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A Measurement Error Approach for Modeling Consumer Risk Preference

Author

Listed:
  • Jehoshua Eliashberg

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • John R. Hauser

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

Von Neumann-Morgenstern (vN-M) utility theory is the dominant theoretical model of risk preference. Recently, market researchers have adapted vN-M theory to model consumer risk preference. But, most applications assess utility functions by asking just n questions to specify n parameters. However, any questioning format, especially under market research conditions, introduces measurement error. This paper explores the implications of measurement error on the estimation of the unknown parameters in vN-M utility functions and provides procedures to deal with measurement error. We assume that the functional form of the utility function, but not its parameters, can be determined a priori through qualitative questioning. We then model measurement error as if question format and other influences cause the consumer to choose the unknown "risk parameter" from a probability distribution and to make his decisions accordingly. We provide procedures to estimate the unknown parameters when the measurement error is either (a) Normal or (b) Exponential. Uncertainty in risk parameters induces uncertainty in utility and expected utility, and hence uncertainty in choice outcomes. Thus, we derive the induced probability distributions of the consumer's utility and the estimators for the implied probability that an alternative is chosen. Results are obtained for both the standard decision analysis "preference indifference" question format and for a "revealed preference" format in which the consumer is asked simply to choose between two risky alternatives. Since uniattribute functions illustrate the essential risk preference properties of vN-M functions, we emphasize uniattribute results. We also provide multiattribute estimation procedures. Numerical examples illustrate the analytical results.

Suggested Citation

  • Jehoshua Eliashberg & John R. Hauser, 1985. "A Measurement Error Approach for Modeling Consumer Risk Preference," Management Science, INFORMS, vol. 31(1), pages 1-25, January.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:1:p:1-25
    DOI: 10.1287/mnsc.31.1.1
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    Citations

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    Cited by:

    1. Gregory W. Fischer & Jianmin Jia & Mary Frances Luce, 2000. "Attribute Conflict and Preference Uncertainty: The RandMAU Model," Management Science, INFORMS, vol. 46(5), pages 669-684, May.
    2. Palmeira, Mauricio, 2020. "Advice in the presence of external cues: The impact of conflicting judgments on perceptions of expertise," Organizational Behavior and Human Decision Processes, Elsevier, vol. 156(C), pages 82-96.
    3. Peter H. Farquhar & Yutaka Nakamura, 1988. "Utility assessment procedures for polynomial‐exponential functions," Naval Research Logistics (NRL), John Wiley & Sons, vol. 35(6), pages 597-613, December.
    4. Nathaniel T. Wilcox, 2017. "Random expected utility and certainty equivalents: mimicry of probability weighting functions," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 3(2), pages 161-173, December.
    5. Felix Holzmeister & Matthias Stefan, 2019. "The risk elicitation puzzle revisited: Across-methods (in)consistency?," Working Papers 2019-19, Faculty of Economics and Statistics, Universität Innsbruck.
    6. Jose Apesteguia & Miguel Angel Ballester, 2014. "Discrete Choice Estimation of Risk Aversion," Working Papers 788, Barcelona School of Economics.
    7. Elie Ofek & Muhamet Yildiz & Ernan Haruvy, 2007. "The Impact of Prior Decisions on Subsequent Valuations in a Costly Contemplation Model," Management Science, INFORMS, vol. 53(8), pages 1217-1233, August.
    8. Thomas Nitschke & Franziska Völckner, 2006. "Präferenzmessung bei unsicheren Produkteigenschaften: Risikoberücksichtigung bei Ergebnissen aus Conjoint-Analysen," Schmalenbach Journal of Business Research, Springer, vol. 58(6), pages 743-770, September.
    9. Chew, Soo Hong & Miao, Bin & Shen, Qiang & Zhong, Songfa, 2022. "Multiple-switching behavior in choice-list elicitation of risk preference," Journal of Economic Theory, Elsevier, vol. 204(C).
    10. Philippe Delquié, 2003. "Optimal Conflict in Preference Assessment," Management Science, INFORMS, vol. 49(1), pages 102-115, January.
    11. Joost M.E. Pennings & Raymond M. Leuthold, 1999. "Commodity Futures Contract Viability: A Multidisciplinary Approach," Finance 9905002, University Library of Munich, Germany.
    12. Manrai, Ajay K., 1995. "Mathematical models of brand choice behavior," European Journal of Operational Research, Elsevier, vol. 82(1), pages 1-17, April.
    13. Holzmeister, Felix & Stefan, Matthias, 2019. "The Risk Elicitation Puzzle Revisited: Across-Methods (In)consistency?," OSF Preprints pj9u2, Center for Open Science.
    14. Qiu, Yueming & Colson, Gregory & Grebitus, Carola, 2014. "Risk preferences and purchase of energy-efficient technologies in the residential sector," Ecological Economics, Elsevier, vol. 107(C), pages 216-229.
    15. Sarin, Rakesh & Wieland, Alice, 2016. "Risk aversion for decisions under uncertainty: Are there gender differences?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 60(C), pages 1-8.
    16. Felix Holzmeister & Matthias Stefan, 2021. "The risk elicitation puzzle revisited: Across-methods (in)consistency?," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 593-616, June.

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