Unifying Multiple MCDA Rankings: Aggregation of Rankings Through Methodological and Computational Perspectives

On predefined and synthetic datasets with controlled noise, we test Borda, Copeland, Footrule, Kemeny–Young, Median Rank, PageRank, Plackett–Luce, Reciprocal Rank Fusion, and Schulze, varying numbers of alternatives and rankings to assess scalability. Positional and simple pairwise rules tend to agree and reward consistently strong options; distance-based and probabilistic models can shift winners toward items closest to the average order or with higher inferred worth. Under low disagreement, most methods yield similar consensus, permitting flexible choice; under high disagreement, rankings diverge and runtimes spread widely. Borda, Median, RRF, and Schulze scale well; Kemeny–Young and Plackett–Luce become costly. We provide a comparative, mechanism-aware view of aggregation choices, linking robustness and computational feasibility to disagreement regimes to guide practical MCDA in real decision settings. Our code is available at https://github.com/Valdecy/pyRankMCDA.