Model for Definition of Multi-Criteria Compensation by the ICCI (Inter-Criteria Compensation Index) in the Ranking of Electric Vehicles

Defining compensatory interactions among criteria is a critical yet often subjective step in multi-criteria decision analysis (MCDA). To address this gap, this study proposes a novel model centered around the Inter-Criteria Compensation Index (ICCI), which is a quantitative measure derived from the standard error between normalized criterion values. The ICCI, complemented by correlation analysis and statistical significance testing, provides a structured framework to objectively identify compensatory, non-compensatory, or partially compensatory criteria pairs. The model also includes a method for adjusting criterion weights based on the ICCI and a sensitivity analysis to detect redundancies. We demonstrate the applicability of this framework through a case study ranking the 17 best-selling small electric vehicles in Brazil based on eight technical and economic criteria. The analysis revealed that six of the eight criteria exhibited strong compensatory relationships, while two were identified as non-compensatory. The subsequent ranking, generated using the TOPSIS method with adjusted weights, identified the optimal vehicle choice, and the sensitivity analysis confirmed that all compensatory criteria were essential, as their removal significantly altered the results. The proposed model reduces subjectivity in method selection, enhances the robustness of MCDA, and provides researchers with a verifiable tool for analyzing complex decision problems.