IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v34y1986i2p289-295.html
   My bibliography  Save this article

The Reconciliation of Decision Analyses

Author

Listed:
  • Dennis V. Lindley

    (Decision Science Consortium, Falls Church, Virginia)

Abstract

A decision problem can be structured in many different ways. This paper addresses a few of the problems that arise as a result of this diversity; for instance, is a particular structuring method worth doing: is the effort likely to be rewarding? Further considerations arise when two or more methods are used and must be combined. Any rule of combination must attach weights, that depend on errors in the analyses, to the results. Consequently, the questions of errors both at random and decision nodes require discussion. We show that errors at decision nodes produce biases. A basic idea underlying our argument is the concept of true utilities and probabilities: we include a discussion of this concept.

Suggested Citation

  • Dennis V. Lindley, 1986. "The Reconciliation of Decision Analyses," Operations Research, INFORMS, vol. 34(2), pages 289-295, April.
  • Handle: RePEc:inm:oropre:v:34:y:1986:i:2:p:289-295
    DOI: 10.1287/opre.34.2.289
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.34.2.289
    Download Restriction: no

    File URL: https://libkey.io/10.1287/opre.34.2.289?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. Anil Gaba & Dana G. Popescu & Zhi Chen, 2019. "Assessing Uncertainty from Point Forecasts," Management Science, INFORMS, vol. 65(1), pages 90-106, January.
    2. Gary J. Summers, 2021. "Friction and Decision Rules in Portfolio Decision Analysis," Decision Analysis, INFORMS, vol. 18(2), pages 101-120, June.
    3. Robert L. Winkler & Robert T. Clemen, 2004. "Multiple Experts vs. Multiple Methods: Combining Correlation Assessments," Decision Analysis, INFORMS, vol. 1(3), pages 167-176, September.
    4. James E. Smith & Robert L. Winkler, 2006. "The Optimizer's Curse: Skepticism and Postdecision Surprise in Decision Analysis," Management Science, INFORMS, vol. 52(3), pages 311-322, March.
    5. Sarah Ben Amor & Kazimierz Zaras & Ernesto A. Aguayo, 2017. "The value of additional information in multicriteria decision making choice problems with information imperfections," Annals of Operations Research, Springer, vol. 253(1), pages 61-76, June.

    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:oropre:v:34:y:1986:i:2:p:289-295. 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.