrne, R. M. J. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/876 · Cited by 285 · see citing works
Machine learning for high-dimensional dynamic stochastic economies
Scheidegger, S., & Bilionis, I. (2019) · Journal of Computational Science · doi:10.1016/j.jocs.2019.03.004 · Cited by 72 · see citing works
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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,062 · see citing works
“The work of Igami [184] about the connections between machine learning and econometrics laid the foundation for “Structural Econometrics for XAI.””
BackgroundKabir, S., Hossain, M. S., & Andersson, K. (2025) · Algorithms · doi:10.3390/a18090556 · Cited by 44 · see citing works
- Authors
- Igami, M.
- Affiliations
- Yale University (US)
- Corresponding author
- Igami, Mitsuru
- ORCID
- Igami, Mitsuru
- Venue
- The Econometrics Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-03-11
- Volume
- 23
- Issue
- 3
- Pages
- S1-S24
- ISSN
- 1368-4221 (ISSN-L)
- 1368-423X
- OpenAlex
- W3011754248
- Cited by
- 28
- References
- 4
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 6 · 2023: 9 · 2022: 3 · 2021: 2 · 2020: 3 · 2019: 2 · 2018: 1
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Econometrics Journal (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Georgetown University
- Osaka University
- Harvard University
- Johns Hopkins University
- University of Copenhagen
- University of Toronto
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Keywords
- artificial intelligence, structural estimation, dynamic structural model, Deep Blue, Bonanza, AlphaGo, game-playing algorithms, value function estimation, conditional choice probabilities, reinforcement learning, supervised learning, chess, shogi
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