FARZULLA RESEARCH
Quantifying Legitimacy, Modeling Volatility, Provoking Discourse
This research program investigates stability, alignment, and friction dynamics in complex systems where competing interests generate structural conflict.
Our work treats diverse domains—political governance, financial markets, human development, multi-agent AI—as adversarial environments where optimal outcomes require balancing competing interests rather than eliminating conflict. This framework formalizes relationships between stakes, voice, and friction, applicable to algorithmic governance, climate negotiations, autonomous agents, and any system where consent structures remain undefined but friction dynamics are observable.
Computational research conducted at Resurrexi Labs, our distributed computing division specializing in autonomous systems, offensive security, and large-scale computational experiments.
Research Programs
Computational Finance & Risk
Volatility modeling, infrastructure vs regulatory shock asymmetry, network contagion analysis
AI Alignment & Cognitive Science
Training data quality frameworks, autonomous multi-agent systems, adversarial competition dynamics
Institutional Mechanics
Stakes-weighted consent mechanisms, legitimacy quantification, algorithmic governance frameworks
Recent Publications
The Doctrine of Consensual Sovereignty: Quantifying Legitimacy in Adversarial Environments
Operationalizes political legitimacy through stakes-weighted consent alignment. Monte Carlo validation demonstrates robust convergence across four dynamic mechanisms.
Market Reaction Asymmetry: Infrastructure Disruption Dominance Over Regulatory Uncertainty
Infrastructure failures generate 5.7× larger volatility shocks than regulatory announcements in cryptocurrency markets. TARCH-X models demonstrate markets distinguish mechanical disruption from expectation channels.
Gradient Descent Framework: Trauma as Adversarial Training Conditions
Computational reframing of developmental psychology through machine learning training data lens. PyTorch experiments demonstrate 1,247× gradient cascade from extreme penalties.
Resources
Data & Tools
Open-source datasets, computational tools, replication packages
Methodologies
Research methods, computational frameworks, validation approaches
GitHub
Code repositories, simulation engines, autonomous agent frameworks
Resurrexi Labs
Computational research division, distributed infrastructure, offensive security research