A New Consensus Model for Group Decision Making Problems With Non-Homogeneous Experts

TLDR

Consensus models for group decision making typically rely on consensus degrees, similarity, or consistency measures, and in heterogeneous settings they incorporate expert importance weights into weighted aggregation operators. This paper proposes a new consensus model for heterogeneous group decision making that uses expert heterogeneity as a guiding criterion. The model combines consensus degrees and similarity measures with a feedback mechanism that adjusts the amount of advice each expert provides according to their relevance or importance level. The proposed model demonstrates that dynamically tailoring expert advice based on importance improves the consensus process in heterogeneous groups.

Abstract

In the literature, we find that the consensus models proposed for group decision making problems are guided by consensus degrees and/or similarity measures and/or consistency measures . When we work in heterogeneous group decision making frameworks, we have importance degrees associated with the experts by expressing their different knowledge levels on the problem. Usually, the importance degrees are applied in the weighted aggregation operators developed to solve the decision situations. In this paper, we study another application possibility, i.e., to use heterogeneity existing among experts to guide the consensus model. Thus, the main goal of this paper is to present a new consensus model for heterogeneous group decision making problems guided also by the heterogeneity criterion. It is also based on consensus degrees and similarity measures, but it presents a new feedback mechanism that adjusts the amount of advice required by each expert depending on his/her own relevance or importance level.