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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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