BLADE-EDGE: A Deterministic Governance Simulation Framework for Multi-Agent Decision Systems

BURAK OKTENLI

Published: 23 Mar 2026, Last Modified: 30 Mar 2026ZenodoEveryoneRevisionsCC BY-SA 4.0
Abstract: BLADE-EDGE v5.0.3 is a deterministic governance simulation framework designed to model, analyze, and reproduce multi-agent decision systems under fully controlled execution conditions. The framework emphasizes transparency, repeatability, and interpretability by eliminating stochastic processes and relying exclusively on rule-based state transitions. The system is implemented using a React-based simulation architecture, where agent interactions, governance rules, and system states evolve through explicitly defined logic pathways. This design enables step-by-step inspection of decision flows and supports rigorous evaluation of governance mechanisms without variability introduced by randomness. BLADE-EDGE is particularly suited for exploring: Multi-agent coordination and conflict resolution Governance rule design and enforcement dynamics Deterministic analogues of AI alignment scenarios Reproducible simulation experiments in complex systems This release (v5.0.3) includes: A runnable browser-based simulation environment (HTML + JSX) Source code implementing deterministic execution logic A formal technical document describing system architecture and assumptions (PDF) Citation metadata (CITATION.cff) and reproducibility guidance Structured documentation of system components and usage The simulation environment is self-contained and can be executed locally in a standard web browser. It leverages CDN-hosted React and Babel tooling for runtime execution, minimizing setup requirements while preserving accessibility and reproducibility. The framework is intended for research, educational, and exploratory use in governance modeling, AI alignment studies, and deterministic system design. It provides a conceptual and technical foundation for studying how rule-based systems behave under controlled conditions and how governance structures influence agent-level outcomes.