Visual consensus feedback mechanism for group decision making with complementary linguistic preference relations

Abstract

A visual consensus feedback mechanism for group decision making (GDM) problems with complementary linguistic preference rela- tions is presented. Linguistic preferences are modelled using triangular fuzzy membership functions, and 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 de ned and used to measure the consensus level (CL). 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 provides experts with visual representations of their consen- sus status to easily `see' their consensus position within the group as well as to identify the alternatives and preference values that should be reconsidered for changing in the subsequent consensus round. The feed- back mechanism also includes individualised recommendations to those identi ed experts on changing their identi ed preference values and vi- sual graphical simulation of future consensus status if the recommended values were to be implemented.