Complex models are often used to understand interactions and drivers of human-induced and/or natural phenomena. It is worth identifying the input variables that drive the model output(s) in a given domain and/or govern specific model behaviors such as contextual indicators based on socio-environmental models. Using the theory of multivariate weighted distributions to characterize specific model behaviors, we propose new measures of association between inputs and such behaviors. Our measures rely on sensitivity functionals (SFs) and kernel methods, including variance-based sensitivity analysis. The proposed $\ell_1$-based kernel indices account for interactions among inputs, higher-order moments of SFs, and their upper bounds are somehow equivalent to the Morris-type screening measures, including dependent elementary effects. Empirical kernel-based indices are derived, including their statistical properties for the computational issues, and numerical results are provided.

翻译：复杂模型常用于理解人类诱发和/或自然现象中的交互作用及驱动因素。识别在特定领域中驱动模型输出和/或支配具体模型行为（如基于社会环境模型的背景指标）的输入变量具有重要意义。利用多元加权分布理论表征特定模型行为，我们提出了输入变量与这些行为之间关联的新度量方法。该度量基于敏感性泛函（SFs）和核方法（包括基于方差的敏感性分析）。所提出的基于ℓ1范数的核指标考虑了输入变量间的交互作用、SFs的高阶矩，且其上限在某种程度上等价于Morris型筛选度量（包括相依基本效应）。我们推导了经验性核指标，包括其针对计算问题的统计性质，并提供了数值结果。