{"title":"An approach for fuzzy group decision making and consensus measure with hesitant judgments of experts","authors":"Chao Huang, Xiaoyue Wu, Mingwei Lin, Zeshui Xu","doi":"10.1007/s10115-024-02098-3","DOIUrl":null,"url":null,"abstract":"<p>In some actual decision-making problems, experts may be hesitant to judge the performances of alternatives, which leads to experts providing decision matrices with incomplete information. However, most existing estimation methods for incomplete information in group decision-making (GDM) neglect the hesitant judgments of experts, possibly making the group decision outcomes unreasonable. Considering the hesitation degrees of experts in decision judgments, an approach is proposed based on the triangular intuitionistic fuzzy numbers (TIFNs) and TODIM (interactive and multiple criteria decision-making) method for GDM and consensus measure. First, TIFNs are applied to handle incomplete information due to the hesitant judgments of experts. Second, considering the risk attitudes of experts, a decision-making model is proposed to rank alternatives for GDM with incomplete information. Subsequently, based on measuring the concordance between solutions, a consensus model is presented to measure the group’s and individual’s consensus degrees. Finally, an illustrative example is presented to show the detailed implementation procedure of the proposed approach. The comparisons with some existing estimation methods verify the effectiveness of the proposed approach for handling incomplete information. The impacts and necessities of experts’ hesitation degrees are discussed by a sensitivity analysis.</p>","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"7 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge and Information Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10115-024-02098-3","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In some actual decision-making problems, experts may be hesitant to judge the performances of alternatives, which leads to experts providing decision matrices with incomplete information. However, most existing estimation methods for incomplete information in group decision-making (GDM) neglect the hesitant judgments of experts, possibly making the group decision outcomes unreasonable. Considering the hesitation degrees of experts in decision judgments, an approach is proposed based on the triangular intuitionistic fuzzy numbers (TIFNs) and TODIM (interactive and multiple criteria decision-making) method for GDM and consensus measure. First, TIFNs are applied to handle incomplete information due to the hesitant judgments of experts. Second, considering the risk attitudes of experts, a decision-making model is proposed to rank alternatives for GDM with incomplete information. Subsequently, based on measuring the concordance between solutions, a consensus model is presented to measure the group’s and individual’s consensus degrees. Finally, an illustrative example is presented to show the detailed implementation procedure of the proposed approach. The comparisons with some existing estimation methods verify the effectiveness of the proposed approach for handling incomplete information. The impacts and necessities of experts’ hesitation degrees are discussed by a sensitivity analysis.
期刊介绍:
Knowledge and Information Systems (KAIS) provides an international forum for researchers and professionals to share their knowledge and report new advances on all topics related to knowledge systems and advanced information systems. This monthly peer-reviewed archival journal publishes state-of-the-art research reports on emerging topics in KAIS, reviews of important techniques in related areas, and application papers of interest to a general readership.