IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v42y1996i7p1033-1042.html
   My bibliography  Save this article

The Magnitude of Errors in Proximal Multiattribute Decision Analysis with Probabilistically Dependent Attributes

Author

Listed:
  • James L. Corner

    (Department of Management Systems, University of Waikato, Hamilton, New Zealand)

  • Craig W. Kirkwood

    (Department of Management, Arizona State University, Tempe, Arizona 85287-4006)

Abstract

This paper investigates the accuracy of an approximation procedure for evaluating alternatives under uncertainty with multiple evaluation attributes. This approximation uses only the first two moments of the probability distributions for the alternatives, and hence it can substantially reduce the amount of information which must be collected in order to evaluate alternatives when evaluation attributes are probabilistically dependent. The accuracy of the approximation is investigated by comparing results from using it with exact calculations for a variety of situations representative of those found in decision analysis practice. This investigation shows that the approximation is accurate for situations representative of many decision analysis applications. However, caution is needed in applying the approximation in some situations where it may give inaccurate results. Characteristics of cases where the approximation is less accurate are presented.

Suggested Citation

  • James L. Corner & Craig W. Kirkwood, 1996. "The Magnitude of Errors in Proximal Multiattribute Decision Analysis with Probabilistically Dependent Attributes," Management Science, INFORMS, vol. 42(7), pages 1033-1042, July.
  • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:7:p:1033-1042
    DOI: 10.1287/mnsc.42.7.1033
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.42.7.1033
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.42.7.1033?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. Huifen Chen, 2001. "Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations," INFORMS Journal on Computing, INFORMS, vol. 13(4), pages 312-331, November.
    2. Jiehua Xie & Zhengyong Zhou, 2022. "Patchwork Constructions of Multiattribute Utility Functions," Decision Analysis, INFORMS, vol. 19(2), pages 141-169, 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:ormnsc:v:42:y:1996:i:7:p:1033-1042. 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.