Decision algorithm with fuzzy framework and evaluation of advanced financial management policy

Area of Science:

• Decision Sciences
• Financial Management
• Fuzzy Set Theory

Background:

• Financial policy assessment often involves uncertainty, hesitation, and vagueness.
• Existing methods may struggle to aggregate diverse expert opinions effectively.
• Picture fuzzy sets and Heronian mean offer potential for improved uncertainty handling.

Purpose of the Study:

• To introduce a novel decision-making algorithm, the circular picture fuzzy heronian mean (C-PFHM) operator.
• To develop a multi-criteria decision-making framework for assessing financial management policies under uncertainty.
• To combine the strengths of picture fuzzy sets and Heronian mean for expert opinion aggregation.

Main Methods:

• Development of the C-PFHM operator integrating picture fuzzy sets and Heronian mean.
• Construction of a decision-making framework involving data collection, normalization, C-PFHM aggregation, defuzzification, and ranking.
• Application of the framework to a financial case study evaluating policy options against key performance indicators.

Main Results:

• The proposed C-PFHM algorithm demonstrated effective assessment of financial policies under uncertainty.
• A high correlation (Spearman's rho = 0.9815) was observed with reference methods (C-PFHM-TOPSIS).
• The algorithm proved reliable in high-fidelity decision-making environments.

Conclusions:

• The C-PFHM operator provides a robust tool for sophisticated financial decision-making.
• The developed framework enhances the management of uncertain and vague expert information.
• The approach offers a reliable and validated method for financial policy evaluation.