IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v16y1996i6p785-791.html
   My bibliography  Save this article

Hybrid Processing of Stochastic and Subjective Uncertainty Data

Author

Listed:
  • J. Arlin Cooper
  • Scott Ferson
  • Lev Ginzburg

Abstract

Uncertainty analyses typically recognize separate stochastic and subjective sources of uncertainty, but do not systematically combine the two, although a large amount of data used in analyses is partly stochastic and partly subjective. We have developed methodology for mathematically combining stochastic and subjective sources of data uncertainty, based on new “hybrid number” approaches. The methodology can be utilized in conjunction with various traditional techniques, such as PRA (probabilistic risk assessment) and risk analysis decision support. Hybrid numbers have been previously examined as a potential method to represent combinations of stochastic and subjective information, but mathematical processing has been impeded by the requirements inherent in the structure of the numbers, e.g., there was no known way to multiply hybrids. In this paper, we will demonstrate methods for calculating with hybrid numbers that avoid the difficulties. By formulating a hybrid number as a probability distribution that is only fuzzily known, or alternatively as a random distribution of fuzzy numbers, methods are demonstrated for the full suite of arithmetic operations, permitting complex mathematical calculations. It will be shown how information about relative subjectivity (the ratio of subjective to stochastic knowledge about a particular datum) can be incorporated. Techniques are also developed for conveying uncertainty information visually, so that the stochastic and subjective components of the uncertainty, as well as the ratio of knowledge about the two, are readily apparent. The techniques demonstrated have the capability to process uncertainty information for independent, uncorrelated data, and for some types of dependent and correlated data. Example applications are suggested, illustrative problems are shown, and graphical results are given.

Suggested Citation

  • J. Arlin Cooper & Scott Ferson & Lev Ginzburg, 1996. "Hybrid Processing of Stochastic and Subjective Uncertainty Data," Risk Analysis, John Wiley & Sons, vol. 16(6), pages 785-791, December.
  • Handle: RePEc:wly:riskan:v:16:y:1996:i:6:p:785-791
    DOI: 10.1111/j.1539-6924.1996.tb00829.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1539-6924.1996.tb00829.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1539-6924.1996.tb00829.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
    ---><---

    Citations

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


    Cited by:

    1. Junrui Xu & James H. Lambert, 2015. "Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 624-641, April.
    2. Bogdan Rębiasz, 2015. "Hybrid correlated data in risk assessment," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 25(1), pages 81-101.
    3. Bogdan Rębiasz, 2016. "New method of selecting efficient project portfolios in the presence of hybrid uncertainty," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 26(4), pages 65-90.

    More about this item

    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:wly:riskan:v:16:y:1996:i:6:p:785-791. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    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.