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Can the human mind learn to backward induce? A neural network answer

Leonidas Spiliopoulos

Game Theory and Information from University Library of Munich, Germany

Abstract: This paper addresses the question of whether neural networks, a realistic cognitive model of the human information processing, can learn to backward induce in a two stage game with a unique subgame-perfect Nash Equilibrium. The result that the neural networks only learn a heuristic that approximates the desired output and does not backward induce is in accordance with the documented difficulty of humans to apply backward induction and their dependence on heuristics.

Keywords: behavioral game theory; neural networks; learning (search for similar items in EconPapers)
JEL-codes: C7 D8 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cbe
Date: 2005-05-30
Note: Type of Document - pdf; pages: 9
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