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arXiv · 2509.24996

Addressing Methodological Sensitivity in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis

Juan B. Cabral·Alvaro Roy Schachner
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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.

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Juan B. Cabral, Alvaro Roy Schachner. 2025-09-29. Addressing Methodological Sensitivity in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis. https://arxiv.org/abs/2509.24996

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