Hostname: page-component-7b9c58cd5d-hxdxx Total loading time: 0 Render date: 2025-03-15T08:55:39.837Z Has data issue: false hasContentIssue false
  • English
  • Français

Changing the probability versus changing the reward

Published online by Cambridge University Press:  14 March 2025

David M. Bruner
David M. Bruner*
Affiliation:
Department of Economics, Appalachian State University, 3111 Raley Hall, 416 Howard St., Boone, NC 28608, USA
*
e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

There are two means of changing the expected value of a risk: changing the probability of a reward or changing the reward. Theoretically, the former produces a greater change in expected utility for risk averse agents. This paper uses two formats of a risk preference elicitation mechanism under two decision frames to test this hypothesis. After controlling for decision error, probability weighting, and order effects, subjects, on average, are slightly risk averse and prefer an increase in the expected value of a risk due to increasing the probability over a compensated increase in the reward. There is substantial across-format inconsistency but very little within-format inconsistency at the individual level.

Keywords

RiskUncertaintyExperiments

JEL classification

C91: Laboratory, Individual Behavior D81: Criteria for Decision-Making under Risk and Uncertainty
Type
Research Article
Information
Experimental Economics , Volume 12 , Issue 4 , December 2009 , pp. 367 - 385
Copyright
Copyright © Economic Science Association 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This research was undertaken at the University of Calgary Behavioural and Experimental Economics Laboratory (CBEEL). I would like to thank Christopher Auld, John Boyce, Glenn Harrison, Michael McKee, Bill Neilson, Rob Oxoby, Christian Vossler, Nat Wilcox, and two anonymous referees for their many helpful comments and suggestions. I would also like to thank participants at the 2007 North American Economic Science Association Meetings where an earlier version of this paper was presented.

Electronic supplementary material The online version of this article (http://dx.doi.org/10.1007/s10683-009-9219-7) contains supplementary material, which is available to authorized users.

