Using Symbolic Regression to Infer Strategies from Experimental Data

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

  author =       "John Duffy and Jim Engle-Warnick",
  title =        "Using Symbolic Regression to Infer Strategies from
                 Experimental Data",
  booktitle =    "Evolutionary Computation in Economics and Finance",
  publisher =    "Physica Verlag",
  year =         "2002",
  editor =       "Shu-Heng Chen",
  volume =       "100",
  series =       "Studies in Fuzziness and Soft Computing",
  chapter =      "4",
  pages =        "61--82",
  month =        "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-7908-1476-8",
  URL =          "",
  DOI =          "doi:10.1007/978-3-7908-1784-3_4",
  abstract =     "We propose the use of a new technique -- symbolic
                 regression -- as a method for inferring the strategies
                 that are being played by subjects in economic decision
                 making experiments. We begin by describing symbolic
                 regression and our implementation of this technique
                 using genetic programming. We provide a brief overview
                 of how our algorithm works and how it can be used to
                 uncover simple data generating functions that have the
                 flavor of strategic rules. We then apply symbolic
                 regression using genetic programming to experimental
                 data from the ultimatum game. We discuss and analyze
                 the strategies that we uncover using symbolic
                 regression and we conclude by arguing that symbolic
                 regression techniques should at least complement
                 standard regression analyses of experimental data.",
  notes =        "Presented at CEF'99 (see \cite{duffy:1999:CEF})
  size =         "21 pages",

Genetic Programming entries for John Duffy Jim Engle-Warnick