Published Papers

Trust After Thinking Machines follows a simple observation to its uncomfortable end: once institutions can 'think' at scale, they stop treating intelligence as a scarce human resource and start treating judgment as a renewable utility. Automation arrives quietly—as triage screens, risk scores, routing queues, ranking systems, and policy engines—reshaping who gets served, flagged, priced, or denied. The book argues this is not merely a technical shift but a shift in authority itself. The core crisis is not that automated decisions are sometimes wrong; it is that they become unanswerable. Responsibility diffuses across vendors, models, policies, and committees until no one can say with evidence who decided, why, and who owns the outcome. The book builds a practical theory of accountable authority for the agentic era, proposing three enforceable properties for legitimate machine-mediated judgment: boundedness, meaningful contestability, and identifiable responsibility—translated into infrastructure like decision provenance records, reviewable evidence trails, and disagreement architectures.

This work treats trust as a coordination capability—the practical ability of people and institutions to commit resources and act under uncertainty without intolerable exposure to betrayal or opportunism. It traces the historical technologies that made trust scalable—oath and witness, record and archive, bureaucracy, metrics, and platforms—showing how each step increases reach while weakening the feedback loops that keep authority answerable. The central claim is that automation has crossed a threshold from assistance to governance. When computational systems deny, prioritize, rank, gate, route, or allocate at scale, they become synthetic authority—authority that binds without a clearly legible author. The work introduces two vectors of agency (infrastructural and intimate), defines a legitimacy standard for automated authority built on operationally affordable disagreement, and proposes hard requirements around boundedness, contestability, identifiable responsibility, and reversibility.

This paper introduces the Supervisory Coherence Model (SCM), a framework linking autonomy anxiety, the undo contract, negotiated interaction grammar, interruption budgeting, and episodic interface architecture as a causal system governing human–agent collaboration. The SCM addresses the structural break in user experience created by agentic AI systems and proposes design primitives—reversibility, provenance, progressive disclosure, risk-tiered approvals, and escalation routing—that maintain supervision under autonomy.

Applies Ken Wilber's Integral Theory to UX design and personalization systems, providing a framework for understanding user experience across multiple dimensions of consciousness and development. This work informs the design of agent systems and product experiences that account for human complexity.

Examines how social capital operates in digital communities and internet-based 'clubs', exploring the dynamics of connection and trust in online spaces.

Introduces the ΨC (Psi-Coherence) Principle, a computational framework for modeling reflective intelligence as a dynamical system driven by entropy-aware coherence accumulation. The framework is structured around five interconnected mechanisms—Entropy-Governed Coherence Accumulation, Contradiction-Driven Reflection, Adaptive Bias Correction, Active Information Seeking, and Networked Coherence—that collectively enable stable, adaptive, reflective coherence under bounded information and memory constraints. Formal convergence to coherence-optimal states is proven with sublinear regret bounds, yielding falsifiable predictions including sigmoidal phase transitions in reflective behavior.