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Learning Backward Induction: A Neural Network Agent Approach

By Leonidas Spiliopoulos

Abstract

This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70% of the time in new games. Similarly to humans, the neural network agents are also found to suffer from subgame and truncation inconsistency, supporting the contention that they are appropriate models of general learning in humans. The agents were found to behave in a bounded rational manner as a result of the endogenous emergence of decision heuristics

Topics: Agent based computational economics, Backward induction, Learning models, Behavioral game theory, Simulations, Complex adaptive systems, Artificial intelligence, Neural networks
Year: 2011
DOI identifier: 10.1007/978-4-431-53907-0_5
OAI identifier: oai:repository.ust.hk:1783.1-32911
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