Accounting for expert-to-expert variability: a potential source of bias in performance assessments of high-level radioactive waste repositories

Expert judgments enter several aspects of many scientific endeavors. They are typically employed to interpret data, predict systemsʹ behaviour and assess uncertainties. In particular, expert judgments are expected to be a relevant source of data for use in performance assessments of high-level radioactive waste repositories. In this paper we consider the task of aggregating the judgments provided by experts with the objective of emphasizing a potential source of bias that might affect the results of the analysis. Typically, the analysts combine mathematical and behavioral schemes to obtain the aggregate measures desired for decision-making purposes. Within this approach to data aggregation, mathematical models are used as tools for sensitivity analysis and they should account for between-expert (expert-to-expert) as well as within-expert variability. A practical example regarding a formal expert judgment elicitation exercise for the future climate at Yucca Mountain vicinity is presented