TY - JOUR
T1 - Mapping conditional scenarios for knowledge structuring in (tail) dependence elicitation
AU - Werner, Christoph
AU - Bedford, Tim
AU - Quigley, John
N1 - © Operational Research Society 2020
Werner, C., Bedford, T., & Quigley, J. (2021). Mapping conditional scenarios for knowledge structuring in (tail) dependence elicitation. Journal of the Operational Research Society, 72(4), 889–907. https://doi.org/10.1080/01605682.2019.1700767
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - uncertainty modelling
KW - stochastic systems
KW - dependence modelling
KW - structured expert judgement
KW - cognitive mapping
U2 - 10.1080/01605682.2019.1700767
DO - 10.1080/01605682.2019.1700767
M3 - Article
SN - 0160-5682
VL - 72
SP - 889
EP - 907
JO - Journal of Operational Research Society
JF - Journal of Operational Research Society
IS - 4
ER -