Analyzing Decision‐Making Processes Using the Energy of Bipolar Neutrosophic Soft Sets

Bipolar neutrosophic soft sets are powerful tools for modeling data under conditions of uncertainty and imprecision due to their rich parametric structure and the useful mathematical properties of the operations defined on them. In this paper, motivated by the limitations of existing decision‐making algorithms, we introduce a new numerical characteristic, the energy of a bipolar neutrosophic soft set defined using singular values, analogous to the graph energy and nuclear norm. Our goal is to develop an efficient decision‐making algorithm that successfully identifies the optimal alternative even in cases where other algorithms provide inaccurate or inconsistent results. Our research is motivated by the need for more reliable decision‐making methods in complex soft environments and the potential of the energy‐based approach to overcome the weaknesses of existing methods, which we demonstrate through a comparative analysis using concrete examples.