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

By Leonidas Spiliopoulos

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.behavioral game theory; neural networks; learning

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Citations

  1. (1988). A General Theory of Equilibrium Selection in Games,
  2. (2002). Detecting failures of backward induction: Monitoring information search in sequential bargaining experiments,
  3. (1989). Multi-layer feedforward networks are universal approximators,

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