IDEAS home Printed from https://ideas.repec.org/a/inm/ordeca/v12y2015i4p190-204.html
   My bibliography  Save this article

Equal Tails: A Simple Method to Elicit Utility Under Violations of Expected Utility

Author

Listed:
  • Manel Baucells

    (Darden School of Business, University of Virginia, Charlottesville, Virginia 22903)

  • Antonio Villasís

    (IDE Escuela de Dirección de Empresas, Quito, Ecuador)

Abstract

Classical methods to elicit utility are biased because most individuals do not treat probabilities linearly. We propose a simple modification of the classical methods that equates, for all prospects being compared, the range of outcomes. We argue that the modification should work in theory, and test the modification experimentally. Our first experiment confirms that the modified certainty equivalent method reduces the curvature of the S-shaped value function. The second experiment is a novel design that compares the trade-off method with the three classical methods in their original and modified forms. Our equal-tails modification of both the certainty equivalent and the lottery equivalent method produces results consistent with the trade-off method. The lottery equivalent modification is particularly useful to elicit utility points when outcomes are nonquantifiable.

Suggested Citation

  • Manel Baucells & Antonio Villasís, 2015. "Equal Tails: A Simple Method to Elicit Utility Under Violations of Expected Utility," Decision Analysis, INFORMS, vol. 12(4), pages 190-204, December.
  • Handle: RePEc:inm:ordeca:v:12:y:2015:i:4:p:190-204
    DOI: 10.1287/deca.2015.0320
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/deca.2015.0320
    Download Restriction: no

    File URL: https://libkey.io/10.1287/deca.2015.0320?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
    ---><---

    Citations

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


    Cited by:

    1. Zhihua Li & Julia Müller & Peter P. Wakker & Tong V. Wang, 2018. "The Rich Domain of Ambiguity Explored," Management Science, INFORMS, vol. 64(7), pages 3227-3240, July.
    2. Cathleen Johnson & Aurélien Baillon & Han Bleichrodt & Zhihua Li & Dennie Dolder & Peter P. Wakker, 2021. "Prince: An improved method for measuring incentivized preferences," Journal of Risk and Uncertainty, Springer, vol. 62(1), pages 1-28, February.
    3. Stefan Trautmann & Peter P. Wakker, 2018. "Making the Anscombe-Aumann approach to ambiguity suitable for descriptive applications," Journal of Risk and Uncertainty, Springer, vol. 56(1), pages 83-116, February.

    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:inm:ordeca:v:12:y:2015:i:4:p:190-204. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.