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The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment

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  • Daniel Schunk

    ()

  • Joachim Winter

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

Abstract

The existing evidence from laboratory experiments suggests that relatively simple heuristics describe observed search behavior better than the optimal stopping rule derived under risk neutrality. Such behavior could be generated by two entirely different classes of decision rules: (i) rules that are optimal conditional on utility functions that depart from risk neutrality or (ii) heuristics that derive from limited cognitive processing capacities and satisfycing. In this paper, we develop and test search models that depart from the standard assumption of risk neutrality in order to distinguish these two possibilities. In our experiment, we present subjects not only with a standard search task, but also with a series of lottery tasks that serve to elicit the shape of their utility functions. We do not find a relationship between behavior in the search task and measures of risk aversion. Our data suggest, however, that loss aversion is important for explaining search behavior.

Suggested Citation

  • Daniel Schunk & Joachim Winter, 2005. "The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment," MEA discussion paper series 05077, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    • Handle: RePEc:mea:meawpa:05077
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    References listed on IDEAS

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    Cited by:

    1. Gil-Lacruz, Ana I. & Gil-Lacruz, Marta, 2011. "Internal Inconsistency and Risk Aversion: Implications on Smoking Decisions/Consistencia interna y aversión al riesgo: implicaciones en la decisión de fumar," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 29, pages 387(18á.)-3, Abril.
    2. Inukai, Keigo & Kawata, Keisuke & Sasaki, Masaru, 2017. "Committee Search with Ex-ante Heterogeneous Agents: Theory and Experimental Evidence," IZA Discussion Papers 10760, Institute for the Study of Labor (IZA).
    3. Börsch-Supan, Axel & Coppola, Michela & Reil-Held, Anette, 1970. "Riester Pensions in Germany: Design, Dynamics, Targetting Success and Crowding-In," MEA discussion paper series 201220, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    4. Therese Grijalva & Jayson Lusk & W. Shaw, 2014. "Discounting the Distant Future: An Experimental Investigation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(1), pages 39-63, September.
    5. 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.
    6. Franz Rothlauf & Daniel Schunk & Jella Pfeiffer, 2005. "Classification of Human Decision Behavior: Finding Modular Decision Rules with Genetic Algorithms," MEA discussion paper series 05079, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    7. Hizen, Yoichi & Kawata, Keisuke & Sasaki, Masaru, 2013. "An experimental test of a committee search model," European Economic Review, Elsevier, vol. 61(C), pages 59-76.
    8. Arnaud Reynaud & Stéphane Couture, 2012. "Stability of risk preference measures: results from a field experiment on French farmers," Theory and Decision, Springer, vol. 73(2), pages 203-221, August.
    9. Takao Asano & Hiroko Okudaira & Masaru Sasaki, 2015. "An experimental test of a search model under ambiguity," Theory and Decision, Springer, vol. 79(4), pages 627-637, December.
    10. Charness, Gary & Kuhn, Peter, 2011. "Lab Labor: What Can Labor Economists Learn from the Lab?," Handbook of Labor Economics, Elsevier.
    11. Marcela Ibanez & Simon Czermak & Matthias Sutter, "undated". "Searching for a better deal ? On the influence of group decision making, time pressure and gender in a search experiment," Working Papers 2008-05, Faculty of Economics and Statistics, University of Innsbruck.
    12. Schunk, Daniel, 2005. "Search behaviour with reference point preferences : theory and experimental evidence," Papers 05-12, Sonderforschungsbreich 504.
    13. Schunk, Daniel, 2009. "Behavioral heterogeneity in dynamic search situations: Theory and experimental evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 33(9), pages 1719-1738, September.
    14. Eriksson, Kimmo & Strimling, Pontus, 2010. "The devil is in the details: Incorrect intuitions in optimal search," Journal of Economic Behavior & Organization, Elsevier, vol. 75(2), pages 338-347, August.
    15. Goecke, Henry & Luhan, Wolfgang J. & Roos, Michael W.M., 2013. "Rational inattentiveness in a forecasting experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 94(C), pages 80-89.
    16. Takao Asano & Hiroko Okudaira & Masaru Sasaki, 2015. "An Experimental Test of a Search Model under Ambiguity," KIER Working Papers 913, Kyoto University, Institute of Economic Research.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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