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. Arcidiacono, Sally Giuseppe; Corrente, Salvatore; Greco, Salvatore: Robust stochastic sorting with interacting criteria hierarchically structured (2021)
  2. Corrente, S.; Figueira, J. R.; Greco, S.: Pairwise comparison tables within the deck of cards method in multiple criteria decision aiding (2021)
  3. Kadziński, Miłosz; Ciomek, Krzysztof: Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting (2021)
  4. Arcidiacono, Sally Giuseppe; Corrente, Salvatore; Greco, Salvatore: As simple as possible but not simpler in multiple criteria decision aiding: the robust-stochastic level dependent Choquet integral approach (2020)
  5. Cerreia-Vioglio, Simone; Giarlotta, Alfio; Greco, Salvatore; Maccheroni, Fabio; Marinacci, Massimo: Rational preference and rationalizable choice (2020)
  6. Giarlotta, Alfio; Watson, Stephen: A bi-preference interplay between transitivity and completeness: reformulating and extending Schmeidler’s theorem (2020)
  7. Kadziński, Miłosz; Ghaderi, Mohammad; Dąbrowski, Maciej: Contingent preference disaggregation model for multiple criteria sorting problem (2020)
  8. Kadziński, Miłosz; Martyn, Krzysztof; Cinelli, Marco; Słowiński, Roman; Corrente, Salvatore; Greco, Salvatore: Preference disaggregation for multiple criteria sorting with partial monotonicity constraints: application to exposure management of nanomaterials (2020)
  9. Mayag, Brice; Bouyssou, Denis: Necessary and possible interaction between criteria in a 2-additive Choquet integral model (2020)
  10. Podinovski, Vladislav V.: Maximum likelihood solutions for multicriterial choice problems (2020)
  11. Tsionas, Mike G.: A note on sigma-mu efficiency analysis as a methodology for evaluating units through composite indicators (2020)
  12. Viappiani, Paolo; Boutilier, Craig: On the equivalence of optimal recommendation sets and myopically optimal query sets (2020)
  13. Greco, Salvatore; Ishizaka, Alessio; Tasiou, Menelaos; Torrisi, Gianpiero: Sigma-mu efficiency analysis: a methodology for evaluating units through composite indicators (2019)
  14. Liu, Jiapeng; Liao, Xiuwu; Kadziński, Miłosz; Słowiński, Roman: Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria (2019)
  15. Tomczyk, Michał K.; Kadziński, Miłosz: EMOSOR: evolutionary multiple objective optimization guided by interactive stochastic ordinal regression (2019)
  16. Alcantud, José Carlos R.; Biondo, Alessio E.; Giarlotta, Alfio: Fuzzy politics. I: The genesis of parties (2018)
  17. Arcidiacono, Sally Giuseppe; Corrente, Salvatore; Greco, Salvatore: GAIA-SMAA-PROMETHEE for a hierarchy of interacting criteria (2018)
  18. Belahcène, K.; Labreuche, C.; Maudet, N.; Mousseau, V.; Ouerdane, W.: An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples (2018)
  19. Costa, Ana Sara; Figueira, José Rui; Borbinha, José: A multiple criteria nominal classification method based on the concepts of similarity and dissimilarity (2018)
  20. Fasth, Tobias; Larsson, Aron; Ekenberg, Love; Danielson, Mats: Measuring conflicts using cardinal ranking: an application to decision analytic conflict evaluations (2018)

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