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Using Neural Networks to Model Bounded Rationality in Interactive Decision-Making

Author

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  • Sgroi, D.

Abstract

This paper considers the use of neural networks to model bounded rational behaviour. The underlying theory and use of neural networks is now a component of various forms of scientific enquiry, be it modelling artificial intelligence, developing better pattern recognition or solving complex optimization problems. This paper surveys the recent literature in economics on their use as a plausible model of learning by example, in which the focus is not on improving their ability to perform to the point of zero error, but rather examining the sorts of errors they make and comparing these with observed bounded rational behaviour.

Suggested Citation

  • Sgroi, D., 2003. "Using Neural Networks to Model Bounded Rationality in Interactive Decision-Making," Cambridge Working Papers in Economics 0339, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:0339
    Note: IO, ET
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    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe0339.pdf
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    Cited by:

    1. Jason Barr & Francesco Saraceno, 2005. "Modeling the Firm as an Artificial Neural Network," Working Papers Rutgers University, Newark 2005-011, Department of Economics, Rutgers University, Newark.
    2. Fioretti, Guido, 2006. "Recognising investment opportunities at the onset of recoveries," Research in Economics, Elsevier, vol. 60(2), pages 69-84, June.

    More about this item

    Keywords

    neural networks; bounded rationality; learning; repeated games; industrial organization;

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D00 - Microeconomics - - General - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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