References

Andersen, S. G., Harrison, G., Lau, M. I., & Rutström, E. E. (2006). Elicitation using multiple price list formats. Experimental Economics, 9(4), 383–406.CrossRefGoogle Scholar
Andersen, S. G., Harrison, G., Lau, M. I., & Rutström, E. E. (2008). Eliciting risk and time preferences. Econometrica, 76, 583–618.CrossRefGoogle Scholar
Anderson, L. R., & Stafford, S. L. (2003). Punishment in a regulatory setting: Experimental evidence from the VCM. Journal of Regulatory Economics, 24, 91–110.CrossRefGoogle Scholar
Anderson, L. R., & Stafford, S. L. (2006). Does crime pay? A classroom demonstration of monitoring and enforcement. Southern Economic Journal, 72, 1016–1025.Google Scholar
Ballinger, T. P., & Wilcox, N. T. (1997). Decisions, error and heterogeneity. The Economic Journal, 107, 1090–1105.CrossRefGoogle Scholar
Becker, G. S. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76, 169–217.CrossRefGoogle Scholar
Becker, G., DeGroot, M., & Marschak, J. (1963). Stochastic models of choice behavior. Behavioral Science, 8, 41–55.CrossRefGoogle Scholar
Block, M. K., & Gerety, V. E. (1995). Some experimental evidence on difference between student and prisoner reactions to monetary penalties and risk. Journal of Legal Studies, 24, 123–138.CrossRefGoogle Scholar
Camerer, C. F., & Ho, T.-H. (1994). Violations of the betweeness axiom and nonlinearity in probabilities. Journal ofRisk and Uncertainty, 8, 167–196.CrossRefGoogle Scholar
Carbone, E. (1998). Investigation of stochastic preference theory using experimental data. Economics Letters, 57,305-312.Google Scholar
Carbone, E., & Hey, J. D. (1994). Discriminating between preference functionals - A preliminary Monte Carlo study. Journal of Risk and Uncertainty, 8, 223–242.CrossRefGoogle Scholar
Carbone, E., & Hey, J. D. (2000). Which error story is best. Journal of Risk and Uncertainty, 20, 161–176.CrossRefGoogle Scholar
Dave, C., Eckel, C., Johnson, C., & Rojas, C. (2007). On the heterogeneity, stability, and validity of risk preferences. Unpublished Manuscript, University of Texas at Dallas.Google Scholar
Debreu, G. (1958). Stochastic choice and cardinal utility. Econometrica, 26, 440–444.CrossRefGoogle Scholar
Fechner, G. (1860/1966). Elements of psychophysics. New York: Holt, Rinehart and Winston.Google Scholar
Fischbacher, U. (2007). Z-tree - Zurich toolbox for readymade economic experiments - Experimenter's manual. Experimental Economics, 171–178.CrossRefGoogle Scholar
Friedman, D., & Sunder, S. (1994). Experimental methods: A primer for economists. New York: Cambridge University Press.CrossRefGoogle Scholar
Goeree, J., Holt, C. A., & Palfrey, T. R. (2003). Risk averse behavior in generalized matching pennies games. Games and Economic Behavior, 45, 97–113.CrossRefGoogle Scholar
Gonzalez, R., & Wu, G. (1999). On the shape of the probability weighting function. Cognitive Psychology, 38, 129–166.CrossRefGoogle ScholarPubMed
Greiner, B. (2004). The online recruitment system ORSEE 2.0 - A guide for the organization of experiments in economics. Working Paper Series in Economics 10, University of Cologne.Google Scholar
Grogger, J. (1991). Certainty vs. severity of punishment. Economic inquiry, 29, 297–309.CrossRefGoogle Scholar
Harless, D. W., & Camerer, C. F. (1994). The predictive utility of generalized expected utility theories. Econometrica, 62(6), 1251–1289.CrossRefGoogle Scholar
Harrison, G. W. (2007). Maximum likelihood estimation of utility functions using Stata. Working Paper 06-12, Department of Economics, College of Business Administration, University of Central Florida.Google Scholar
Harrison, G. W., & Rutström, E. E. (2008). Research in experimental economics (Vol. 12). Emerald Group Publishing Limited.Google Scholar
Harrison, G. W., Johnson, E., McInnes, M. M., & Rutström, E. E. (2005). Risk aversion and incentive effects: Comment. American Economic Review, 95(3), 897–901.CrossRefGoogle Scholar
Hey, J. D. (1995). Experimental investigations of errors in decision-making under uncertainty. European Economic Review, 29, 633–640.CrossRefGoogle Scholar
Hey, J. D. (2001). Does repetition improve consistency? Experimental Economics, 4, 5–54.CrossRefGoogle Scholar
Hey, J. D. (2005). Why we should not be silent about noise. Experimental Economics, 8, 325–345.CrossRefGoogle Scholar
Hey, J. D., & Orme, C. (1994). Investigating generalizations of expected utility theory using experimental data. Econometrica, 62(6), 1291–1326.CrossRefGoogle Scholar
Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, 92(5), 1644–1657.CrossRefGoogle Scholar
Holt, C. A., & Laury, S. K. (2005). Risk aversion and incentive effects new data without order effects. American Economic Review, 95(3), 902–904.CrossRefGoogle Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory an analysis of decision under risk. Econometrica, 47, 263–291.CrossRefGoogle Scholar
Knight, F. H. (1921). Risk, uncertainty and profit. Boston: Houghton Mifflin.Google Scholar
LeRoy, S.F., & Singell, L. D. (1987). Knight on risk and uncertainty. Journal of Political Economy, 95(2), 394–406.CrossRefGoogle Scholar
Loomes, G., & Sugden, R. (1995). Incorporating a stochastic element into decision theories. European Economic Review, 39, 641–648.CrossRefGoogle Scholar
Loomes, G., & Sugden, R. (1998). Testing alternative stochastic specifications for risky choice. Economica, 65, 581–598.CrossRefGoogle Scholar
Loomes, G., Moffatt, P. G., & Sugden, R. (2002). A microeconometric test of alternative stochastic theories of risky choice. Journal of Risk and Uncertainty, 24, 103–130.CrossRefGoogle Scholar
Luce, D. (1959). Individual choice behavior. New York: Wiley.Google Scholar
Myers, S. B. (1983). Estimating the economic model of crime: Employment versus punishment effects. Quarterly Journal of Economics, February, 157–166.CrossRefGoogle Scholar
Polinsky, A. M., & Shavell, S. (2000). The economic theory of public enforcement of law. Journal of Economic Literature, 38, 45–76.CrossRefGoogle Scholar
Prelec, D. (1998). The probability weighting function. Econometrica, 66, 497–528.CrossRefGoogle Scholar
Rothschild, M., & Stiglitz, J. (1970). Increasing risk: I. A definition. Journal of Economic Theory, 2, 225–243.CrossRefGoogle Scholar
Samuelson, P. A. (1952). Probability, utility, and the independence axiom. Econometrica, 20(4), 670–678.CrossRefGoogle Scholar
Smith, V. L., & Walker, J. (1993). Monetary rewards and decision cost in experimental economics. Economic Inquiry, 31, 245–261.CrossRefGoogle Scholar
Starmer, C., & Sugden, R. (1991). Does the random-lottery incentive system elicit true preferences? An experimental investigation. American Economic Review, 81(4), 971–978.Google Scholar
Stott, H. P. (2006). Cumulative prospect theory's functional menagerie. Journal of Risk and Uncertainty, 32, 101–130.CrossRefGoogle Scholar
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323.CrossRefGoogle Scholar
Wilcox, N. T. (1993). Lottery choice: Incentives, complexity, and decision time. The Economic Journal, 103, 1397–1470.CrossRefGoogle Scholar
Wilcox, N. T. (2007). In Risk Aversion in Experiments. Research in Experimental Economics: Vol. 12. Greenwich: JAI.Google Scholar
Wilcox, N. T. (Forthcoming). Stochastically more risk averse: A contextual utility approach. Journal of Econometrics.Google Scholar
Witte, A. D. (1980). Estimating the economic model of crime with individual data. Quarterly Journal of Economics, February, 57–84.CrossRefGoogle Scholar
Supplementary material: File

Bruner supplementary material

Bruner supplementary material 1
Download Bruner supplementary material(File)
File 561.7 KB
Supplementary material: File

Bruner supplementary material

Bruner supplementary material 2
Download Bruner supplementary material(File)
File 77.7 KB
Supplementary material: File

Bruner supplementary material

Bruner supplementary material 3
Download Bruner supplementary material(File)
File 63.7 KB