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Neural networks and bounded rationality

Author

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  • Sgroi, Daniel
  • Zizzo, Daniel J.

Abstract

Traditionally the emphasis in neural network research has been on improving their performance as a means of pattern recognition. Here we take an alternative approach and explore the remarkable similarity between the under-performance of neural networks trained to behave optimally in economic situations and observed human performance in the laboratory under similar circumstances. In particular, we show that neural networks are consistent with observed laboratory play in two very important senses. Firstly, they select a rule for behavior which appears very similar to that used by laboratory subjects. Secondly, using this rule they perform optimally only approximately 60% of the time.

Suggested Citation

  • Sgroi, Daniel & Zizzo, Daniel J., 2007. "Neural networks and bounded rationality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 717-725.
  • Handle: RePEc:eee:phsmap:v:375:y:2007:i:2:p:717-725
    DOI: 10.1016/j.physa.2006.10.026
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    References listed on IDEAS

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    1. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, January.
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    Cited by:

    1. Spiliopoulos, Leonidas, 2012. "Interactive learning in 2×2 normal form games by neural network agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5557-5562.
    2. Spiliopoulos, Leonidas, 2009. "Neural networks as a learning paradigm for general normal form games," MPRA Paper 16765, University Library of Munich, Germany.
    3. 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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