Visual information feedback mechanism and attitudinal prioritisation method for group decision making with triangular fuzzy complementary preference relations
A visual information feedback mechanism for group decision making (GDM) problems with triangular fuzzy complementary preference relations (TFCPRs) is investigated. The concepts of similarity degree (SD) between two experts as well as the proximity degree (PD) between an expert and the rest of experts in the group are developed for TFCPRs. The consensus level (CL) is defined by combining SD and PD, and a feedback mechanism is proposed to identify experts, alternatives and corresponding preference values that contribute less to consensus. The novelty of this feedback mechanism is that it will provide each expert with visual representations of his/her consensus status to easily ‘see’ his/her consensus position within the group as well as to identify the alternatives and preference values that he/she should be reconsidered for changing in the subsequent consensus round. The feedback mechanism also includes individualised recommendation to those identified experts on changing their identified preference values and visual graphical simulation of future consensus status if the recommended values were to be implemented. Based on the continuous ordered weighted average (COWA) operator, the triangular fuzzy COWA (TF-COWA) operator is defined, and a novel attitudinal expected score function for TFCPRs is developed. The advantage of this function is that the alternatives are ranked by taking into account the attitudinal character of the group of experts or its moderator if applicable. Additionally, a ranking sensitivity analysis of the attitudinal expected score function with respect to the attitudinal parameter is provided.
Citation : Wu, J. and Chiclana, F. (2014) Visual information feedback mechanism and attitudinal prioritisation method for group decision making with triangular fuzzy complementary preference relations. Information Sciences, 279, pp. 716-734
ISSN : 0020-0255
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes