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Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

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  • Daniel Houser
  • Michael Keane
  • Kevin McCabe

Abstract

Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confronted with a difficult dynamic stochastic decision problem. Subjects practiced before playing for money. Based on money round decisions, our procedure classifies subjects into three types, which we label "Near Rational,""Fatalist," and "Confused." There is clear evidence of continuity in subjects' behaviors between the practice and money rounds: types who performed best in practice also tended to perform best when playing for money. However, the agreement between practice and money play is far from perfect. The divergences appear to be well explained by a combination of type switching (due to learning and/or increased effort in money play) and errors in our probabilistic type assignments. Copyright The Econometric Society 2004.

Suggested Citation

  • 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.
    • Handle: RePEc:ecm:emetrp:v:72:y:2004:i:3:p:781-822
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    1. 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.
    2. McKelvey, Richard D & Palfrey, Thomas R, 1992. "An Experimental Study of the Centipede Game," Econometrica, Econometric Society, vol. 60(4), pages 803-836, July.
    3. Haltiwanger, John & Waldman, Michael, 1985. "Rational Expectations and the Limits of Rationality: An Analysis of Heterogeneity," American Economic Review, American Economic Association, vol. 75(3), pages 326-340, June.
    4. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
    5. Goeree, Jacob K. & Holt, Charles A. & Palfrey, Thomas R., 2002. "Quantal Response Equilibrium and Overbidding in Private-Value Auctions," Journal of Economic Theory, Elsevier, vol. 104(1), pages 247-272, May.
    6. Cyert, Richard M & DeGroot, Morris H, 1974. "Rational Expectations and Bayesian Analysis," Journal of Political Economy, University of Chicago Press, vol. 82(3), pages 521-536, May/June.
    7. Houser, Daniel & Winter, Joachim, 2004. "How Do Behavioral Assumptions Affect Structural Inference? Evidence from a Laboratory Experiment," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 64-79, January.
    8. McCabe, Kevin & Houser, Daniel & Ryan, Lee & Smith, Vernon & Trouard, Ted, 2001. "A Functional Imaging Study of Cooperation in Two-Person reciprocal Exchange," MPRA Paper 5172, University Library of Munich, Germany.
    9. Braunstein, Yale M & Schotter, Andrew, 1982. "Labor Market Search: An Experimental Study," Economic Inquiry, Western Economic Association International, vol. 20(1), pages 133-144, January.
    10. Andreoni, James, 1995. "Cooperation in Public-Goods Experiments: Kindness or Confusion?," American Economic Review, American Economic Association, vol. 85(4), pages 891-904, September.
    11. Geweke, John & Keane, Michael, 2001. "Computationally intensive methods for integration in econometrics," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.),Handbook of Econometrics, edition 1, volume 5, chapter 56, pages 3463-3568, Elsevier.
    12. Charles A. Holt & Jacob K. Goeree, 1999. "Stochastic Game Theory: For Playing Games, Not Just for Doing Theory," Virginia Economics Online Papers 306, University of Virginia, Department of Economics.
    13. Harrison, Glenn W & Morgan, Peter, 1990. "Search Intensity in Experiments," Economic Journal, Royal Economic Society, vol. 100(401), pages 478-486, June.
    14. Keane, Michael P & Wolpin, Kenneth I, 1994. "The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 648-672, November.
    15. Daniel Houser & Robert Kurzban, 2002. "Revisiting Kindness and Confusion in Public Goods Experiments," American Economic Review, American Economic Association, vol. 92(4), pages 1062-1069, September.
    16. Goeree, Jacob K. & Holt, Charles A. & Palfrey, Thomas R., 2003. "Risk averse behavior in generalized matching pennies games," Games and Economic Behavior, Elsevier, vol. 45(1), pages 97-113, October.
    17. 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.
    18. Cox, James C & Oaxaca, Ronald L, 1992. "Direct Tests of the Reservation Wage Property," Economic Journal, Royal Economic Society, vol. 102(415), pages 1423-1432, November.
    19. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 612-643, August.
    20. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
    21. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    22. Cox, James C & Oaxaca, Ronald L, 1989. "Laboratory Experiments with a Finite-Horizon Job-Search Model," Journal of Risk and Uncertainty, Springer, vol. 2(3), pages 301-329, September.
    23. Keane, Michael P & Wolpin, Kenneth I, 1997. "The Career Decisions of Young Men," Journal of Political Economy, University of Chicago Press, vol. 105(3), pages 473-522, June.
    24. Geweke, John & Houser, Dan & Keane, Michael, 1999. "Simulation Based Inference for Dynamic Multinomial Choice Models," MPRA Paper 54279, University Library of Munich, Germany.
    25. 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.
    26. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    27. Houser, Daniel, 2003. "Bayesian analysis of a dynamic stochastic model of labor supply and saving," Journal of Econometrics, Elsevier, vol. 113(2), pages 289-335, April.
    28. Hey, John D., 1987. "Still searching," Journal of Economic Behavior & Organization, Elsevier, vol. 8(1), pages 137-144, March.
    29. 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.
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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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