Multi-Criteria Decision Analysis

Multi-Criteria Decision Analysis studies how to compare alternatives when no single measure captures what matters, such as cost, quality, fairness, or risk. Common approaches include Analytic Hierarchy Process pairwise comparisons for setting criterion weights, TOPSIS and related methods for ranking, and fuzzy or stochastic methods for representing uncertain judgments. Pareto optimization and interactive preference learning help explore trade-offs, with applications ranging from site selection and policy choices to evaluating AI models. A central challenge is producing rankings that are robust and interpretable without hiding value judgments in the choice of criteria, weights, or aggregation method.