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

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Author Info
Leonidas Spiliopoulos (University of Sydney)

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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.

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File URL: http://129.3.20.41/eps/game/papers/0505/0505008.pdf
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Publisher Info
Paper provided by EconWPA in its series Game Theory and Information with number 0505008.

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Length: 9 pages
Date of creation: 30 May 2005
Date of revision:
Handle: RePEc:wpa:wuwpga:0505008

Note: Type of Document - pdf; pages: 9
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Web page: http://129.3.20.41

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Related research
Keywords: 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

This paper has been announced in the following NEP Reports:

References listed on IDEAS
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.:
  1. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March. [Downloadable!] (restricted)
  2. Binmore, Ken & McCarthy, John & Ponti, Giovanni & Samuelson, Larry & Shaked, Avner, 2002. "A Backward Induction Experiment," Journal of Economic Theory, Elsevier, vol. 104(1), pages 48-88, May. [Downloadable!] (restricted)
  3. D. Sgroi & D. J. Zizzo, 2002. "Strategy Learning in 3x3 Games by Neural Networks," Cambridge Working Papers in Economics 0207, Faculty of Economics, University of Cambridge. [Downloadable!]
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