Objective Weighting in MCDM: A Comparative Study of CRITIC, ITARA, MEREC, and Beyond

Objective weighting in Multiple Criteria Decision Making (MCDM) has become increasingly vital, addressing the challenge of criteria importance in complex decision-making scenarios. Unlike subjective approaches, objective weighting relies on data-driven methods that enhance consistency and mitigate biases in criteria prioritization. This article presents a comparative study of three prominent objective weighting techniques: CRITIC (Criteria Importance Through Intercriteria Correlation), ITARA (Information Theory and Absolute Ranking Algorithm), and MEREC (Mean Entropy-based Relative Criteria Weighting). Each method is evaluated based on criteria such as data dependency, computational efficiency, and sensitivity to data outliers. Through this comparison, we aim to highlight their respective strengths, limitations, and potential applications across various fields, including environmental sustainability and urban planning. The study also discusses these methods' challenges and suggests future directions for developing more robust weighting techniques, including hybrid models that integrate objective and subjective approaches. This work contributes to the evolving discourse on MCDM by offering insights into advanced objective weighting methodologies, underscoring their role in informed decision-making.