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Linguistic Approaches for Multiple Criteria Decision Making and Applications

Submission Deadline: 30 June 2023 (closed)

Guest Editors

Prof. Huchang Liao, Sichuan University, China
Dr. Xingli Wu, Sichuan University, China
Dr. Abbas Mardani, University of South Florida, United States
Prof. Zeshui Xu, Sichuan University, China
Prof. Edmundas Kazimieras Zavadskas, Vilnius Gediminas Technical University, Lithuania


Artificial intelligence is an intelligent tool that assists human agents in decision making. An agent’s behavior shall be driven by an underlying preference model to clearly reflect the user’s preferences. Language is the most common and intuitive form of human expression. The acquisition of preference information requires not only a modeling language and suitable representations, but also automatic learning, discovery and modeling methods.


Linguistic approach deals with linguistic variables whose values of words or sentences are in natural or artificial language, rather than specific numbers. It enhances the feasibility, flexibility, and credibility of assessments, thus advancing decision analysis to a new research area – computing with words. To date, various models of linguistic expressions, such as probabilistic linguistic term sets, have been proposed to portray different categories of linguistic evaluation information. Real world decision-making problems usually involve selecting, ranking, or sorting a finite set of alternatives evaluated on a finite set of criteria. Multiple criteria decision making (MCDM) provides rich techniques to solve such problems, designed to recommend decisions that are consistent with the value systems of decision-makers. There are three well-established theories for modeling value systems: 1) multiple criteria value/utility theory, 2) outranking relations, and 3) decision rules, and two ways of preference elicitation: 1) aggregation approaches with direct preference elicitation, where decision makers are required to provide the values of parameters in a default preference model; 2) disaggregation approaches with indirect preference elicitation based on holistic judgments on reference alternatives. The theory and methods of MCDM based on linguistic approaches have gained much attention of researchers in the past, and have made great progress in research. However, in the context of big data, decision-making problems tend to be complex. For example, online reviews are an example of evaluation information that is large in scale and presents an unstructured form. How to deal with complex MCDM problems under linguistic settings still needs further research.


This special issue aims at encouraging researchers and practitioners to address challenges associated with decision making methodologies inlinguistic contexts. We are looking for papers with a focus on MCDM methods considering complex situations, including large-scale and unstructured linguistic evaluations, large-scale alternatives and large-scale decision makers. In particular, new approaches of decision making in data-driven topics are especially welcome. Potential topics include but are not limited to methods and applications in:


n  Natural language processing and computing with words;

n  Large-scale group decision making with linguistic approaches;

n  Multiple criteria decision making with linguistic approaches;

n  Preference disaggregation analysis with linguistic approaches;

n  Data-driven decision making with linguistic approaches;

n  Decision support system with linguistic approaches.


Computing with words; multiple criteria decision making; linguistic approach; big data; preference model

Published Papers

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