Reliability-oriented sensitivity analysis methods have been developed for understanding the influence of model inputs relative to events which characterize the failure of a system (e.g., a threshold exceedance of the model output). In this field, the target sensitivity analysis focuses primarily on capturing the influence of the inputs on the occurrence of such a critical event. This paper proposes new target sensitivity indices, based on the Shapley values and called "target Shapley effects", allowing for interpretable sensitivity measures under dependent inputs. Two algorithms (one based on Monte Carlo sampling, and a given-data algorithm based on a nearest-neighbors procedure) are proposed for the estimation of these target Shapley effects based on the $\ell^2$ norm. Additionally, the behavior of these target Shapley effects are theoretically and empirically studied through various toy-cases. Finally, the application of these new indices in two real-world use-cases (a river flood model and a COVID-19 epidemiological model) is discussed.
翻译:为了解模型投入对系统失灵特征事件的影响(例如,模型输出的临界值超过临界值),制定了注重可靠性的敏感度分析方法,以了解模型投入对系统失灵特征事件的影响(例如,模型输出的临界值超过临界值),在这方面,目标敏感度分析主要侧重于捕捉投入对发生这种重大事件的影响,本文件根据“毛绒”值提出了新的目标敏感度指数,称为“目标形状效应”,允许在依赖投入下采取可解释的敏感度措施。提出了两种算法(一种基于蒙特卡洛取样,一种基于近邻程序的特定数据算法),用于根据“美元”的规范估算这些目标“形状效应”效应。此外,这些目标“形状效应”在理论上和实验性地研究过各种小案例。最后,讨论了这些新指数在两个实际使用案例(河流洪水模型和COVID-19流行病学模型)中的应用情况。