Personalized individual semantics in computing with words for supporting linguistic group decision making

Abstract

In the thesis, the author focuses on the idea that words have different meanings for different people, it is called personalized individual semantics (PISs). To show the PIS, some decision making models and approaches with linguistic preference relations are studied to compute the personalized numerical scales for linguistic terms. Under different decision making contexts, the constructed models and the designed computation processes are different. Three decision making contexts to study the PIS of each expert is considered, i.e., classical decision making, hesitant linguistic decision making and large-scale group decision making.