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
Summary
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.
Keywords
Computing with words; multiple criteria decision making; linguistic approach; big data; preference model
Published Papers
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Open Access
ARTICLE
Multidimensional Quality Evaluation of Graduate Thesis: Based on the Probabilistic Linguistic MABAC Method
Yuyan Luo, Xiaoxu Zhang, Tao Tong, Yong Qin, Zheng Yang
Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2023.025413
（This article belongs to this Special Issue:
Linguistic Approaches for Multiple Criteria Decision Making and Applications)
Abstract Graduate education is the main way to train high-level innovative talents, the basic layout to cope with the
global talent competition, and the important cornerstone for implementing the innovation-driven development
strategy and building an innovation-driven country. Therefore, graduate education is of great remarkably to the
development of national education. As an important manifestation of graduate education, the quality of a graduate
thesis should receive more attention. It is conducive to promoting the quality of graduates by supervising and
examining the quality of the graduate thesis. For this purpose, this work is based on text mining, expert interviews,
and questionnaire surveys…
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Open Access
ARTICLE
A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making
Runze Liu, Liguo Fei, Jianing Mi
Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2039-2067, 2023, DOI:10.32604/cmes.2023.024927
（This article belongs to this Special Issue:
Linguistic Approaches for Multiple Criteria Decision Making and Applications)
Abstract Probabilistic linguistic term sets (PLTSs) are an effective tool for expressing subjective human cognition that offer advantages in the field of multi-attribute decision-making (MADM). However, studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers (DMs) in certain circumstances, such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete, thus affecting their role in the decision-making process. Belief function theory is a leading stream of thought in uncertainty processing that is suitable for dealing with the limitations of PLTS. Therefore, the purpose of this study is to extend…
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Graphic Abstract
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Open Access
ARTICLE
Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection
Mingjun Jiang
Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 487-515, 2023, DOI:10.32604/cmes.2023.024014
（This article belongs to this Special Issue:
Linguistic Approaches for Multiple Criteria Decision Making and Applications)
Abstract Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns, leading to non-cooperative behaviors during the consensus-reaching process. Many studies on non-cooperative behavior management assumed that the maximum degree of cooperation of experts is to totally accept the revisions suggested by the moderator, which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process. In addition, when grouping a large group into subgroups by clustering methods, existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously. In this study, we introduce a…
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