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Article Type

Research Article

Abstract

Fuzzy reasoning and neutrosophic reasoning are both used to handle uncertainty, but they are not intended for the same uncertainty structure. Fuzzy reasoning is suitable when uncertainty appears mainly as gradual vagueness, where a value may belong to a concept such as ``high risk'' or ``good performance'' to a certain degree. In this case, a membership value is often sufficient. Neutrosophic reasoning is more suitable when the problem also contains incomplete information, undecided evidence, or conflict between sources. In such cases, one membership degree may be too limited because it cannot represent support, rejection, and indeterminacy separately. This study introduces a Fuzzy–Neutrosophic Suitability Index (FNSI) for selecting an appropriate reasoning model according to the structure of uncertainty in the problem. The index is built from three normalized uncertainty descriptors: vagueness G( x ), incompleteness H( x ), and conflict Q( x ). The proposed rule selects fuzzy reasoning when vagueness is the dominant source of uncertainty, selects neutrosophic reasoning when incompleteness or conflict becomes dominant, and identifies a transitional region for borderline cases. In addition, the study presents the mathematical form of the index, proves its boundedness and monotonic behavior, and gives numerical examples that show how the rule works in different uncertainty settings. The contribution of the study is a structured selection mechanism rather than another fuzzy or neutrosophic variant. The proposed model does not replace fuzzy or neutrosophic reasoning. Instead, it helps clarify when fuzzy reasoning is sufficient and when neutrosophic reasoning is more appropriate. It also reduces arbitrary model selection by linking the choice of reasoning model to explicit uncertainty descriptors.

Keywords

Fuzzy reasoning, Neutrosophic reasoning, Vagueness, Incompleteness, Conflict, Suitability index, Uncertainty modeling, Reasoning model selection

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