IDEAS home Printed from https://ideas.repec.org/h/spr/isochp/978-3-030-46474-5_7.html
   My bibliography  Save this book chapter

Adversarial Risk Analysis as a Decomposition Method for Structured Expert Judgement Modelling

In: Expert Judgement in Risk and Decision Analysis

Author

Listed:
  • David Ríos Insua

    (Instituto de Ciencias Matemáticas (CSIC-UAM-UC3M-UCM))

  • David Banks

    (Department of Statistical Science (Duke University))

  • Jesús Ríos

    (IBM Research Division (IBM))

  • Jorge González-Ortega

    (Instituto de Ciencias Matemáticas (CSIC-UAM-UC3M-UCM))

Abstract

We argue that adversarial risk analysis may be incorporated into the structured expert judgement modelling toolkit for cases in which we need to forecast the actions of competitors based on expert knowledge. This is relevant in areas such as cybersecurity, security, defence and business competition. As a consequence, we present a structured approach to facilitate the elicitation of probabilities over the actions of other intelligent agents by decomposing them into multiple, but simpler, assessments later combined together using a rationality model of the adversary to produce a final probabilistic forecast. We then illustrate key concepts and modelling strategies of this approach to support its implementation.

Suggested Citation

  • David Ríos Insua & David Banks & Jesús Ríos & Jorge González-Ortega, 2021. "Adversarial Risk Analysis as a Decomposition Method for Structured Expert Judgement Modelling," International Series in Operations Research & Management Science, in: Anca M. Hanea & Gabriela F. Nane & Tim Bedford & Simon French (ed.), Expert Judgement in Risk and Decision Analysis, chapter 0, pages 179-196, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-46474-5_7
    DOI: 10.1007/978-3-030-46474-5_7
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:isochp:978-3-030-46474-5_7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.