Using Neural Networks to Model Bounded Rationality in Interactive Decision-Making
AbstractThis 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.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0339.
Date of creation: Sep 2003
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neural networks; bounded rationality; learning; repeated games; industrial organization;
Find related papers by 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, and Information
This paper has been announced in the following NEP Reports:
- NEP-CBE-2003-09-14 (Cognitive & Behavioural Economics)
- NEP-CMP-2003-09-14 (Computational Economics)
- NEP-GTH-2003-09-14 (Game Theory)
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