5,965 research outputs found
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
A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme
Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of expertsâ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of expertsâ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of expertsâ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version
Dynamic reference point method with probabilistic linguistic information based on the regret theory for public health emergency decision-making
Group emergency decision-making is an uncertain and dynamic
process, in which the decision makers may be bounded rational
and have a risk appetite. To depict the vague qualitative assessments, the probabilistic linguistic term sets are employed to
express the perceptions of decision makers. First, considering the
regret-aversion of the decision makersâ psychological characteristic, the value function and the regret-rejoice function in the regret
theory are modified to adapt the probabilistic linguistic information. Second, the definition and aggregation operators of the
probabilistic linguistic time variable are proposed to describe and
aggregate the dynamic decision information. Third, the probabilistic linguistic models based on the dynamic reference point
method and the regret theory are studied to maximise the
expectation-levels of alternatives at the relative time point. The
proposed method is applied to select the optimal response strategy for the outbreak of COVID-19 in China. Finally, the comparative analysis is designed to verify the applicability and
reasonability of the proposed method
A probabilistic linguistic thermodynamic method based on the water-filling algorithm and regret theory for emergency decision making
Since thermodynamics can describe the energy of matter and its
form of storage or transformation in the system, it is introduced
to resolve the uncertain decision-making problems. The paper
proposes the thermodynamic decision-making method which
considers both the quantity and quality of the probabilistic linguistic
decision information. The analogies for thermodynamical
indicators: energy, exergy and entropy are developed under the
probabilistic linguistic circumstance. The probabilistic linguistic
thermodynamic method combines the regret theory which captures
decision makersâ regret-aversion and the objective weight of
criterion obtained by the water-filling algorithm. The proposed
method is applied to select the optimal solution to respond to
the floods in Chongqing, China. The self-comparison is conducted
to verify the effectiveness of the objective weight obtained by
the water-filling algorithm and regret theory in the probabilistic
linguistic thermodynamic method. The reliability and feasibility of
the proposed method are verified by comparative analysis with
other decision-making methods by some simulation experiments
and non-parametric tests
Fuzzy expert systems in civil engineering
Imperial Users onl
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