Researchers at University of Strathclyde Report Findings in Risk Management (Sequential Refined Partitioning for Probabilistic Dependence Assessment)

By a News Reporter-Staff News Editor at Insurance Weekly News -- A new study on Risk Management is now available. According to news reporting originating in Glasgow, United Kingdom, by VerticalNews journalists, research stated, "Modeling dependence probabilistically is crucial for many applications in risk assessment and decision making under uncertainty. Neglecting dependence between multivariate uncertainties can distort model output and prevent a proper understanding of the overall risk."

The news reporters obtained a quote from the research from the University of Strathclyde, "Whenever relevant data for quantifying and modeling dependence between uncertain variables are lacking, expert judgment might be sought to assess a joint distribution. Key challenges for the use of expert judgment for dependence modeling are over-and underspecification. An expert can provide assessments that are infeasible, i.e., not consistent with any probability distribution (overspecification), and on the other hand, without making very restrictive parametric assumptions an expert cannot fully define a probability distribution (underspecification). The sequential refined partitioning method addresses over-and underspecification while allowing for flexibility about which part of a joint distribution is assessed and its level of detail. Potential overspecification is avoided by ensuring low cognitive complexity for experts through eliciting single conditioning sets and by offering feasible assessment ranges. The feasible range of any (sequential) assessment can be derived by solving a linear programming problem. Underspecification is addressed by modeling the density of directly and indirectly assessed distribution parts as minimally informative given their constraints. Hence, our method allows for modeling the whole distribution feasibly and in accordance with experts' information. A nonparametric way of assessing and modeling dependence flexibly in such detail has not been presented in the expert judgment literature for probabilistic dependence models so far."

According to the news reporters, the research concluded: "We provide an example of assessing terrorism risk in insurance underwriting."

For more information on this research see: Sequential Refined Partitioning for Probabilistic Dependence Assessment. Risk Analysis, 2018;():. (Wiley-Blackwell - www.wiley.com/; Risk Analysis - onlinelibrary.wiley.com/journal/10.1111/(ISSN)1539-6924)

Our news correspondents report that additional information may be obtained by contacting C. Werner, Dept. of Management Science, University of Strathclyde, Glasgow, UK. Additional authors for this research include T. Bedford and J. Quigley.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1111/risa.13162. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Keywords for this news article include: Europe, Glasgow, United Kingdom, Risk Management.

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