Advanced Search
MyIDEAS: Login to save this article or follow this journal

Preference Factoring for Stochastic Trees

Contents:

Author Info

  • Gordon Hazen

    ()
    (Department of Industrial Engineering and Management Sciences, Northwestern University, Technological Institute, Evanston, Illinois 60208-3119)

Registered author(s):

    Abstract

    Stochastic trees are extensions of decision trees that facilitate the modeling of temporal uncertainties. Their primary application has been to medical treatment decisions. It is often convenient to present stochastic trees in factored form, allowing loosely coupled pieces of the model to be formulated and presented separately. In this paper, we show how the notion of factoring can be extended as well to preference components of the stochastic model. We examine updateable-state utility, a flexible class of expected utility models that permit stochastic trees to be rolled back much in the manner of decision trees. We show that preference summaries for updateable-state utility can be factored out of the stochastic tree. In addition, we examine utility decompositions which can arise when factors in a stochastic tree are treated as attributes in a multiattribute utility function.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://dx.doi.org/10.1287/mnsc.46.3.389.12067
    Download Restriction: no

    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 46 (2000)
    Issue (Month): 3 (March)
    Pages: 389-403

    as in new window
    Handle: RePEc:inm:ormnsc:v:46:y:2000:i:3:p:389-403

    Contact details of provider:
    Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA
    Phone: +1-443-757-3500
    Fax: 443-757-3515
    Email:
    Web page: http://www.informs.org/
    More information through EDIRC

    Related research

    Keywords: expected utility; medical decision making; stochastic trees; multiattribute utility; time preference; quality-adjusted lifetime;

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

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

    Cited by:
    1. C. Armero & G. Garc�a-Donato & A. López-Qu�lez, 2010. "Bayesian methods in cost-effectiveness studies: objectivity, computation and other relevant aspects," Health Economics, John Wiley & Sons, Ltd., vol. 19(6), pages 629-643.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:46:y:2000:i:3:p:389-403. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.

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