{"ID":2863752,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24996","arxiv_id":"2509.24996","title":"Addressing Methodological Sensitivity in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis","abstract":"Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addresses this methodological sensitivity through automated exploration of the scaling transformation space. The implementation leverages the existing Scikit-Criteria infrastructure to automatically generate all possible methodological combinations and provide robust comparative analysis.We apply this approach in an evaluation dataset of cryptocurrencies with 6 methodological scenarios, showing a range of correlation between methods, explicitly quantifying the methodological sensitivity limits.","short_abstract":"Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addre...","url_abs":"https://arxiv.org/abs/2509.24996","url_pdf":"https://arxiv.org/pdf/2509.24996v2","authors":"[\"Juan B. Cabral\",\"Alvaro Roy Schachner\"]","published":"2025-09-29T16:21:30Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.SE\"]","methods":"[\"LoRA\"]","has_code":false}