Discrepancy Measures for Global Sensitivity Analysis

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