3 research outputs found
Modified EDAS Method Based on Cumulative Prospect Theory for Multiple Attributes Group Decision Making with Interval-valued Intuitionistic Fuzzy Information
The Interval-valued intuitionistic fuzzy sets (IVIFSs) based on the
intuitionistic fuzzy sets combines the classical decision method is in its
research and application is attracting attention. After comparative analysis,
there are multiple classical methods with IVIFSs information have been applied
into many practical issues. In this paper, we extended the classical EDAS
method based on cumulative prospect theory (CPT) considering the decision
makers (DMs) psychological factor under IVIFSs. Taking the fuzzy and uncertain
character of the IVIFSs and the psychological preference into consideration,
the original EDAS method based on the CPT under IVIFSs (IVIF-CPT-MABAC) method
is built for MAGDM issues. Meanwhile, information entropy method is used to
evaluate the attribute weight. Finally, a numerical example for project
selection of green technology venture capital has been given and some
comparisons is used to illustrate advantages of IVIF-CPT-MABAC method and some
comparison analysis and sensitivity analysis are applied to prove this new
methods effectiveness and stability.Comment: 48 page
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence
Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039
71421001,71910107002,71771037,71874023
71871149Sichuan University sksyl201705
2018hhs-5