Neural Networks as a Unifying Learning Model for Random Normal Form Games

Adaptive Behavior (2011), vol. 19, no. 6, pp. 383 - 408, doi.org/10.1177/1059712311417636

36 Pages Posted: 12 Aug 2009 Last revised: 13 Aug 2012

See all articles by Leonidas Spiliopoulos

Leonidas Spiliopoulos

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development

Date Written: January 20, 2010

Abstract

This paper models the learning process of a population of randomly-rematched tabula rasa neural network agents playing randomly generated 3 × 3 normal form games of all strategic types. Evidence was found of the endogenous emergence of a similarity measure of games based on the number and types of Nash equilibria, and of heuristics that have been found effective in describing human behavior in experimental one-shot games. The neural network agents were found to approximate experimental human behavior very well across various dimensions such as convergence to Nash equilibria, equilibrium selection and adherence to principles of dominance and iterated dominance. This is corroborated by evidence from five studies of experimental one-shot games, as the Spearman correlation coefficients of the probability distribution over the neural networks’ and human subjects’ actions ranged from 0.49 to 0.89.

Keywords: Behavioral game theory, Learning, Global games, Neural networks, Agent-based computational economics, Simulations, Complex adaptive systems, Artificial intelligence

JEL Classification: C45, C70, C73

Suggested Citation

Spiliopoulos, Leonidas, Neural Networks as a Unifying Learning Model for Random Normal Form Games (January 20, 2010). Adaptive Behavior (2011), vol. 19, no. 6, pp. 383 - 408, doi.org/10.1177/1059712311417636, Available at SSRN: https://ssrn.com/abstract=1447968 or http://dx.doi.org/10.2139/ssrn.1447968

Leonidas Spiliopoulos (Contact Author)

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development ( email )

Lentzeallee 94
D-14195 Berlin, 14195
Germany

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