The PAWN index is gaining traction among the modelling community as a sensitivity measure. However, the robustness to its design parameters has not yet been scrutinized: the size ($N$) and sampling ($\varepsilon$) of the model output, the number of conditioning intervals ($n$) or the summary statistic ($θ$). Here we fill this gap by running a sensitivity analysis of a PAWN-based sensitivity analysis. We compare the results with the design uncertainties of the Sobol' total-order index ($S_{Ti}^*$). Unlike in $S_{Ti}^*$, the design uncertainties in PAWN create non-negligible chances of producing biased results when ranking or screening inputs. The dependence of PAWN upon ($N,n,\varepsilon, θ$) is difficult to tame, as these parameters interact with one another. Even in an ideal setting in which the optimum choice for ($N,n,\varepsilon, θ$) is known in advance, PAWN might not allow to distinguish an influential, non-additive model input from a truly non-influential model input.

The paper has been accepted for publication in Environmental Modeling and Software (use the journal version to cite the paper)