A Bayesian approach to comparing human reliability analysis methods using human performance data. (March 2022) - Zhao, Yunfei
Abstract: Various methods for human reliability analysis have been developed, but a rigorous approach to quantitatively comparing these methods is still lacking. This research proposed a Bayesian approach with an attempt to address this problem. The Bayesian approach is based on ensemble modeling, which outputs the weighted average of the human error probability predictions by the human reliability analysis methods to be compared. Before incorporating any human performance data, the weights in the ensemble model represent the prior probabilities of or one's prior beliefs in the human reliability analysis methods. Using human performance data, the weights can be updated based on Bayes' rule to reflect one's updated beliefs in the human reliability analysis methods. The ensemble model with updated weights itself can be further used as a posterior predictive model for human reliability. The proposed approach is demonstrated using the human performance data collected from the international human reliability analysis empirical study. The results show that the posterior beliefs vary with the data set used in the analysis. Future research using a larger human performance data set is expected to reach more conclusive comparisons. Highlights: Bayesian approach to quantitative HRA method comparison. Ensemble modeling for HEP prediction. Uncertainty in HEP predicted by an HRA method. Analyst-to-analyst variability in HEP estimates. Demonstration with human performance data from international HRA study.