A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction

In human reliability analysis (HRA), models are often used for the prediction of human error probabilities (HEPs), given a set of performance conditions, typically represented by ratings on a set of influencing factors. The relationships underlying these models (yielding HEPs for specific sets of factor ratings) should ideally be built on empirical data. However the derivation of these relationships in practice has to cope with limited availability of data, so that a strong component of expert judgment is always present. Nevertheless, the incorporation of expert judgment in HRA models is typically not done in a formal way, so that that it is often impossible to distinguish source data and judgments. In this context, this paper presents a Bayesian approach to aggregate expert estimates on human error probabilities to determine the relationships of an HRA model. The idea is to build a computable model using information from experts, provided as estimates. A numerical example demonstrates that the approach formally and transparently represents (and distinguishes) the inherent variability of the HEP quantity as well as that of the experts providing their estimates.