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Using Symbolic Regression to Infer Strategies from Experimental Data

Author

Listed:
  • John Duffy

    (University of Pittsburgh)

  • Jim Warnick

    (University of Pittsburgh)

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 repeated 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.

Suggested Citation

  • John Duffy & Jim Warnick, 1999. "Using Symbolic Regression to Infer Strategies from Experimental Data," Computing in Economics and Finance 1999 1033, Society for Computational Economics.
    • Handle: RePEc:sce:scecf9:1033
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    Cited by:

    1. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    2. Jim Warnick, 1999. "I Can't Think With All This Noise: Inferring Strategies Using Symbolic Regression," Working Papers 99-08-057, Santa Fe Institute.
    3. Jim Engle-Warnick & Bradley Ruffle, 2006. "The Strategies Behind Their Actions: A Method To Infer Repeated-Game Strategies And An Application To Buyer Behavior," Departmental Working Papers 2005-04, McGill University, Department of Economics.
    4. Daniel Houser & Michael Keane & Kevin McCabe, 2004. "Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm," Econometrica, Econometric Society, vol. 72(3), pages 781-822, May.
    5. Engle-Warnick, Jim, 2003. "Inferring strategies from observed actions: a nonparametric, binary tree classification approach," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11-12), pages 2151-2170, September.

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