{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/190404488","title":"A sensitivity analysis of the PAWN sensitivity index","arxiv_id":"1904.04488","date":"2019-04-09","proceeding":null,"authors":["Arnald Puy","Samuele Lo Piano","Andrea Saltelli"],"abstract":"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 ($\\theta$). 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 ($N,n,\\varepsilon, \\theta$) is difficult to tame, as these parameters interact with one another. Even in an ideal setting in which the optimum choice for ($N,n,\\varepsilon, \\theta$) is known in advance, PAWN might not allow to distinguish an influential, non-additive model input from a truly non-influential model input.","url_abs":"http://arxiv.org/abs/1904.04488v1","url_pdf":"http://arxiv.org/pdf/1904.04488v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"190404488","repo_url":"https://github.com/arnaldpuy/pawn_uncertainty","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}