Pairwise Outperformance Aggregations in Multiple-Criteria Analysis of Alternatives

Granat, J. & Makowski, M. ORCID: https://orcid.org/0000-0002-6107-0972 (2026). Pairwise Outperformance Aggregations in Multiple-Criteria Analysis of Alternatives. In: Artificial Intelligence for Knowledge Acquisition and Management. Eds. Pondel, M., Chomiak-Orsa, I., Mercier-Laurent, E., Berka, P., & Przysucha, L., pp. 428-435 Springer Cham. ISBN 978-3-032-18922-6 10.1007/978-3-032-18920-2_41.

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Abstract

Numerous methods have been developed for multiple criteria analysis of a given set of discrete alternatives. Only a small fraction of them are suitable for users with limited modeling skills and for problems with a large number of alternatives and criteria, optionally organized into hierarchical structures. This paper presents new methods effectively supporting analysis of such problems, as well as using a very simple, but yet effective, way of interactive specification of preferences suitable also for a large number of diversified users without experience in analytical methods. The approach is based on the equitable aggregations concept and the developed pairwise outperformance aggregations considering inter-alternative relations. The methods have been implemented for, and applied to analyzing real-life problems, in particular multiple-criteria analysis of future energy technologies, which involves large numbers of both alternatives and criteria.

Item Type:	Book Section
Research Programs:	Energy, Climate, and Environment (ECE) Energy, Climate, and Environment (ECE) > Integrated Assessment and Climate Change (IACC) Energy, Climate, and Environment (ECE) > Sustainable Service Systems (S3)
Depositing User:	Luke Kirwan
Date Deposited:	02 Oct 2026 08:01
Last Modified:	02 Oct 2026 08:01
URI:	https://pure.iiasa.ac.at/21914

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