A multi-view approach to multi-criteria decision making.

In this paper, we investigate a new approach to multi-criteria decision making (MCDM) centred upon the application of canonical correlation analysis (CCA) to distinct groups of judgement criteria. By resorting to MV-MCDM (multi-view multi-criteria decision making), one can estimate reliable values for criteria weights via CCA for multi-view multi-criteria problems; reduce the dimensionality of the decision matrix by considering only one of the available views; and easily extend well-known MCDM methods, such as simple additive weighting (SAW) and technique for order of preferences by similarity to ideal solution (TOPSIS). MV-MCDM also allows the adoption of different aggregation methods (such as the Choquet integral and a new heuristic based on radar charts) to generate the overall scores of the alternatives. A numerical example with the multi-view versions of SAW and TOPSIS demonstrates the applicability of the novel approach.