Enhancing Efficiency in Fuzzy ANP-Based Intelligent Systems: A Python Framework and the Principle of Stakeholder Representativeness

Implementing complex Multi-Criteria Group Decision-Making (MCGDM) models like the Fuzzy Analytic Network Process (FANP)—a key methodology within the python-based soft computing paradigm—presents inherent computational complexities, particularly regarding the attainment of structural stability and steady-state in high-order limit supermatrices. To overcome these obstacles, this study introduces a novel Python-based computational intelligence framework that automates and enhances the entire FANP workflow. This framework facilitates computational reliability by identifying the invariance of relative priorities within high-order supermatrices, a process that can be computationally intensive through manual procedures. Through four distinct FANP case studies, we validate the framework's superior efficiency and reliability. Our findings reveal an alternative principle for designing expert-driven intelligent systems. We empirically demonstrate that model stability—characterized by the structural alignment of both DEMATEL causal relationships and final FANP rankings—is achieved more effectively with a smaller, strategically diverse expert panel. This research suggests that integrating strategic stakeholder representativeness into the selection process provides a robust pathway toward structural consensus, offering a valuable complement to broad-scale data collection in complex decision environments.