While sensitivity analysis improves the transparency and reliability of mathematical models, its uptake by modelers is still scarce. This is partially explained by its technical requirements, which may be hard to understand and implement by the non-specialist. Here we propose a sensitivity analysis approach based on the concept of discrepancy that is as easy to understand as the visual inspection of input-output scatterplots. Firstly, we show that some discrepancy measures are able to rank the most influential parameters of a model almost as accurately as the variance-based total sensitivity index. We then introduce an ersatz-discrepancy whose performance as a sensitivity measure matches that of the best-performing discrepancy algorithms, is simple to implement, easier to interpret and orders of magnitude faster.

翻译：尽管敏感性分析提高了数学模型的透明度和可靠性，但其在建模者中的普及程度仍然有限。这部分是由于其技术要求较高，非专业人员可能难以理解和实施。本文提出了一种基于差异性概念的敏感性分析方法，该方法与输入-输出散点图的可视化检查一样易于理解。首先，我们证明某些差异性度量能够几乎与基于方差的总敏感性指数一样准确地排序模型中最具影响力的参数。随后，我们介绍了一种替代差异性度量，其作为敏感性指标的性能可与最优异的差异性算法相媲美，实现简单、解释更直观，且速度快数个数量级。