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

By Mitsuru Igami. 2017

35 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Peter Maass
2. Mohammed Berrada
3. Amina Adadi

In Management Science and Operations Research

1. Amir Hussain
2. Simone Scardapane
3. Peter Maass
4. Ozan Öktem
5. Mohammed Berrada
6. Kaizhu Huang
7. Amina Adadi

Citations in the corpus

• Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “Available: https://arxiv.org/abs/1610.08210 [180] M. Igami.”
• Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023, background): “The work of Igami [184] about the connections between machine learning and econometrics laid the foundation for “Structural Econometrics for XAI.””
• Solving inverse problems using data-driven models (2019, background): “In particular, deep learning (LeCun, Bengio and Hinton 2015), which has had a transformative impact on a wide range of tasks related to artificial intelligence, ranging from computer vision and speech recognition to playing games (Igami 2017), is starting to show its impact on inverse problems.”