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Learning to play 3×3 games: Neural networks as bounded-rational players

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  • Sgroi, Daniel
  • Zizzo, Daniel John

Abstract

We present a neural network methodology for learning game-playing rules in general. Existing research suggests learning to find a Nash equilibrium in a new game is too difficult a task for a neural network, but says little about what it will do instead. We observe that a neural network trained to find Nash equilibria in a known subset of games will use self-taught rules developed endogenously when facing new games. These rules are close to payoff dominance and its best response. Our findings are consistent with existing experimental results, both in terms of subject's methodology and success rates.

Suggested Citation

  • Sgroi, Daniel & Zizzo, Daniel John, 2009. "Learning to play 3×3 games: Neural networks as bounded-rational players," Journal of Economic Behavior & Organization, Elsevier, vol. 69(1), pages 27-38, January.
    • Handle: RePEc:eee:jeborg:v:69:y:2009:i:1:p:27-38
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    3. Mohlin, Erik, 2012. "Evolution of theories of mind," Games and Economic Behavior, Elsevier, vol. 75(1), pages 299-318.
    4. Daniele Condorelli & Massimiliano Furlan, 2024. "Deep Learning to Play Games," Papers 2409.15197, arXiv.org.
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    7. Salle, Isabelle L., 2015. "Modeling expectations in agent-based models — An application to central bank's communication and monetary policy," Economic Modelling, Elsevier, vol. 46(C), pages 130-141.

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