Ordinal regression revisited: multiple criteria ranking with a set of additive value functions. VisualUTA is the first implementation of the UTA^GMS method for multiple criteria ranking of alternatives from set A using a set of additive value functions which result from an ordinal regression. The preference information provided by the decision maker is a set of pairwise comparisons on a subset of alternatives A^R, called reference alternatives. The preference model built via ordinal regression is a set of all additive value functions compatible with the preference information. Using this model, one can define two relations in the set A: the necessary weak preference relation (strong outranking) which holds for any two alternatives a, b from set A if and only if for all compatible value functions a is preferred to b, and the possible weak preference relation (weak outranking) which holds for this pair if and only if for at least one compatible value function a is preferred to b. These relations establish a necessary (strong) and a possible (weak) ranking of alternatives from A, being, respectively, a partial preorder and a strongly complete and negatively transitive relation. The UTA^GMS method is intended to be used interactively, with an increasing subset A^R and a progressive statement of pairwise comparisons. When no preference information is provided, the necessary weak preference relation is a weak dominance relation, and the possible weak preference relation is a complete relation. Every new pairwise comparison of reference alternatives is enriching the necessary relation and it is impoverishing the possible relation, so that they converge with the growth of the preference information. Moreover, the method can support the decision maker also when his/her preference statements cannot berepresented in terms of an additive value function.

References in zbMATH (referenced in 109 articles , 1 standard article )

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  2. Vetschera, Rudolf: Deriving rankings from incomplete preference information: a comparison of different approaches (2017)
  3. Zhang, Hongjun; Yin, Chengxiang; Qi, Xiuli; Zhang, Rui; Kang, Xingdang: Cognitive best worst method for multiattribute decision-making (2017)
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  5. Angilella, Silvia; Bottero, Marta; Corrente, Salvatore; Ferretti, Valentina; Greco, Salvatore; Lami, Isabella M.: Non additive robust ordinal regression for urban and territorial planning: an application for siting an urban waste landfill (2016)
  6. Branke, Juergen; Corrente, Salvatore; Greco, Salvatore; Słowiński, Roman; Zielniewicz, Piotr: Using Choquet integral as preference model in interactive evolutionary multiobjective optimization (2016)
  7. Cheng, Li-Chen; Chen, Yen-Liang; Chiang, Yu-Chia: Identifying conflict patterns to reach a consensus -- a novel group decision approach (2016)
  8. Corrente, Salvatore; Greco, Salvatore; Słowiński, Roman: Multiple criteria hierarchy process for ELECTRE tri methods (2016)
  9. Covantes, Edgar; Fernández, Eduardo; Navarro, Jorge: Handling the multiplicity of solutions in a MOEA based PDA-THESEUS framework for multi-criteria sorting (2016)
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  12. Ma, Li-Ching: A new group ranking approach for ordinal preferences based on group maximum consensus sequences (2016)
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  17. Giarlotta, Alfio: Normalized and strict NaP-preferences (2015)
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  19. Liu, Jiapeng; Liao, Xiuwu; Yang, Jian-bo: A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty (2015)
  20. Pei, Wenbin; Lin, He; Li, Li: Multi-decision-makers-based monotonic variable consistency rough set approach with multiple attributes and criteria (2015)