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      Genetic neural networks to approximate feedback Nash equilibria in dynamic games

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      Author
      Alemdar, N. M.
      Sirakaya, S.
      Date
      2003
      Source Title
      Computers and Mathematics with Applications
      Print ISSN
      0898-1221
      Publisher
      Pergamon Press
      Volume
      46
      Issue
      11
      Pages
      1493 - 1509
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      This paper develops a general purpose numerical method to compute the feedback Nash equilibria in dynamic games. Players' feedback strategies are first approximated by neural networks which are then trained online by parallel genetic algorithms to search over all time-invariant equilibrium strategies synchronously. To eliminate the dependence of training on the initial conditions of the game, the players use the same stationary feedback policies (the same networks), to repeatedly play the game from a number of initial states at any generation. The fitness of a given feedback strategy is then computed as the sum of payoffs over all initial states. The evolutionary equilibrium of the game between the genetic algorithms is the feedback Nash equilibrium of the dynamic game. An oligopoly model with investment is approximated as a numerical example. (C) 2003 Elsevier Ltd. All rights reserved.
      Keywords
      Feedback Nash equilibrium
      Parallel genetic algorithms
      Neural networks
      Permalink
      http://hdl.handle.net/11693/13415
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/S0898-1221(03)90186-6
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      • Department of Economics 649
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