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Evolutionary Detection of New Classes of Equilibria: Application in Behavioral Games

  • Dumitru Dumitrescu
  • Rodica Ioana Lung
  • Réka Nagy
  • Daniela Zaharie
  • Attila Bartha
  • Doina Logofătu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6239)

Abstract

Standard game theory relies on the assumption that players are rational decision makers that try to maximize their payoffs. Experiments with human players show that real people rarely follow the predictions of normative theory. Our aim is to model the human behavior accurately. Several classes of equilibria (Nash, Pareto, Nash-Pareto and fuzzy Nash-Pareto) are considered by using appropriate generative relations. Three versions of the centipede game are used to illustrate the different types of equilibrium.

Based on a study of how people play the centipede game, an equilibrium configuration that models the human behavior is detected. This configuration is a joint equilibrium obtained as a fuzzy combination of Nash and Pareto equilibria. In this way a connection between normative theory, computational game theory and behavioral games is established.

Keywords

Games Equilibrium Evolutionary Detection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dumitru Dumitrescu
    • 1
  • Rodica Ioana Lung
    • 1
  • Réka Nagy
    • 1
  • Daniela Zaharie
    • 2
  • Attila Bartha
    • 1
  • Doina Logofătu
    • 3
  1. 1.Babes-Bolyai UniversityCluj NapocaRomania
  2. 2.West UniversityTimisoaraRomania
  3. 3.University of Applied SciencesMunichGermany

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