How to treat expert judgment? With certainty it contains uncertainty!. (July 2020)

• Record Type: Journal Article
• Authors: Pasman, Hans J.; Rogers, William J.
• Is Part Of: Journal of loss prevention in the process industries. Volume 66(2020)
• Publication Date: 2020-07
• Subjects: Probability -- Imprecision -- Expert elicitation -- Dempster-shafer theory -- Fuzzy sets and logic; Chemical industries -- Safety measures -- Periodicals; 660.2804
• DOI: 10.1016/j.jlp.2020.104200
• Languages: English
• ISSNs: 0950-4230

Abstract

Abstract: To be acceptably safe one must identify the risks one is exposed to and decide what risk reducing measures are required. It is uncertain whether the threat really will materialize, but determining the size and probability of the risk is also full of uncertainty. When performing an analysis and preparing for decision making under uncertainty, quite frequently failure rate data, information on consequence severity or on a probability value, yes, even on the possibility that an event can or cannot occur, is lacking. In those cases, a possible way and sometimes the only way to proceed is to revert to expert judgment. Even in case historical data is available, an expert can be asked whether and to what extent such data still hold in the current situation. Anyhow, expert elicitation comes with an uncertainty depending on the expert's reliability, which becomes very visible when two or more experts give different answers or even conflicting answers. This is not a new problem, and very bright minds have thought how to tackle this in a rational and objective way. But so far, however, the topic has not been given much attention in daily process safety and risk assessment practice. Therefore, this paper has a review and applied character and will present various approaches with detailed explanation and examples. Highlights: In quantitative risk assessments reliable event/failure probability data are often lacking. Expert judgement may be of help, even to supplement or evaluate historical data. Expert opinions may differ or conflict, requiring rational/objective aggregation methods. Multiple methods to cope with this have been developed but are rarely used in QRA. This paper explains foundations of methods mostly used and provides simple examples.