IDEAS home Printed from https://ideas.repec.org/a/kap/theord/v60y2006i2p315-334.html
   My bibliography  Save this article

Error Propagation in the Elicitation of Utility and Probability Weighting Functions

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11238-005-4593-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11238-005-4593-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fennema, Hein & van Assen, Marcel, 1998. "Measuring the Utility of Losses by Means of the Tradeoff Method," Journal of Risk and Uncertainty, Springer, vol. 17(3), pages 277-295, December.
    2. Mark McCord & Richard de Neufville, 1986. ""Lottery Equivalents": Reduction of the Certainty Effect Problem in Utility Assessment," Management Science, INFORMS, vol. 32(1), pages 56-60, January.
    3. Smith, Vernon L & Walker, James M, 1993. "Monetary Rewards and Decision Cost in Experimental Economics," Economic Inquiry, Western Economic Association International, vol. 31(2), pages 245-261, April.
    4. Nathalie Etchart-Vincent, 2004. "Is Probability Weighting Sensitive to the Magnitude of Consequences? An Experimental Investigation on Losses," Journal of Risk and Uncertainty, Springer, vol. 28(3), pages 217-235, May.
    5. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    6. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    7. Wu, George, 1994. "An Empirical Test of Ordinal Independence," Journal of Risk and Uncertainty, Springer, vol. 9(1), pages 39-60, July.
    8. Han Bleichrodt & Jose Luis Pinto, 2000. "A Parameter-Free Elicitation of the Probability Weighting Function in Medical Decision Analysis," Management Science, INFORMS, vol. 46(11), pages 1485-1496, November.
    9. Peter Wakker & Daniel Deneffe, 1996. "Eliciting von Neumann-Morgenstern Utilities When Probabilities Are Distorted or Unknown," Management Science, INFORMS, vol. 42(8), pages 1131-1150, August.
    10. Paolo Ghirardato & Fabio Maccheroni & Massimo Marinacci & Marciano Siniscalchi, 2003. "A Subjective Spin on Roulette Wheels," Econometrica, Econometric Society, vol. 71(6), pages 1897-1908, November.
    11. Starmer, Chris & Sugden, Robert, 1989. "Probability and Juxtaposition Effects: An Experimental Investigation of the Common Ratio Effect," Journal of Risk and Uncertainty, Springer, vol. 2(2), pages 159-178, June.
    12. Peter H. Farquhar, 1984. "State of the Art---Utility Assessment Methods," Management Science, INFORMS, vol. 30(11), pages 1283-1300, November.
    13. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December.
    14. Harless, David W & Camerer, Colin F, 1994. "The Predictive Utility of Generalized Expected Utility Theories," Econometrica, Econometric Society, vol. 62(6), pages 1251-1289, November.
    15. Camerer, Colin F, 1989. "An Experimental Test of Several Generalized Utility Theories," Journal of Risk and Uncertainty, Springer, vol. 2(1), pages 61-104, April.
    16. John C. Hershey & Paul J. H. Schoemaker, 1985. "Probability Versus Certainty Equivalence Methods in Utility Measurement: Are they Equivalent?," Management Science, INFORMS, vol. 31(10), pages 1213-1231, October.
    17. Mohammed Abdellaoui, 2000. "Parameter-Free Elicitation of Utility and Probability Weighting Functions," Management Science, INFORMS, vol. 46(11), pages 1497-1512, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Blavatskyy, Pavlo R., 2012. "Utility of a quarter-million," Economics Letters, Elsevier, vol. 117(3), pages 650-653.
    2. 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.
    3. Kirsten Rohde, 2010. "The hyperbolic factor: A measure of time inconsistency," Journal of Risk and Uncertainty, Springer, vol. 41(2), pages 125-140, October.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2019. "Taming models of prospect theory in the wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Leibniz Institute for Financial Research SAFE, revised 2019.
    2. 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.
    3. Mohammed Abdellaoui, 2000. "Parameter-Free Elicitation of Utility and Probability Weighting Functions," Management Science, INFORMS, vol. 46(11), pages 1497-1512, November.
    4. Peter Brooks & Horst Zank, 2005. "Loss Averse Behavior," Journal of Risk and Uncertainty, Springer, vol. 31(3), pages 301-325, December.
    5. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    6. Abdellaoui, Mohammed & Bleichrodt, Han, 2007. "Eliciting Gul's theory of disappointment aversion by the tradeoff method," Journal of Economic Psychology, Elsevier, vol. 28(6), pages 631-645, December.
    7. Peter Brooks & Simon Peters & Horst Zank, 2014. "Risk behavior for gain, loss, and mixed prospects," Theory and Decision, Springer, vol. 77(2), pages 153-182, August.
    8. Kpegli, Yao Thibaut & Corgnet, Brice & Zylbersztejn, Adam, 2023. "All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components," Journal of Mathematical Economics, Elsevier, vol. 104(C).
    9. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    10. George Wu & Alex B. Markle, 2008. "An Empirical Test of Gain-Loss Separability in Prospect Theory," Management Science, INFORMS, vol. 54(7), pages 1322-1335, July.
    11. Michael H. Birnbaum & Ulrich Schmidt & Miriam D. Schneider, 2017. "Testing independence conditions in the presence of errors and splitting effects," Journal of Risk and Uncertainty, Springer, vol. 54(1), pages 61-85, February.
    12. Glenn W. Harrison & J. Todd Swarthout, 2016. "Cumulative Prospect Theory in the Laboratory: A Reconsideration," Experimental Economics Center Working Paper Series 2016-04, Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University.
    13. Mohammed Abdellaoui & Ahmed Driouchi & Olivier L’Haridon, 2011. "Risk aversion elicitation: reconciling tractability and bias minimization," Theory and Decision, Springer, vol. 71(1), pages 63-80, July.
    14. Han Bleichrodt & Jose Luis Pinto & Peter P. Wakker, 2001. "Making Descriptive Use of Prospect Theory to Improve the Prescriptive Use of Expected Utility," Management Science, INFORMS, vol. 47(11), pages 1498-1514, November.
    15. 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.
    16. Pavlo R. Blavatskyy, "undated". "A Stochastic Expected Utility Theory," IEW - Working Papers 231, Institute for Empirical Research in Economics - University of Zurich.
    17. 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.
    18. Diecidue, Enrico & Schmidt, Ulrich & Zank, Horst, 2009. "Parametric weighting functions," Journal of Economic Theory, Elsevier, vol. 144(3), pages 1102-1118, May.
    19. 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.
    20. Aurélien Baillon & Han Bleichrodt & Alessandra Cillo, 2015. "A Tailor-Made Test of Intransitive Choice," Operations Research, INFORMS, vol. 63(1), pages 198-211, February.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:theord:v:60:y:2006:i:2:p:315-334. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.