Generalized Divide the Dollar

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Ashlock:2016:CECa,
  author =       "Daniel Ashlock and Garrison Greenwood",
  title =        "Generalized Divide the Dollar",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "343--350",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, FSM",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7743814",
  abstract =     "Divide the dollar is a two-player simultaneous derived
                 from a game invented by John Nash because its strategy
                 space has an entire subspace of Nash equilibria. This
                 study describes and explores a family of
                 generalizations of divide the dollar with easily
                 controlled properties. If we view divide the dollar as
                 modelling the process of making a bargain, then the
                 generalized game makes it easy to model the impact of
                 external subsidies on bargaining. Classical divide the
                 dollar is compared to four generalizations representing
                 a simple subsidy in three different amounts and a more
                 complex type of subsidy. The distribution of simple
                 strategies that arise under replicator dynamics is
                 compared to the bids that arise in populations of
                 evolving, adaptive agents. Agents are encoded using a
                 finite state representation that conditions its
                 transitions on the result of bargains. These results
                 fall into three categories, the first player obtains a
                 higher amount, the second one does, or the agents fail
                 to make a deal. The replicator dynamic results are
                 compared to obtain the naive degree of distortion
                 caused by the subsidies. The results for evolving
                 agents are then examined to figure out the degree to
                 which adaptation compensated for or amplifies this
                 distortion.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Daniel Ashlock Garrison W Greenwood

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