A Neutrosophic Framework Design and Performance Evaluation for Ethical Governance Construction in Big Data–Driven Physical Education

Abstract

The ethical challenges of implementing Big Data and intelligent systems 
in physical education (PE) environments demand a robust decision-making 
paradigm capable of handling inconsistencies, indeterminacies, and conflicting 
stakeholder priorities. This paper proposes an original neutrosophic-based αDiscounting Multi-Criteria Decision-Making (α-DMCDM) model, specifically 
tailored for ethical governance in Big Data-enhanced PE systems. The model 
integrates neutrosophic logic, capturing degrees of truth (T), indeterminacy (I), 
and falsehood (F) with the α-Discounting technique to resolve strong 
inconsistency among ethical criteria.
We construct a mathematical decision model that incorporates ethical principles 
(such as privacy, fairness, transparency, and surveillance) as neutrosophic 
variables with corresponding α-discounted preference equations. The model is 
capable of resolving inconsistent ethical conflicts using parameterized matrix 
representations, eigenvalue-based prioritization, and fairness-driven 
normalization. Through a real-world case study, we demonstrate how the 
proposed framework evaluates conflicting ethical criteria using a rich set of 
analytical equations, neutrosophic matrices, and decision vectors. Several formal 
theorems, examples, and numerical validations are presented to establish 
mathematical soundness. The study contributes a novel approach to ethical 
MCDM under uncertainty, offering strong theoretical foundations for real-world 
Big Data governance challenges in educational systems.

DOI: 10.5281/zenodo.16934950