Using Symbolic Regression to Infer Strategies from Experimental Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InCollection{duffy:1999:srised,

• 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 = "http://www.pitt.edu/~jduffy/docs/Usr.ps",
• 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}) http://fmwww.bc.edu/cef99/sess/chen.cfp.html",
• size = "21 pages",

}

Genetic Programming entries for John Duffy Jim Engle-Warnick

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