This book proposes a novel CWW model to personalize individual semantics in linguistic decision making, based on two new concepts: numerical scale and consistency-driven methodology. The numerical scale model provides a unified framework to connect different linguistic symbolic computational models for CWW, and the consistency-driven methodology customizes individuals' semantics to support linguistic group decision making by setting personalized numerical scales. The book is a valuable resource for researchers and postgraduates who are interested in CWW in linguistic decision making.

Autorentext
Yucheng Dong is a Professor at the Business School of Sichuan University, China. His current research interests include linguistic decision making, group decision making, opinion dynamics, social network decision making. He is the author or coauthor of over 90 refereed articles published in international journals, a member of the editorial board of Information Fusion and the area editor of Computers and Industrial Engineering.
Jiuping Xu is a Cheung Kong Professor at the Business School of Sichuan University, China. He has published over 40 books, and he is the author or coauthor of more than 200 refereed articles published in international journals. Dr. Xu is the President of the International Society for Management Science and Engineering Management, and the Editor-in-Chief of the International Journal of Management Science and Engineering Management.

Inhalt
1Introduction1.1Computing with words in decision making1.2Linguistic symbolic computational models1.2.1The 2-tuple linguistic model1.2.2The proportional 2-tuple linguistic model1.2.3Linguistic hierarchy1.2.4Hesitant linguistic term set1.3A core problem in linguistic decision makingReferences2Numerical scale model2.1Numerical scale2.1.1The definition of numerical scale2.1.2Interval numerical scale2.2Linguistic computational model2.2.1Linguistic computational framework2.2.2Linguistic aggregation2.2.3Illustrative example2.3Setting the interval numerical scale based on IT2 FSs2.3.1Generalizing the Wang and Hao model2.3.2Comparative studyReferences3A unified framework3.1Connecting numerical scale model to linguistic hierarchy3.1.1Definition of unbalanced linguistic term sets3.1.2The revised retranslation process in linguistic hierarchy3.1.3Equivalence between numerical scale model and linguistic hierarchy3.2Hesitant unbalanced linguistic information3.2.1Possibility degree formulas3.2.2Unbalanced hesitant linguistic aggregationReferences4Consistency of interval-like reciprocal preference relations4.1Consistency of interval-valued reciprocal preference relations4.1.1Interval-valued reciprocal preference relations4.1.2Average-case consistency measure4.1.3Average-case consistency improving method4.2Consistency of hesitant linguistic preference relations4.2.1Hesitant linguistic preference relations4.2.2Interval consistency measure4.2.3Interval consistency measure VS Normalization method4.2.4Connection among ICI, NCI, and ACIReferences5Consistency-driven methodology5.1Personalized individual semantics in linguistic term sets5.1.1Consistency-driven methodology to set personalized interval numerical scales5.1.2A CWW framework with PISs5.2Personalized individual semantics in hesitant linguistic contexts  5.2.1Personalizing hesitant individual semantics5.2.2Numerical examples and analysis5.2.3Discussion: Advantages and limitationsReferences6Applications in various decision problems6.1The analytic hierarchy process6.1.1Basic knowledge6.1.2The framework of the AHP with PISs6.1.3Consistency-driven methodology to deal with PISs6.1.4Personalized AHP interval numerical scales6.2Consensus model in linguistic GDM problem6.2.1A GDM framework with PISs6.2.2Consensus reaching process6.3MAGDM with linguistic preference information on alternatives6.3.1MAGDM with numerical preference information6.3.2A PIS based MAGDM framework6.3.3Obtaining the individual numerical scales with PISs6.3.4Numerical and simulation analysisReferences