Sequential Refined Partitioning for Probabilistic Dependence Assessment

AbstractModeling 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. 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 (under specification). 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 thr ough 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 ...

Source: Risk Analysis - December 7, 2018 Category: International Medicine & Public Health Authors: Christoph Werner, Tim Bedford, John Quigley Tags: Original Research Article Source Type: research