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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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",
• URL = "https://ideas.repec.org/a/jem/ejemed/1002.html",
• 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 = "Also known as \cite{RePEc:jem:ejemed:1002}",

}

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

Citations