Visual consensus feedback mechanism for group decision making with complementary linguistic preference relations
A visual consensus feedback mechanism for group decision making (GDM) problems with complementary linguistic preference relations 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 defined 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 consensus 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 feedback mechanism also includes individualised recommendations 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.
Citation:Chiclana, F. Wu, J. Herrera-Viedma, E. (2014) Visual consensus feedback mechanism for group decision making with complementary linguistic preference relations. In: Vicenc Torra et al. (Eds.): MDAI 2014, LNAI 8825, pp. 72–83
Research Group:Centre for Computational Intelligence