Abstract
An initial assessment on a given set of alternatives is necessary for understanding complex decision problems and their possible solutions. Attitudes and preferences articulate and come together under a decision process that should be explicitly modeled for understanding and solving the inherent conflict of decision making. This paper revises multi-criteria modeling of imprecise data, inferring outranking and indifference binary relations and classifying alternatives according to their similarity or dependency. After the initial assessment on the set of alternatives, preference orders are built according to the attitudes of decision makers, aiding the decision process by identifying solutions with minimal dissention.
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Acknowledgements
This research has been partially supported by the Danish Industry Foundation and the Center for research in the Foundations of Electronic Markets (CFEM), funded by the Danish Council for Strategic Research.
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Franco, C., Hougaard, J.L., Nielsen, K. (2017). Clustering Alternatives and Learning Preferences Based on Decision Attitudes and Weighted Overlap Dominance. In: Torra, V., Dahlbom, A., Narukawa, Y. (eds) Fuzzy Sets, Rough Sets, Multisets and Clustering. Studies in Computational Intelligence, vol 671. Springer, Cham. https://doi.org/10.1007/978-3-319-47557-8_20
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DOI: https://doi.org/10.1007/978-3-319-47557-8_20
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