INDIAN KNOWLEDGE SYSTEMS AND FUZZY MATHEMATICS: PHILOSOPHICAL PERSPECTIVES AND PRACTICAL APPLICATIONS

Abstract

This study investigates the theoretical and practical synergy between Indian Knowledge Systems (IKS) and fuzzy mathematics for more nuanced handling of uncertainty. Drawing upon philosophical concepts such as Anekāntavāda (the doctrine of multiple viewpoints), Syādvāda (conditional assertions), and Nyāya (logical inference), the research demonstrates how partial truths and context dependent knowledge can be formalized within fuzzy set theory. A multi-perspective framework is proposed, leveraging extended membership functions and specialized aggregation operators to incorporate these Indian philosophical principles. Two case studies-complex decision-making (e.g., supply chain risk assessment) and natural language processing (guided by Panini's linguistic insights) illustrate the advantages of combining IKS-inspired approaches with fuzzy inference systems. Empirical comparisons reveal higher interpretability and improved handling of contradictory or incomplete data compared to classical fuzzy models. Challenges include avoiding oversimplification of deep philosophical ideas and ensuring computational feasibility in high-dimensional domains. The findings suggest that an IKS-fuzzy integration not only broadens the conceptual scope of fuzzy logic but also offers a culturally and philosophically enriched method for addressing real-world uncertainty in AI, social sciences, and beyond.