A large group decision making approach for dependence assessment in human reliability analysis

Human reliability analysis (HRA) is a systematic technique to assess human contribution to system risk and has been widely used in diverse complex systems. Dependence assessment among human errors is an important activity in HRA, which depends heavily on domain experts' knowledge and experience. Normally, it is common for experts to give their judgments using linguistic labels and different types of uncertainties may exist in the dependence assessments. Additionally, the existing dependence assessment methods are limited to small-scale expert groups, which reduce the accuracy of dependence analysis with the increasing complexity of high risky systems. In this article, we develop a large group dependence assessment (LGDA) model based on interval 2-tuple linguistic variables and cluster analysis method to manage the dependence in HRA. Further, we propose an extended Muirhead mean operator to determine the dependence levels between consecutive operator actions. Finally, an empirical healthcare dependence analysis is taken as an example to illustrate the effectiveness and practicality of our proposed LGDA approach.