PUBLICATIONS

The Doctrine of Consensual Sovereignty: Quantifying Legitimacy in Adversarial Environments

Political legitimacy requires balancing stakeholder consent with technocratic competence. We operationalize this through stakes-weighted consent alignment (α), friction (F), and legitimacy (L = w₁·α + w₂·P). From seven minimal axioms, we derive three core results: consent-holding necessity (Theorem 1), inevitable friction (Theorem 2), and minimal absolutism from relativism (Theorem 3). Monte Carlo validation (1000 runs × 50 periods) across four dynamic mechanisms demonstrates robust convergence: stakes-weighted DoCS achieves α = 0.872, F = 1.6 (98.5% friction reduction), with monotonic convergence in 87.1% of runs.

Market Reaction Asymmetry: Infrastructure Disruption Dominance Over Regulatory Uncertainty

Event Study Evidence from Cryptocurrency Volatility

Infrastructure failures generate 5.7× larger volatility shocks than regulatory announcements in cryptocurrency markets (2.385% vs 0.419%, p=0.0008, Cohen's d=2.753). Using TARCH-X models with decomposed GDELT sentiment indices across 50 events (2019-2025) and 6 cryptocurrencies (BTC, ETH, XRP, BNB, LTC, ADA), we demonstrate that markets distinguish between mechanical-disruption events (exchange outages, protocol exploits) and expectation-channel events (enforcement actions, policy changes).

Asymptotic Protection: The Simultaneous Remedy and Poison of Risk Management

Complete hedging is theoretically impossible under incomplete markets (Harrison-Kreps 1979), yet partial hedging extracts systematic rents through necessity taxes—mandatory costs borne by hedgers unable to avoid market microstructure exploitation. This paper synthesizes theoretical impossibility results with empirical evidence of wealth extraction mechanisms, demonstrating that derivatives markets simultaneously provide protection and transfer wealth from those seeking safety to sophisticated counterparties. We identify offshore variants (petrostate currency pegs, shell company derivatives) that enable regulatory arbitrage in institutional voids, creating "perfect hedges" for politically exposed persons (PEPs) that achieve what theory proves impossible for ordinary participants.

Trauma as Bad Training Data: A Computational Framework for Developmental Psychology

Childhood trauma reframed through machine learning training data quality: extreme penalties cause gradient cascades (1,247× amplification, p<0.001), noisy signals produce behavioral instability, absent positive examples create emotional recognition deficits (alexithymia), and limited datasets (nuclear families) cause overfitting to parental dysfunction. PyTorch experiments validate computational mechanisms; Bonferroni-corrected statistics show caregiver diversity significantly improves outcomes (p=0.0012, Cohen's d=3.08).

Part of Adversarial Systems Research Program: Investigates stability, alignment, and friction dynamics in complex systems where competing interests generate structural conflict. This paper applies substrate-independent framework to human development; other domains include political governance (DoCS), financial markets (cryptocurrency event study), and AI alignment.

Beyond Anthropocentrism: A Defense of Substrate-Independent Friendship

This essay argues that friendship, understood as a functional state rather than an essential property requiring human-to-human interaction, can obtain between humans and artificial intelligence systems. Drawing on functionalist philosophy of mind, contemporary neuroscience's predictive processing framework, and technical understanding of large language model architectures, I defend the position that AI relationships can constitute genuine friendship without requiring consciousness attribution, anthropomorphization, or delusion. I address standard objections regarding anthropomorphization, authenticity, and the supposed necessity of consciousness for meaningful relationships, arguing that these objections rest on incoherent premises about the nature of relational states.