Mapping conditional scenarios for knowledge structuring in (tail) dependence elicitation

Werner, Christoph and Bedford, Tim and Quigley, John (2019) Mapping conditional scenarios for knowledge structuring in (tail) dependence elicitation. Journal of Operational Research Society. ISSN 0160-5682 (In Press)

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    Abstract

    In decision and risk analysis, probabilistic modelling of uncertainties provides essential information for decision-makers. As uncertainties are typically not isolated and simplifying assumptions (such as independence) are often not justifiable, methods that model their dependence are being developed. A common challenge is that relevant historical data for specifying and quantifying a model are lacking. In this case, the dependence information should be elicited from experts. Guidance for eliciting dependence is sparse whereas particularly little research addresses the structuring of experts' knowledge about dependence relationships prior to a quantitative elicitation. However, such preparation is crucial for developing confidence in the resulting judgements, mitigating biases and ensuring transparency, especially when assessing tail dependence. Therefore, we introduce a qualitative risk analysis method based on our definition of conditional scenarios that structures experts' knowledge about (tail) dependence prior to its assessment. In an illustrative example, we show how to elicit conditional scenarios that support the assessment of a quantitative model for the complex risks of the UK higher education sector.

    Creators(s): Werner, Christoph ORCID logoORCID: https://orcid.org/0000-0002-5307-9686, Bedford, Tim ORCID logoORCID: https://orcid.org/0000-0002-3545-2088 and Quigley, John;
    Item type:Article
    ID code:70817
    Keywords:uncertainty modelling, stochastic systems, dependence modelling, structured expert judgement, cognitive mapping, Risk Management, Probabilities. Mathematical statistics, Management Information Systems, Strategy and Management, Management Science and Operations Research, Marketing
    Subjects:Social Sciences > Industries. Land use. Labor > Risk Management
    Science > Mathematics > Probabilities. Mathematical statistics
    Department:Strathclyde Business School > Management Science
    Depositing user: Pure Administrator
    Date deposited:11 Dec 2019 15:57
    Last modified:01 Jan 2021 13:24
    URI:https://strathprints.strath.ac.uk/id/eprint/70817
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