Neural networks and bounded rationality
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.
Volume (Year): 375 (2007)
Issue (Month): 2 ()
|Contact details of provider:|| Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:375:y:2007:i:2:p:717-725. See general information about how to correct material in RePEc.
If references are entirely missing, you can add them using this form.