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

Christoph Werner, Tim Bedford, John Quigley

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
22 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)889-907
Number of pages19
JournalJournal of Operational Research Society
Issue number4
Early online date6 Feb 2020
Publication statusPublished - 1 Jan 2021


  • uncertainty modelling
  • stochastic systems
  • dependence modelling
  • structured expert judgement
  • cognitive mapping


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