Biography: Hei-Fong Ho received her MS degree in computer science from Queens College, New York City University, USA in 1992 and PhD degree in information management from National Cheng Kung University, Taiwan, in 2015. Currently, she is an associate professor in the Department of Business Administration at Chang Jung Christian University, Taiwan. Her research interests are in the fields of fuzzy decision making, human cognition, knowledge management, and data mining with consumer behavior. She has published several application-oriented articles in academic journals and international conferences.
Speech Title: A Novel Consensus Model with a Flexible Cognitive Framework for Group Decision Making
Abstract: Most research of heterogeneous multiple attribute group decision making (MAGDM), only consider the differences between decision makers' selection and compute the weight for each decision maker depending on the sum of differences of all attributes. Following the weighted aggregation, the final result is reached and the degree of consensus can be obtained.
In this research, we propose the process of fuzzy linguistic MAGDM which could be analogous to the memory behaviors of the human brain. Knowledge is elicited and validated, as in the short-term memory, and then eventually integrated into the long-term memory to serve as expertise. Therefore, memory-based decision implies two characters: discrimination and consistency. The proposed knowledge consistency (KC) index, characterized by measures of both individual and group consistencies on each attribute, can provide a more effective assessment to assign dynamically suitable experts’ weights than most existing MAGDM models. Furthermore, the proposed approach is flexible, as it enables decision makers to define the set of the parameters of the membership functions associated with labels, thus improving the quality of the linguistic term sets and leading to better assessments. This indicates that the KC index can indeed lead to a more satisfactory overall level of consensus. In addition, the mutual validation between the set of the parameters of the membership functions associated with labels by experts and the evaluation of the experts’ weights can be manifested in terms of the KC index.