Combining Probability Distributions From Experts in Risk Analysis

TLDR

Expert judgments are valuable in risk analysis where hard data are scarce, yet no single aggregation process is universally optimal, requiring consideration of both mathematical and behavioral aspects. The study aims to guide the design of expert‑distribution combination processes in probabilistic risk analysis by reviewing methods, highlighting conceptual and practical issues, and emphasizing the need to balance information gain with methodological trade‑offs. The authors treat expert judgments as probability distributions and review both mathematical aggregation methods and behavioral approaches for combining them in PRA. Combining expert distributions yields a single probability distribution that summarizes current expert opinion, providing risk analysts and decision‑makers with a consolidated view of uncertainty.

Abstract

This paper concerns the combination of experts' probability distributions in risk analysis, discussing a variety of combination methods and attempting to highlight the important conceptual and practical issues to be considered in designing a combination process in practice. The role of experts is important because their judgments can provide valuable information, particularly in view of the limited availability of “hard data” regarding many important uncertainties in risk analysis. Because uncertainties are represented in terms of probability distributions in probabilistic risk analysis (PRA), we consider expert information in terms of probability distributions. The motivation for the use of multiple experts is simply the desire to obtain as much information as possible. Combining experts' probability distributions summarizes the accumulated information for risk analysts and decision‐makers. Procedures for combining probability distributions are often compartmentalized as mathematical aggregation methods or behavioral approaches, and we discuss both categories. However, an overall aggregation process could involve both mathematical and behavioral aspects, and no single process is best in all circumstances. An understanding of the pros and cons of different methods and the key issues to consider is valuable in the design of acombination process for a specific PRA. The output, a ”combined probabilitydistribution,” can ideally be viewed as representing a summary of the current state of expert opinion regarding the uncertainty of interest.