Can the human mind learn to backward induce? A neural network answer
AbstractThis 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.
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Bibliographic InfoPaper provided by EconWPA in its series Game Theory and Information with number 0505008.
Length: 9 pages
Date of creation: 30 May 2005
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Note: Type of Document - pdf; pages: 9
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behavioral game theory; neural networks; learning;
Find related papers by JEL classification:
- C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
- D8 - Microeconomics - - Information, Knowledge, and Uncertainty
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"A backward induction experiment,"
34, Wisconsin Madison - Social Systems.
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