<![CDATA[This paper explores the use of fuzzy set theory to model the behavior of voters in a multi-agent electoralenvironment. Voters, represented as fuzzy agents, communicate using imprecise language to formcommunities based on shared linguistic terms. By leveraging graph theory, we construct a model of afuzzy voting system where agents are linked based on the similarity of their fuzzy language. Theproposed approach focuses on identifying, constructing, and extracting communities of fuzzy voterswithout delving into their relational dynamics. Using fuzzy set membership functions, we definelinguistic variables that reflect the imprecision in voter behavior. The study introduces an algorithm todetect communities by creating links between fuzzy voters, ultimately forming groups based on theirlinguistic similarities. Results demonstrate that fuzzy communities can be successfully constructed,where the membership function quantifies the degree of belonging of voters to specific communities.This method contributes to a better understanding of voting behavior in complex, heterogeneous systemsand offers a novel approach to community detection in multi-agent systems]]>
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