Affiliations:
Universidad Autónoma del Estado de Hidalgo, Instituto de Ciencias Básicas e Ingenierías, Área Académica de Computación y Electrónica, Carretera Pachuca - Tulancingo Km. 4.5, Mineral de la Reforma, Hidalgo, México
Correspondence:
[*]
Corresponding author. Omar López-Ortega, Universidad Autónoma del Estado de Hidalgo, Instituto de Ciencias Básicas e Ingenierías, Área Académica de Computación y Electrónica, Carretera Pachuca - Tulancingo Km. 4.5, Mineral de la Reforma, Hidalgo, México. E-mail: [email protected].
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.
Keywords: Consensus measure, fuzzy distance, aggregation, decision making
