IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpex/0211001.html
   My bibliography  Save this paper

Behavior in a dynamic decision problem: An analysis of experimental evidence using a bayesian type classification algorithm

Author

Listed:
  • Daniel Houser

    (George Mason University)

  • Michael Keane

    (Yale University)

  • Kevin McCabe

    (George Mason University)

Abstract

It has been long recognized that different people may use different strategies, or decision rules, when playing games or dealing with other complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules that are present in a population of agents. We show that the algorithm performs well in both a Monte Carlo study and in an empirical application. We apply our procedure to analyze the actual behavior of subjects who are confronted with a difficult dynamic stochastic decision problem in a laboratory setting. The procedure does an excellent job of grouping the subjects into easily interpretable types. Given the difficultly of the decision problem, we were surprised to find that nearly a third of subjects were a “Near Rational” type that played a good approximation to the optimal decision rule. More than 40% of subjects followed a rule that we describe as “fatalistic,” since they play as if they don’t appreciate the extent to which payoffs are a controlled stochastic process. And about a quarter of the subjects are classified as “Confused,” since they play the game quite poorly. Interestingly, we find that those subjects who practiced most before playing the game for money were the most likely to play poorly. Thus, lack of effort does not seem to account for poor performance. It is our hope that, in future work, our type classification algorithm will facilitate the positive analysis of peoples’ behavior in many types of complex decision problems.

Suggested Citation

  • 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, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpex:0211001
    Note: Type of Document - PDF; prepared on IBM PC; to print on PostScript; pages: 60 ; figures: included
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/exp/papers/0211/0211001.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. McKelvey, Richard D & Palfrey, Thomas R, 1992. "An Experimental Study of the Centipede Game," Econometrica, Econometric Society, vol. 60(4), pages 803-836, July.
    5. 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.
    6. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    7. Andreoni, James, 1995. "Cooperation in Public-Goods Experiments: Kindness or Confusion?," American Economic Review, American Economic Association, vol. 85(4), pages 891-904, September.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Harrison, Glenn W & Morgan, Peter, 1990. "Search Intensity in Experiments," Economic Journal, Royal Economic Society, vol. 100(401), pages 478-486, June.
    16. 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.
    17. 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.
    18. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. Hey, John D., 1987. "Still searching," Journal of Economic Behavior & Organization, Elsevier, vol. 8(1), pages 137-144, March.
    26. 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.
    27. 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.
    28. 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.
    29. Geweke, John & Houser, Dan & Keane, Michael, 1999. "Simulation Based Inference for Dynamic Multinomial Choice Models," MPRA Paper 54279, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rothlauf, Franz & Schunk, Daniel & Pfeiffer, Jella, 2005. "Classification of human decision behavior : finding modular decision rules with genetic algorithms," Papers 05-04, Sonderforschungsbreich 504.
    2. Houser, Daniel & Winter, Joachim, 2000. "Time preference and decision rules in a price search experiment," Sonderforschungsbereich 504 Publications 01-34, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    3. Philip A. Haile & Ali Hortaçsu & Grigory Kosenok, 2008. "On the Empirical Content of Quantal Response Equilibrium," American Economic Review, American Economic Association, vol. 98(1), pages 180-200, March.
    4. Schunk, Daniel & Winter, Joachim, 2009. "The relationship between risk attitudes and heuristics in search tasks: A laboratory experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 71(2), pages 347-360, August.
    5. 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.
    6. Schunk, Daniel, 2005. "Search behaviour with reference point preferences : theory and experimental evidence," Papers 05-12, Sonderforschungsbreich 504.
    7. repec:pit:wpaper:334 is not listed on IDEAS
    8. James Cox & Ronald Oaxaca, 2000. "Good News and Bad News: Search from Unknown Wage Offer Distributions," Experimental Economics, Springer;Economic Science Association, vol. 2(3), pages 197-225, March.
    9. Boone, Jan & Sadrieh, Abdolkarim & van Ours, Jan C., 2009. "Experiments on unemployment benefit sanctions and job search behavior," European Economic Review, Elsevier, vol. 53(8), pages 937-951, November.
    10. Jacob K. Goeree & Charles A. Holt, 2001. "Ten Little Treasures of Game Theory and Ten Intuitive Contradictions," American Economic Review, American Economic Association, vol. 91(5), pages 1402-1422, December.
    11. Breitmoser, Yves, 2019. "Knowing me, imagining you: Projection and overbidding in auctions," Games and Economic Behavior, Elsevier, vol. 113(C), pages 423-447.
    12. Michael P. Keane, 2011. "Labor Supply and Taxes: A Survey," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 961-1075, December.
    13. Daniel Friedman & Kai Pommerenke & Rajan Lukose & Garrett Milam & Bernardo Huberman, 2007. "Searching for the sunk cost fallacy," Experimental Economics, Springer;Economic Science Association, vol. 10(1), pages 79-104, March.
    14. Daniela Cagno & Tibor Neugebauer & Carlos Rodriguez-Palmero & Abdolkarim Sadrieh, 2014. "Recall searching with and without recall," Theory and Decision, Springer, vol. 77(3), pages 297-311, October.
    15. Robert M. Sauer, 2015. "Does It Pay For Women To Volunteer?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(2), pages 537-564, May.
    16. Shachat, Jason & Swarthout, J. Todd, 2012. "Learning about learning in games through experimental control of strategic interdependence," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 383-402.
    17. Sonnemans, Joep, 1998. "Strategies of search," Journal of Economic Behavior & Organization, Elsevier, vol. 35(3), pages 309-332, April.
    18. Nicolas Jacquemet & Olivier L’Haridon & Isabelle Vialle, 2014. "Marché du travail, évaluation et économie expérimentale," Revue française d'économie, Presses de Sciences-Po, vol. 0(1), pages 189-226.
    19. Camerer, Colin F. & Ho, Teck-Hua & Chong, Juin-Kuan, 2002. "Sophisticated Experience-Weighted Attraction Learning and Strategic Teaching in Repeated Games," Journal of Economic Theory, Elsevier, vol. 104(1), pages 137-188, May.
    20. Jacob Goeree & Charles Holt & Thomas Palfrey, 2005. "Regular Quantal Response Equilibrium," Experimental Economics, Springer;Economic Science Association, vol. 8(4), pages 347-367, December.
    21. ENGLE-WARNICK, Jim & McCAUSLAND, William J. & MILLER, John H., 2004. "The Ghost in the Machine: Inferring Machine-Based Strategies from Observed Behavior," Cahiers de recherche 2004-11, Universite de Montreal, Departement de sciences economiques.

    More about this item

    Keywords

    behavioral experiments type-classification bayesian;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpex:0211001. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: EconWPA (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.