Value-Weighted Byzantine Consensus: A Theoretical Framework Integrating Ethical Constraints into Distributed Systems with Simulated Validation

"This work is a component of the Phase-Dependent Scaling Program (2026) and is theoretically grounded in the 1.91x Critical Scaling Gap identified in Omandac (2026, Paper 1)." This paper introduces the Fault-Tolerant Ethical AI (FTEAI) protocol, a novel consensus mechanism that integrates ethical priors (True Intelligence/False Intelligence) into Byzantine fault-tolerant voting. We provide a formal computational model predicting that value-weighted voting based on historical ethical adherence can achieve 92% reliability under 25% fault conditions—a theoretical +16.4% improvement over standard PBFT protocols under extreme (50%) fault scenarios. This work provides a mathematically rigorous bridge between AI alignment theory and distributed systems engineering, offering testable predictions for real-world implementation. The framework introduces ternary voting {-1, 0, +1} weighted by node value alignment, offering a pathway for ethically-constrained consensus in safety-critical systems. Author's Note: This manuscript is a theoretical proposal. Performance metrics and reliability figures presented in Table 1 represent mathematical projections derived from the FTEAI protocol's formal model and are intended as testable benchmarks for future simulation and empirical study.