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

TL;DR

This paper explores the connections between advanced game-playing AIs and econometric models, revealing how these AI systems can be interpreted as structural estimations in economics.

Contribution

It provides a novel econometric interpretation of three famous game AIs, linking their algorithms to established economic estimation methods.

Findings

Deep Blue is a calibrated value function.

Bonanza uses an estimated value function via nested fixed-point method.

AlphaGo's neural networks correspond to conditional choice probability and simulation methods.

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.