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 implemen-tation 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 repeated “ultimatum game.” We discuss and analyze the strategies that we uncover using symbolic re-gression and conclude by arguing that symbolic regression techniques should at least complement standard regression analyses of experimental data.
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References
Duffy J., Feltovich N. (1999) Does Observation of Others Affect Learning in Strategic Environments? An Experimental Study. International Journal of Game Theory 28, 131–152
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© 2002 Springer-Verlag Berlin Heidelberg
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Duffy, J., Engle-Warnick, J. (2002). Using Symbolic Regression to Infer Strategies from Experimental Data. In: Chen, SH. (eds) Evolutionary Computation in Economics and Finance. Studies in Fuzziness and Soft Computing, vol 100. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1784-3_4
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DOI: https://doi.org/10.1007/978-3-7908-1784-3_4
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2512-1
Online ISBN: 978-3-7908-1784-3
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