Abstract
Sensitivity analysis is a critical component of Multi-Criterion Decision analysis (MCDA), enabling the evaluation of how variations in input data influence decision outcomes. Although traditional techniques, such as scenario analysis and stability intervals, can enhance robustness, they often fail to capture the full variability inherent in the decision model. To overcome this limitation, we propose a novel framework that applies Sobol sensitivity indices to assess the global importance of criteria in MCDA, with a particular focus on models constructed using the Characteristic Objects Method (COMET). Unlike standard approaches that perturb the decision matrix, our method evaluates the sensitivity of the decision model as a whole. We demonstrate its effectiveness through controlled experiments, including hybrid configurations such as Expected Solution Point (ESP) with COMET, and use first-order (S1) and total effect (ST) Sobol indices. The results show that the variance-based sensitivity analysis provides valuable, complementary insights into the global behavior of the model and the influence of individual criteria. This study introduces a robust analytical tool that enhances the interpretability and methodological depth of decision support systems in MCDA
Paper Type
Short Paper
DOI
10.62036/ISD.2025.83
Variance-Based Analysis of Global Criteria Importance in the ESP-COMET Method
Sensitivity analysis is a critical component of Multi-Criterion Decision analysis (MCDA), enabling the evaluation of how variations in input data influence decision outcomes. Although traditional techniques, such as scenario analysis and stability intervals, can enhance robustness, they often fail to capture the full variability inherent in the decision model. To overcome this limitation, we propose a novel framework that applies Sobol sensitivity indices to assess the global importance of criteria in MCDA, with a particular focus on models constructed using the Characteristic Objects Method (COMET). Unlike standard approaches that perturb the decision matrix, our method evaluates the sensitivity of the decision model as a whole. We demonstrate its effectiveness through controlled experiments, including hybrid configurations such as Expected Solution Point (ESP) with COMET, and use first-order (S1) and total effect (ST) Sobol indices. The results show that the variance-based sensitivity analysis provides valuable, complementary insights into the global behavior of the model and the influence of individual criteria. This study introduces a robust analytical tool that enhances the interpretability and methodological depth of decision support systems in MCDA
Recommended Citation
Sałabun, W., Shekhovtsov, A. & Wątróbski, J. (2025). Variance-Based Analysis of Global Criteria Importance in the ESP-COMET MethodIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.83