Fuzzy similarity metrics and their application to consensus reaching in group decision making

Abstract

This paper presents an account of fuzzy similarity metrics that have been proposed to quantify consensus in Multi Criteria Group Decision Making. Fuzzy similarity metrics are indispensable to determine consensus when experts evaluate alternatives in fuzzy terms, which capture experts’ uncertainty and hesitancy. Furthermore, factors such as the level of expertise or cognitive bias lead to disagreements within the group. The fuzzy similarity metrics described in this article are used to measure the similarity between type 1 and type 2 fuzzy sets, and fuzzy numbers. Consensus can be quantified at three different levels: criteria judgement, alternative judgement, or expert preferences. Promising future work includes the incorporation of social fuzzy measures under the umbrella of multi-agent systems as well as the analysis of fuzzy intuitionistic sets.