Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Equilibria Detection in Non-cooperative Games
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Fuzzy nash-pareto equilibrium: concepts and evolutionary detection
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Between selfishness and altruism: fuzzy nash--berge-zhukovskii equilibrium
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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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.