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Neutrosophic Sets and Systems

Abstract

This article explores the potential of Andean epistemology—rooted in principles of complementarity (yanantin), interdependence, and indeterminacy—to enrich contemporary science and artificial intelligence (AI). By transcending binary logic through concepts like ch’ixi (simultaneous coexistence of opposites) and yanantin, Andean thought aligns with Florentin Smarandache’s neutrosophic logic, a system that formalizes truth (T), falsehood (F), and indeterminacy (I) to navigate ambiguity. Smarandache’s MultiAlism, which emphasizes the coexistence of multiple perspectives, further resonates with Andean cosmovision, offering a framework to reinterpret mythology and folklore beyond rigid dualities. Integrating these principles into AI could foster systems that better handle uncertainty, cultural context, and ethical complexity. However, current models like GPT-4 exhibit critical limitations: biases toward overestimating truth values (T) and artificially clustering indeterminacy (I ≈ 0.7), flattening nuanced concepts such as yanantin and ch’ixi. Additionally, the low semantic similarity (cosine = 0.17) between AI-generated justifications and original Andean texts reveals gaps in replicating cultural terminology and relational reasoning, underscoring the need for epistemologically grounded AI design. Future work should prioritize expanding datasets with contextualized examples—including regional variations, ritual practices, and neutrosophic scales (T, I, F)—employing advanced language models aligned with Andean epistemological annotations and integrating hybrid post processing strategies such as bilingual (Quechua/Spanish) fine-tuning and expert collaboration for culturally and technically rigorous validation. By bridging Andean philosophy with Smarandache’s neutrosophic frameworks, this research advocates for culturally inclusive AI capable of honoring pluralistic worldviews, fostering equitable knowledge systems, and advancing technologies that serve humanity and the planet holistically

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