Using Neural Networks to Model Bounded Rationality in Interactive Decision-Making
Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
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.
Keywords: neural networks; bounded rationality; learning; repeated games; industrial organization (search for similar items in EconPapers)
JEL-codes: C72 D00 D83 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cbe, nep-cmp and nep-gth
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:0339
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