Modelling and assessment of dependent performance shaping factors through Analytic Network Process

Despite continuous progresses in research and applications, one of the major weaknesses of current HRA methods dwells in their limited capability of modelling the mutual influences between performance shaping factors (PSFs). Indeed at least two types of dependencies between PSFs can be defined: (i) dependency between the states of the PSFs; (ii) dependency between the influences (impacts) of the PSFs on the human performance. This paper introduces a method, based on Analytic Network Process (ANP), for the quantification of the latter, where the overall contribution of each PSF (weight) to the human error probability (HEP) is eventually returned. The core of the method is the modelling process, articulated into two steps: firstly, a qualitative network of dependencies between PSFs is identified, then, the importance of each PSF is quantitatively assessed using ANP. The model allows to distinguish two components of the PSF influence: direct influence that is the influence that the considered PSF is able to express by itself, notwithstanding the presence of other PSFs and indirect influence that is the incremental influence of the considered PSF through its influence on other PSFs. A case study in Air Traffic Control is presented where the proposed approach is integrated into the cognitive simulator PROCOS. The results demonstrated a significant modification of the influence of PSFs over the operator performance when dependencies are taken into account, underlining the importance of considering not only the possible correlation between the states of PSFs but also their mutual dependency in affecting human performance in complex systems.