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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.

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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 05077.

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Date of creation: 21 Jun 2005
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Handle: RePEc:mea:meawpa:05077

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Cited by:
  1. Couture, Stéphane & Reynaud, Arnaud, 2010. "Stability of Risk Preference Measures: Results from a Field Experiment on French Farmers," TSE Working Papers 10-151, Toulouse School of Economics (TSE).
  2. 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 (18 pá, Abril.
  3. Marcela Ibanez & Simon Czermak & Matthias Sutter, . "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.
  4. 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.
  5. 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.
  6. Charness, Gary & Kuhn, Peter J., 2010. "Lab Labor: What Can Labor Economists Learn from the Lab?," IZA Discussion Papers 4941, Institute for the Study of Labor (IZA).
  7. Daniela Di Cagno & Tibor Neugebauer & Carlos Rodriguez-Palmero & Abdolkarim Sadrieh, 2014. "Recall Searching with and without Recall," Working Papers 2014/14, Economics Department, Universitat Jaume I, Castellón (Spain).
  8. 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.
  9. 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.
  10. 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.

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