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Current Human Reliability Analysis models express error probabilities as a function of task types and operational context, without explicitly modelling the influence of different crew behavioral characteristics on the error probability. The influence of such variability is treated only implicitly, by variability and uncertainty distributions with bounds primarily obtained by expert judgment. This paper presents a methodology to empirically incorporate crew performance variability in error probability quantification, from simulator data. Crew behaviors are represented by a set of “behavioral patterns” that emerge in the observation of operating crews (e.g. in information sharing or in adhering to procedural guidance). The paper demonstrates the use of a Bayesian hierarchical model to explicitly capture the performance variability emerging from data. The methodology is applied to a case study from literature. Numerical demonstrations are performed in order to compare the proposed approach to the existing quantification models used in HRA for treating simulator data.

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