Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo

Mitsuru Igami

Papers from arXiv.org

Abstract: Artificial intelligence (AI) has achieved superhuman performance in a growing number of tasks, but understanding and explaining AI remain challenging. This paper clarifies the connections between machine-learning algorithms to develop AIs and the econometrics of dynamic structural models through the case studies of three famous game AIs. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza is an estimated value function via Rust's (1987) nested fixed-point method. AlphaGo's "supervised-learning policy network" is a deep neural network implementation of Hotz and Miller's (1993) conditional choice probability estimation; its "reinforcement-learning value network" is equivalent to Hotz, Miller, Sanders, and Smith's (1994) conditional choice simulation method. Relaxing these AIs' implicit econometric assumptions would improve their structural interpretability.

Date: 2017-10, Revised 2018-03
New Economics Papers: this item is included in nep-big and nep-cmp
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Published in the Econometrics Journal, 23:3 (September, 2020), S1-S24

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1710.10967

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