Eliciting Multivariate Uncertainty from Experts

Abstract

In decision and risk analysis problems, modelling uncertainty probabilistically provides key insights and information for decision makers. A common challenge is that uncertainties are typically not isolated but interlinked which introduces complex (and often unexpected) effects on the model output. Therefore, dependence needs to be taken into account and modelled appropriately if simplifying assumptions, such as independence, are not sensible. Similar to the case of univariate uncertainty, which is described elsewhere in this book, relevant historical data to quantify a (dependence) model are often lacking or too costly to obtain. This may be true even when data on a model’s univariate quantities, such as marginal probabilities, are available. Then, specifying dependence between the uncertain variables through expert judgement is the only sensible option. A structured and formal process to the elicitation is essential for ensuring methodological robustness. This chapter addresses the main elements of structured expert judgement processes for dependence elicitation. We introduce the processes’ common elements, typically used for eliciting univariate quantities, and present the differences that need to be considered at each of the process’ steps for multivariate uncertainty. Further, we review findings from the behavioural judgement and decision making literature on potential cognitive fallacies that can occur when assessing dependence as mitigating biases is a main objective of formal expert judgement processes. Given a practical focus, we reflect on case studies in addition to theoretical findings. Thus, this chapter serves as guidance for facilitators and analysts using expert judgement.