A dual-driven MAGDM method based on single-valued neutrosophic credibility numbers Einstein variable extended power geometric aggregation operator and SPA-MARCOS

Traditional multi-attribute group decision-making (MAGDM) methods primarily rely on expert knowledge-driven information, often overlooking the value of objective data in decision-making processes. To address this gap, this paper proposes a novel dual-driven MAGDM method that incorporates knowledge-driven information, expressed through single-valued neutrosophic credibility numbers (SvNCNs), and data-driven information, represented by exact numbers (ENs). The primary innovations of this method include the development of the variable extended power geometric (VEPG) operator, which effectively aggregates knowledge-driven information from SvNCNs. The SvNCN Einstein variable extended power geometric (SvNCNEVEPG) operator is also introduced, and its mathematical properties are rigorously proven, offering an advanced approach to handling extreme values. To resolve ambiguity and uncertainty in decision analysis, a new subjective weight determination method, SvNCN-PIPRECIA, is introduced, complemented by an objective entropy-based weighting method. These are integrated into a new combined weight determination model using the Uninorm operator, enhancing the accuracy and reliability of the decision-making process. The dual-driven MAGDM method, combining knowledge-driven and data-driven information, improves decision-making comprehensiveness and precision through the dual-driven SPA-Entropy-PIPRECIA-MARCOS approach. The proposed methodology is validated through a case study on evaluating data trading platforms (DTPs), where sensitivity analysis of parameters and a comparative study with existing methods demonstrate the flexibility and scientific robustness of the approach.