Using Symbolic Regression to Infer Strategies from Experimental Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{duffy:1999:srised,
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author = "John Duffy and Jim Engle-Warnick",
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title = "Using Symbolic Regression to Infer Strategies from
Experimental Data",
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booktitle = "Evolutionary Computation in Economics and Finance",
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publisher = "Physica Verlag",
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year = "2002",
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editor = "Shu-Heng Chen",
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volume = "100",
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series = "Studies in Fuzziness and Soft Computing",
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chapter = "4",
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pages = "61--82",
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month = "2002",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-7908-1476-8",
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URL = "http://www.pitt.edu/~jduffy/docs/Usr.ps",
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DOI = "doi:10.1007/978-3-7908-1784-3_4",
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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.",
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notes = "Presented at CEF'99 (see \cite{duffy:1999:CEF})
http://fmwww.bc.edu/cef99/sess/chen.cfp.html",
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size = "21 pages",
- }
Genetic Programming entries for
John Duffy
Jim Engle-Warnick
Citations