Experimental Economics

, Volume 8, Issue 4, pp 369–388 | Cite as

Stochastic Choice and the Allocation of Cognitive Effort



Data from a risky choice experiment are used to estimate a fully parametric stochastic model of risky choice. As is usual with such analyses, Expected Utility Theory is rejected in favour of a form of Rank Dependent Theory. Then an estimate of the risk aversion parameter is deduced for each subject, and this is used to construct a measure of the “closeness to indifference'' of each subject in each choice problem. This measure is then used as an explanatory variable in a random effects model of decision time, with other explanatory variables being the complexity of the problem, the financial incentives, and the amount of experience accumulated at the time of performing the task. The most interesting finding is that significantly more effort is allocated to problems in which subjects are close to indifference. This presents us with another reason (in addition to statistical information considerations) why such tasks should play a prominent role in experiments.


risky choice rank dependent theory random effects decision times 


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  1. Bacharach, M. (2001). “Choice Without Preference: A Study of Decision Making in Buridan Problems.” mimeo, University of Oxford.Google Scholar
  2. Buschena, D. and Zilberman, D. (2000). “Generalized Expected Utility, Heteroscedastic Error, and Path Dependence in Risky Choice.” Journal of Riskand Uncertainty. 20, 67–88.Google Scholar
  3. Camerer, C.F. and Hogarth, R.M. (1999). “The Effects of Financial Incentives in Experiments: A Review and Capital-labor-production Framework.” Journal of Risk and Uncertainty. 19, 7–42.CrossRefGoogle Scholar
  4. Cleveland, W.S. (1979). “Robust Locally Weighted Regression and Smoothing Scatterplots.” Journal of the American Statistical Association. 74, 829–836.Google Scholar
  5. Hey, J.D. (1995). “Experimental Investigations of Errors in Decision Making Under Risk.” European Economic Review. 39, 633–640.CrossRefGoogle Scholar
  6. Hey, J.D.(2001). “Does Repetition Improve Consistency.” Experimental Economics. 4, 5–54.Google Scholar
  7. Hey, J.D. and Orme, C. (1994). “Investigating Generalisations of Expected Utility Theory Using Experimental Data.” Econometrica. 62, 1291–1326.Google Scholar
  8. Huber, J. and Zwerina, K. (1996). “The Importance of Utility Balance in Efficient Choice Designs.” Journal of Marketing Research. 23, 307–17.Google Scholar
  9. Little, I.M.D. (1949). “A Reformulation of the Theory of Consumer's Behaviour.” Oxford Economic Papers. 1, 90–99.Google Scholar
  10. Loomes, G., Moffatt, P.G. and Sugden, R. (2002). “A Microeconometric Testof Aalternative Stochastic Theories of Risky Choice.” Journal of Risk and Uncertainty. 24, 103–130.CrossRefGoogle Scholar
  11. Loomes, G. and Sugden, R. (1998). “Testing Different StochasticSpecifications of Risky Choice.” Economica. 65, 581–598.CrossRefGoogle Scholar
  12. Moffatt, P.G. and Peters S.A. (2001). “Testing for the Presence of a Tremble in Economic Experiments.” Experimental Economics. 4, 221–228.Google Scholar
  13. Prelec, D. (1998). “The Probability Weighting Function.” Econometrica. 66, 497–527.Google Scholar
  14. Tversky, A. and Fox, C.R. (1994). “Weighting Risk and Uncertainty.” Psychological Review. 102, 269– 283.Google Scholar
  15. Wilcox, N.T. (1994). “On A Lottery Pricing Anomaly: Time Tells the Tale.” Journal of Risk and Uncertainty, 8, 311–324.Google Scholar
  16. Wu, G. and Gonzalez, R. (1996). “Curvature of the Probability Weighting Function.” Management Science, 42, 1676–1690.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.School of EconomicsUniversity of East AngliaNorwichUK

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