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Classification of Human Decision Behavior: Finding Modular Decision Rules with Genetic Algorithms

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  • Franz Rothlauf
  • Daniel Schunk

    ()

  • Jella Pfeiffer

    (Munich Center for the Economics of Aging (MEA))

Abstract

The understanding of human behavior in sequential decision tasks is important for economics and socio-psychological sciences. In search tasks, for example when individuals search for the best price of a product, they are confronted in sequential steps with different situations and they have to decide whether to continue or stop searching. The decision behavior of individuals in such search tasks is described by a search strategy. This paper presents a new approach of finding high-quality search strategies by using genetic algorithms (GAs). Only the structure of the search strategies and the basic building blocks (price thresholds and price patterns) that can be used for the search strategies are pre-specified. It is the purpose of the GA to construct search strategies that well describe human search behavior. The search strategies found by the GA are able to predict human behavior in search tasks better than traditional search strategies from the literature which are usually based on theoretical assumptions about human behavior in search tasks. Furthermore, the found search strategies are reasonable in the sense that they can be well interpreted, and generally that means they describe the search behavior of a larger group of individuals and allow some kind of categorization and classification. The results of this study open a new perspective for future research in developing behavioral strategies. Instead of deriving search strategies from theoretical assumptions about human behavior, researchers can directly analyze human behavior in search tasks and find appropriate and high- quality search strategies. These can be used for gaining new insights into the motivation behind human search and for developing new theoretical models about human search behavior.

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Bibliographic Info

Paper provided by Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy in its series MEA discussion paper series with number 05079.

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Date of creation: 21 Jun 2005
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Handle: RePEc:mea:meawpa:05079

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References

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  1. Schunk, Daniel & Winter, Joachim, 2007. "The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment," Discussion Papers in Economics 1377, University of Munich, Department of Economics.
  2. Daniel Houser & Michael Keane & Kevin McCabe, 2002. "Behavior in a dynamic decision problem: An analysis of experimental evidence using a bayesian type classification algorithm," Experimental 0211001, EconWPA.
  3. El-Gamal, Mahmoud A. & Grether, David M., 1995. "Are People Bayesian? Uncovering Behavioral Strategies," Working Papers 919, California Institute of Technology, Division of the Humanities and Social Sciences.
  4. Eckstein, Zvi & Mortensen, Dale T., 2006. "Labor search," European Economic Review, Elsevier, vol. 50(4), pages 807-810, May.
  5. Houser, Daniel & Winter, Joachim, 2004. "How Do Behavioral Assumptions Affect Structural Inference? Evidence from a Laboratory Experiment," Munich Reprints in Economics 19372, University of Munich, Department of Economics.
  6. Sonnemans, Joep, 1998. "Strategies of search," Journal of Economic Behavior & Organization, Elsevier, vol. 35(3), pages 309-332, April.
  7. Hey, John D., 1981. "Are optimal search rules reasonable? and vice versa? (And does it matter anyway?)," Journal of Economic Behavior & Organization, Elsevier, vol. 2(1), pages 47-70, March.
  8. Jim Engle-Warnick, 2000. "Inferring Strategies from Observed Actions: A Nonparametric Binary Tree Classification Approach," Econometrics 0004002, EconWPA, revised 02 Aug 2001.
  9. Eckstein, Zvi & van den Berg, Gerard J, 2004. "Empirical Labour Search: A Survey," CEPR Discussion Papers 4199, C.E.P.R. Discussion Papers.
  10. Sonnemans, Joep, 2000. "Decisions and strategies in a sequential search experiment," Journal of Economic Psychology, Elsevier, vol. 21(1), pages 91-102, February.
  11. Hey, John D., 1982. "Search for rules for search," Journal of Economic Behavior & Organization, Elsevier, vol. 3(1), pages 65-81, March.
  12. Hey, John D., 1987. "Still searching," Journal of Economic Behavior & Organization, Elsevier, vol. 8(1), pages 137-144, March.
  13. Lippman, Steven A & McCall, John J, 1976. "The Economics of Job Search: A Survey," Economic Inquiry, Western Economic Association International, vol. 14(3), pages 347-68, September.
  14. Harrison, Glenn W & Morgan, Peter, 1990. "Search Intensity in Experiments," Economic Journal, Royal Economic Society, vol. 100(401), pages 478-86, June.
  15. El-Gamal, Mahmoud A. & Palfrey, Thomas R., 1995. "Vertigo: Comparing structural models of imperfect behavior in experimental games," Games and Economic Behavior, Elsevier, vol. 8(2), pages 322-348.
  16. McKelvey, Richard D & Palfrey, Thomas R, 1992. "An Experimental Study of the Centipede Game," Econometrica, Econometric Society, vol. 60(4), pages 803-36, July.
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