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Stochastic Choice and the Allocation of Cognitive Effort

Published online by Cambridge University Press:  14 March 2025

Peter G. Moffatt
Peter G. Moffatt*
Affiliation:
School of Economics, University of East Anglia, Norwich, UK
*
email: [email protected]
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Abstract

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.

Keywords

risky choicerank dependent theoryrandom effectsdecision times

JEL classification

C23: Panel Data Models • Spatio-temporal Models C91: Laboratory, Individual Behavior D81: Criteria for Decision-Making under Risk and Uncertainty
Type
Research Article
Information
Experimental Economics , Volume 8 , Issue 4: Exploring the Error in Experimental Economics , December 2005 , pp. 369 - 388
Copyright
Copyright © 2005 Economic Science Association

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References

Bacharach, M. (2001). “Choice Without Preference: A Study of Decision Making in Buridan Problems.” mimeo, University of Oxford.Google Scholar
Buschena, D. and Zilberman, D. (2000). “Generalized Expected Utility, Heteroscedastic Error, and Path Dependence in Risky Choice.” Journal of Risk and Uncertainty. 20, 67–88.CrossRefGoogle Scholar
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
Cleveland, W.S. (1979). “Robust Locally Weighted Regression and Smoothing Scatterplots.” Journal of the American Statistical Association. 74, 829–836.CrossRefGoogle Scholar
Hey, J.D. (1995). “Experimental Investigations of Errors in Decision Making Under Risk.” European Economic Review. 39, 633–640.CrossRefGoogle Scholar
Hey, J.D. (2001). “Does Repetition Improve Consistency.” Experimental Economics. 4, 5–54.CrossRefGoogle Scholar
Hey, J.D. and Orme, C. (1994). “Investigating Generalisations of Expected Utility Theory Using Experimental Data.” Econometrica. 62, 1291–1326.CrossRefGoogle Scholar
Huber, J. and Zwerina, K. (1996). “The Importance of Utility Balance in Efficient Choice Designs.” Journal of Marketing Research. 23, 307–17.Google Scholar
Little, I.M.D. (1949). “A Reformulation of the Theory of Consumer's Behaviour.” Oxford Economic Papers. 1, 90–99.CrossRefGoogle Scholar
Loomes, G., Moffatt, P.G. and Sugden, R. (2002). “A Microeconometric Test of Aalternative Stochastic Theories of Risky Choice.” Journal of Risk and Uncertainty. 24, 103–130.CrossRefGoogle Scholar
Loomes, G. and Sugden, R. (1998). “Testing Different Stochastic Specifications of Risky Choice.” Economica. 65, 581–598.CrossRefGoogle Scholar
Moffatt, P.G. and Peters, S.A. (2001). “Testing for the Presence of a Tremble in Economic Experiments.” Experimental Economics. 4, 221–228.CrossRefGoogle Scholar
Prelec, D. (1998). “The Probability Weighting Function.” Econometrica. 66, 497–527.CrossRefGoogle Scholar
Tversky, A. and Fox, C.R. (1994). “Weighting Risk and Uncertainty.” Psychological Review. 102, 269283.Google Scholar
Wilcox, N.T. (1994). “On A Lottery Pricing Anomaly: Time Tells the Tale.” Journal of Risk and Uncertainty, 8, 311–324.Google Scholar
Wu, G. and Gonzalez, R. (1996). “Curvature of the Probability Weighting Function.” Management Science, 42, 1676–1690.CrossRefGoogle Scholar