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.
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Volume (Year): 375 (2007)
Issue (Month): 2 ()
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- Drew Fudenberg & David K. Levine, 1996.
"The Theory of Learning in Games,"
Levine's Working Paper Archive
624, David K. Levine.
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