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Error Propagation in the Elicitation of Utility and Probability Weighting Functions

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  • Pavlo Blavatskyy

Abstract

Elicitation methods in decision-making under risk allow us to infer the utilities of outcomes as well as the probability weights from the observed preferences of an individual. An optimally efficient elicitation method is proposed, which takes the inevitable distortion of preferences by random errors into account and minimizes the effect of such errors on the inferred utility and probability weighting functions. Under mild assumptions, the optimally efficient method for eliciting utilities and probability weights is the following three-stage procedure. First, a probability is elicited whose subjective weight is one half. Second, the utility function is elicited through the midpoint chaining certainty equivalent method using the probability elicited at the first stage. Finally, the probability weighting function is elicited through the probability equivalent method. Copyright Springer 2006

Suggested Citation

  • Pavlo Blavatskyy, 2006. "Error Propagation in the Elicitation of Utility and Probability Weighting Functions," Theory and Decision, Springer, vol. 60(2), pages 315-334, May.
    • Handle: RePEc:kap:theord:v:60:y:2006:i:2:p:315-334
    DOI: 10.1007/s11238-005-4593-x
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    Cited by:

    1. Adam Booij & Bernard Praag & Gijs Kuilen, 2010. "A parametric analysis of prospect theory’s functionals for the general population," Theory and Decision, Springer, vol. 68(1), pages 115-148, February.
    2. Pavlo Blavatskyy, 2021. "A simple non-parametric method for eliciting prospect theory's value function and measuring loss aversion under risk and ambiguity," Theory and Decision, Springer, vol. 91(3), pages 403-416, October.
    3. Pavlo R. Blavatskyy, 2016. "Risk preferences of Australian academics: where retirement funds are invested tells the story," Theory and Decision, Springer, vol. 80(3), pages 411-426, March.
    4. Stefan Zeisberger & Dennis Vrecko & Thomas Langer, 2012. "Measuring the time stability of Prospect Theory preferences," Theory and Decision, Springer, vol. 72(3), pages 359-386, March.
    5. Christopher Schwand & Rudolf Vetschera & Lea Wakolbinger, 2010. "The influence of probabilities on the response mode bias in utility elicitation," Theory and Decision, Springer, vol. 69(3), pages 395-416, September.
    6. Mohammed Abdellaoui & Han Bleichrodt & Corina Paraschiv, 2007. "Loss Aversion Under Prospect Theory: A Parameter-Free Measurement," Management Science, INFORMS, vol. 53(10), pages 1659-1674, October.
    7. Blavatskyy, Pavlo R., 2012. "Utility of a quarter-million," Economics Letters, Elsevier, vol. 117(3), pages 650-653.
    8. Gijs van de Kuilen & Peter P. Wakker, 2011. "The Midweight Method to Measure Attitudes Toward Risk and Ambiguity," Management Science, INFORMS, vol. 57(3), pages 582-598, March.
    9. Booij, Adam S. & van de Kuilen, Gijs, 2009. "A parameter-free analysis of the utility of money for the general population under prospect theory," Journal of Economic Psychology, Elsevier, vol. 30(4), pages 651-666, August.
    10. Kirsten Rohde, 2010. "The hyperbolic factor: A measure of time inconsistency," Journal of Risk and Uncertainty, Springer, vol. 41(2), pages 125-140, October.
    11. Erner, Carsten & Klos, Alexander & Langer, Thomas, 2013. "Can prospect theory be used to predict an investor’s willingness to pay?," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 1960-1973.

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    More about this item

    Keywords

    cumulative prospect theory; decision theory; elicitation; von Neumann–Morgenstern utility; probability weighting; rank-dependent expected utility; C91; D81;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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