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Human attitude analysis based on fuzzy soft differential equations with Bonferroni mean

In this paper, we introduce a system of fuzzy soft differential equations to analyze how an individual’s behavior evolves over time based on their companion’s actions in specific situations and decisions. To address the interrelationships among arguments in group decision-making problems, we utilize the Bonferroni mean (BM), which effectively captures these connections. Leveraging the capabilities of BM, we define the Bonferroni fuzzy soft matrix (BFSM) and the weighted Bonferroni fuzzy soft matrix (WBFSM) for representing data. WBFSM serves as a decision matrix and determines the optimum fuzzy soft constant (OFSC), a crucial component in fuzzy soft differential equations. By incorporating the OFSC, we develop a system of fuzzy soft differential equations that can study dynamic processes involving nonlinear and uncertain data. Additionally, we present an innovative and efficient technique for analyzing individuals’ future attitudes based on their present decisions. To demonstrate the practicality and feasibility of our proposed technique, we provide an illustrative example accompanied by phase portraits and line graphs.

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