VisualUTA

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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  1. Ishizaka, Alessio; Siraj, Sajid: Are multi-criteria decision-making tools useful? An experimental comparative study of three methods (2018)
  2. Karakaya, G.; Köksalan, M.; Ahipaşaoğlu, S. D.: Interactive algorithms for a broad underlying family of preference functions (2018)
  3. Liu, Jiapeng; Liao, Xiuwu; Huang, Wei; Yang, Jian-bo: A new decision-making approach for multiple criteria sorting with an imbalanced set of assignment examples (2018)
  4. Sobrie, Olivier; Gillis, Nicolas; Mousseau, Vincent; Pirlot, Marc: UTA-poly and UTA-splines: additive value functions with polynomial marginals (2018)
  5. Wang, Haichao; Lahdelma, Risto; Salminen, Pekka: Complementary judgment matrix method with imprecise information for multicriteria decision-making (2018)
  6. Wu, Jian-zhang; Pap, Endre; Szakal, Aniko: Two kinds of explicit preference information oriented capacity identification methods in the context of multicriteria decision analysis (2018)
  7. Belahcene, Khaled; Labreuche, Christophe; Maudet, Nicolas; Mousseau, Vincent; Ouerdane, Wassila: Explaining robust additive utility models by sequences of preference swaps (2017)
  8. Benabbou, Nawal; Perny, Patrice; Viappiani, Paolo: Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems (2017)
  9. Ben Abdallah, N.; Destercke, S.; Sallak, M.: Easy and optimal queries to reduce set uncertainty (2017)
  10. Branke, Juergen; Corrente, Salvatore; Greco, Salvatore; Gutjahr, Walter: Efficient pairwise preference elicitation allowing for indifference (2017)
  11. Ciomek, Krzysztof; Kadziński, Miłosz; Tervonen, Tommi: Heuristics for selecting pair-wise elicitation questions in multiple criteria choice problems (2017)
  12. Corrente, Salvatore; Doumpos, Michael; Greco, Salvatore; Słowiński, Roman; Zopounidis, Constantin: Multiple criteria hierarchy process for sorting problems based on ordinal regression with additive value functions (2017)
  13. Corrente, Salvatore; Greco, Salvatore; Słowiński, Roman: Handling imprecise evaluations in multiple criteria decision aiding and robust ordinal regression by (n)-point intervals (2017)
  14. Fernández, Eduardo; Figueira, José Rui; Navarro, Jorge; Roy, Bernard: ELECTRE TRI-nB: a new multiple criteria ordinal classification method (2017)
  15. Ghaderi, Mohammad; Ruiz, Francisco; Agell, Núria: A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding (2017)
  16. Giarlotta, Alfio; Watson, Stephen: Well-graded families of NaP-preferences (2017)
  17. Giarlotta, Alfio; Watson, Stephen: Necessary and possible indifferences (2017)
  18. Kaddani, Sami; Vanderpooten, Daniel; Vanpeperstraete, Jean-Michel; Aissi, Hassene: Weighted sum model with partial preference information: application to multi-objective optimization (2017)
  19. Kadziński, Miłosz; Ghaderi, Mohammad; Wąsikowski, Jakub; Agell, Núria: Expressiveness and robustness measures for the evaluation of an additive value function in multiple criteria preference disaggregation methods: an experimental analysis (2017)
  20. Qi, Xiuli; Yin, Chengxiang; Cheng, Kai; Liao, Xianglin: The interval cognitive network process for multi-attribute decision-making (2017)