Addressing Methodological Sensitivity in MCDM with a Systematic Pipeline Approach to Data Transformation Sensitivity Analysis
For practitioners using MCDM, this provides a systematic way to evaluate robustness, addressing the ad-hoc selection problem.
The paper tackles the sensitivity of MCDM methods to normalization choices, which can alter 20-40% of rankings, and presents a framework for automated exploration of scaling transformations to quantify this sensitivity.
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.