Formalizing the Diversity of Latin American Perspectives: A Neutrosophic Framework for Multiperspectivism

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Maikel Y. Leyva Vázquez
Florentino Smarandache

Abstract

This article addresses the challenge of formalizing the complex and often contradictory knowledge systems present in Latin American decolonial thought by introducing Neutrosophic as a conceptual framework. Multiperspectivism is presented as a tool to operationalize philosophical perspectivism through neutrosophic logic. The study assumes that knowledge is situated and that truth depends on the observer's viewpoint—a concept inspired by Nietzschean philosophy but with unique manifestations in Amerindian cosmologies and pluralistic legal systems. The central methodology involves developing a "Neutrosophic Multiperspectivism Model," which employs Multi-Neutrosophic Sets to represent subjective perspectives as triplets of truth, indeterminacy, and falsity ⟨V, I, F⟩. Key contributions include a similarity function that quantifies the affinity between different perspectives, transforming qualitative information into data suitable for formal analysis. The model is illustrated through a case study on a land dispute, enabling the identification of underlying structural similarities, such as absolutist reasoning patterns shared by opposing parties, and the recognition of potential strategic alliances. The main conclusion is that neutrosophic logic provides a robust mathematical framework for managing ambiguity and plurality in perspectivist contexts, moving beyond mere acceptance of diversity to offer a formal and potentially integrative analysis of differing perspectives.

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Formalizing the Diversity of Latin American Perspectives: A Neutrosophic Framework for Multiperspectivism. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 39, 70-94. https://fs.unm.edu/NCML2/index.php/112/article/view/836
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How to Cite

Formalizing the Diversity of Latin American Perspectives: A Neutrosophic Framework for Multiperspectivism. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 39, 70-94. https://fs.unm.edu/NCML2/index.php/112/article/view/836