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Artificial intelligence (AI) has achieved superhuman performance in a growingnumber of tasks, but understanding and explaining AI remain challenging. Thispaper clarifies the connections between machine-learning algorithms to developAIs and the econometrics of dynamic structural models through the case studiesof three famous game AIs. Chess-playing Deep Blue is a calibrated valuefunction, whereas shogi-playing Bonanza is an estimated value function viaRust's (1987) nested fixed-point method. AlphaGo's "supervised-learning policynetwork" is a deep neural network implementation of Hotz and Miller's (1993)conditional choice probability estimation; its "reinforcement-learning valuenetwork" is equivalent to Hotz, Miller, Sanders, and Smith's (1994) conditionalchoice simulation method. Relaxing these AIs' implicit econometric assumptionswould improve their structural interpretability.

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