Equilibrium Selection by Co-evolution for Bargaining Problems under Incomplete Information about Time Preferences

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{NanlinJin:2005:CEC,
  author =       "Nanlin Jin",
  title =        "Equilibrium Selection by Co-evolution for Bargaining
                 Problems under Incomplete Information about Time
                 Preferences",
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
                 Computation",
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "3",
  pages =        "2661--2668",
  address =      "Edinburgh, UK",
  month =        "2-5 " # sep,
  organization = "IEEE",
  publisher =    "IEEE Press",
  email =        "njin@essex.ac.uk",
  keywords =     "genetic algorithms, genetic programming, co-evolution,
                 game theory",
  ISBN =         "0-7803-9363-5",
  URL =          "http://cswww.essex.ac.uk/Research/CSP/finance/papers/Jin-IncompleteInfo-Cec2005.pdf",
  DOI =          "doi:10.1109/CEC.2005.1555028",
  size =         "8 pages",
  abstract =     "The main purpose of this work is to measure the impact
                 of players' information completeness on the outcomes in
                 dynamic strategic games. We apply Co-evolutionary
                 Algorithms to solve four incomplete information
                 bargaining problems and investigate the experimental
                 outcomes on players' shares from agreements, the
                 efficiency of agreements and the evolutionary time for
                 convergence. Empirical analyses indicate that in the
                 absence of complete information on the counterpart(s)'
                 preferences, co-evolving populations are still able to
                 select equilibriums which are Pareto-efficient and
                 stationary. This property of the co-evolutionary
                 algorithm supports its future applications on complex
                 dynamic games.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the EPS, and
                 the IEE.",
}

Genetic Programming entries for Nanlin Jin

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