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Contingent preference disaggregation model for multiple criteria sorting problem

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  • Kadziński, Miłosz
  • Ghaderi, Mohammad
  • Dąbrowski, Maciej

Abstract

The conventional preference disaggregation approaches for multiple criteria sorting aim at reconstructing an entire set of assignment examples provided by a Decision Maker (DM) with a single preference model instance. In case the DM’s holistic preference information is not consistent with an assumed model, one needs to accept that some assignment examples are not reproduced. We propose a new approach for handling inconsistency in the context of a threshold-based value-driven sorting procedure. Specifically, we introduce preference disaggregation methods for reconstructing all assignment examples with a set of complementary preference models. The proposed approach builds on the assumption that the importance of particular criteria or, more generally, the shape of marginal value functions and their maximal shares in the comprehensive value are contingent (i.e., dependent) on the performance profile of a given alternative. Therefore, in case of inconsistency, the set of assignment examples is divided into subsets, each of which is reconstructed by a unique model to be used only if certain circumstances are valid. We present three methods for learning a set of contingent models, allowing different degrees of variation in the contingent models along two dimensions: the shape of marginal value functions and interrelations between the models. To apply such a set for classification of non-reference alternatives, we learn a decision tree which makes the application of a given model dependent on the alternatives’ profiles represented by the performances on particular criteria, hence allowing to select an appropriate model among the competing models to evaluate a non-reference alternative. The method’s applicability is demonstrated on a problem of evaluating research units representing different fields of science.

Suggested Citation

  • Kadziński, Miłosz & Ghaderi, Mohammad & Dąbrowski, Maciej, 2020. "Contingent preference disaggregation model for multiple criteria sorting problem," European Journal of Operational Research, Elsevier, vol. 281(2), pages 369-387.
  • Handle: RePEc:eee:ejores:v:281:y:2020:i:2:p:369-387
    DOI: 10.1016/j.ejor.2019.08.043
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    2. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2023. "Multiple criteria sorting models and methods. Part II: theoretical results and general issues," 4OR, Springer, vol. 21(2), pages 181-204, June.
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    4. Marco Cinelli & Matteo Spada & Wansub Kim & Yiwen Zhang & Peter Burgherr, 2021. "MCDA Index Tool: an interactive software to develop indices and rankings," Environment Systems and Decisions, Springer, vol. 41(1), pages 82-109, March.
    5. Leyva López, Juan Carlos & Solano Noriega, Jesús Jaime & Figueira, José Rui & Liu, Jun & Gastélum Chavira, Diego Alonso, 2021. "Non-dominated sorting genetic-based algorithm for exploiting a large-sized fuzzy outranking relation," European Journal of Operational Research, Elsevier, vol. 293(2), pages 615-631.
    6. Podinovski, Vladislav V., 2020. "Maximum likelihood solutions for multicriterial choice problems," European Journal of Operational Research, Elsevier, vol. 286(1), pages 299-308.
    7. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2021. "MURAME parameter setting for creditworthiness evaluation: data-driven optimization," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 295-339, June.
    8. Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu & Mao, Xiaoxin & Wang, Yao, 2020. "A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples," European Journal of Operational Research, Elsevier, vol. 286(3), pages 963-985.
    9. Kadziński, Miłosz & Ciomek, Krzysztof, 2021. "Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting," European Journal of Operational Research, Elsevier, vol. 293(2), pages 658-680.
    10. Ghaderi, Mohammad, 2022. "Public health interventions in the face of pandemics: Network structure, social distancing, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1016-1031.
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    12. Tlili, Ali & Belahcène, Khaled & Khaled, Oumaima & Mousseau, Vincent & Ouerdane, Wassila, 2022. "Learning non-compensatory sorting models using efficient SAT/MaxSAT formulations," European Journal of Operational Research, Elsevier, vol. 298(3), pages 979-1006.
    13. Jiapeng Liu & Miłosz Kadziński & Xiuwu Liao, 2023. "Modeling Contingent Decision Behavior: A Bayesian Nonparametric Preference-Learning Approach," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 764-785, July.
    14. Ru, Zice & Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu, 2023. "Probabilistic ordinal regression methods for multiple criteria sorting admitting certain and uncertain preferences," European Journal of Operational Research, Elsevier, vol. 311(2), pages 596-616.
    15. Cinelli, Marco & Kadziński, Miłosz & Miebs, Grzegorz & Gonzalez, Michael & Słowiński, Roman, 2022. "Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system," European Journal of Operational Research, Elsevier, vol. 302(2), pages 633-651.

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