 Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGoMitsuru Igami The Econometrics Journal, 2020, vol. 23, issue 3, S1-S24 Abstract: SummaryThis 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. Keywords: Approximate dynamic programming; artificial intelligence; conditional choice probability; deep neural network; dynamic structural model; inverse reinforcement learning; optimal control; reinforcement learning; simulation estimator (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:23:y:2020:i:3:p:s1-s24. Access Statistics for this article The Econometrics Journal is currently edited by Jaap Abbring More articles in The Econometrics Journal from Royal Economic Society Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.