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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions Cong-Cong, Li; Dong, Yucheng; Xu, Yejun; Chiclana, Francisco; Herrera-Viedma, Enrique; Herrera, Francisco. - : Elsevier, 2018 Abstract: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. ; The reciprocal preference relation (RPR) is a powerful tool to represent decision makers’ preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the ‘classical’ RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed. Keyword: Additive consistency; Consistency-driven method; Decision making; Reciprocal preference relation URL: http://hdl.handle.net/2086/17368 https://doi.org/10.1016/j.inffus.2018.12.004 BASE Hide details 2 Flexible inverse adaptive fuzzy inference model to identify the evolution of Operational Value at Risk for improving operational risk management Pena, Alejandro; Bonet, Isis; Lochmuller, Christian. - : Elsevier, 2018 BASE Show details 3 A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events Pena, Alejandro; Bonet, Isis; Lochmuller, Christian. - : Elsevier, 2018 BASE Show details 4 Preference similarity network structural equivalence clustering based consensus group decision making model Kamis, Nor Hanimah; Chiclana, Francisco; Levesley, Jeremy. - : Elsevier, 2018 BASE Show details

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