Equilibrium Selection via Adaptation: Using Genetic Programming to Model Learning in a Coordination Game

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Chen:2002:EJEMED,
  author =       "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
  title =        "Equilibrium Selection via Adaptation: Using Genetic
                 Programming to Model Learning in a Coordination Game",
  journal =      "The Electronic Journal of Evolutionary Modeling and
                 Economic Dynamics",
  year =         "2002",
  month =        "15 " # jan,
  keywords =     "genetic algorithms, genetic programming, Adaptation,
                 Coordination Game, Equilibrium Selection, Survival of
                 the Fittest",
  ISSN =         "1298-0137",
  URL =          "http://sclab.mis.yzu.edu.tw/faculty/yeh/paper/2002/e-jemed2002.pdf",
  broken =       "http://beagle.montesquieu.u-bordeaux.fr/jemed/1002/",
  size =         "44 pages",
  abstract =     "This paper models adaptive learning behavior in a
                 simple coordination game that Van Huyck, Cook and
                 Battalio (1994) have investigated in a controlled
                 laboratory setting with human subjects. We consider how
                 populations of artificially intelligent players behave
                 when playing the same game. We use the genetic
                 programming paradigm, as developed by Koza (1992,
                 1994), to model how a population of players might learn
                 over time. In genetic programming one seeks to breed
                 and evolve highly fit computer programs that are
                 capable of solving a given problem. In our application,
                 each computer program in the population can be viewed
                 as an individual agent's forecast rule. The various
                 forecast rules (programs) then repeatedly take part in
                 the coordination game evolving and adapting over time
                 according to principles of natural selection and
                 population genetics. We argue that the genetic
                 programming paradigm that we use has certain advantages
                 over other models of adaptive learning behavior in the
                 context of the coordination game that we consider. We
                 find that the pattern of behavior generated by our
                 population of artificially intelligent players is
                 remarkably similar to that followed by the human
                 subjects who played the same game. In particular, we
                 find that a steady state that is theoretically unstable
                 under a myopic, bestresponse learning dynamic turns out
                 to be stable under our genetic programming based
                 learning system, in accordance with Van Huyck et al.'s
                 (1994) finding using human subjects. We conclude that
                 genetic programming techniques may serve as a plausible
                 mechanism for modelling human behavior, and may also
                 serve as a useful selection criterion in environments
                 with multiple equilibria.",
  notes =        "RePEc:jem:ejemed:1002",
}

Genetic Programming entries for Shu-Heng Chen John Duffy Chia Hsuan Yeh

Citations