Using Symbolic Regression to Infer Strategies from Experimental Data
AbstractWe 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.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 1033.
Date of creation: 01 Mar 1999
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-1999-07-12 (All new papers)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- 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.
- Jim Engle-Warnick, 2000.
"Inferring Strategies from Observed Actions: A Nonparametric Binary Tree Classification Approach,"
0004002, EconWPA, revised 02 Aug 2001.
- 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.
- Jim Engle-Warnick, 2001. "Inferring Strategies from Observed Actions: A Nonparametric, Binary Tree Classification Approach," Economics Papers 2001-W14, Economics Group, Nuffield College, University of Oxford.
- Jim Warnick, 1999. "I Can't Think With All This Noise: Inferring Strategies Using Symbolic Regression," Working Papers 99-08-057, Santa Fe Institute.
- Daniel Houser & Michael Keane & Kevin McCabe, 2002.
"Behavior in a dynamic decision problem: An analysis of experimental evidence using a bayesian type classification algorithm,"
- 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, 05.
- John Duffy, 2004.
"Agent-Based Models and Human Subject Experiments,"
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.