TAG ARCHIVE
decision-theory
3 MARIA OS blog articles tagged decision-theory, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
CEO OS Decision Mechanics — A Five-Axis Architecture for Capturing Judgment Mathematically
A complete design theory of CEO OS that formalizes executive cognition as a five-dimensional decision space X = (L, D, S, I, R) and scales organizational judgment through severity scoring, decision inertia, and layer alignment
Judgment does not scale. Execution does. Yet every organization attempts to scale judgment by stacking it through human hierarchies, producing information loss, preference distortion, and responsibility diffusion at every layer. CEO OS treats organizational judgment as a governed classification and escalation problem. This paper presents a five-axis decision space X = (L, D, S, I, R) that captures cognitive depth, domain specialization, decision severity, organizational inertia, and responsibility boundaries. We introduce a 300-question elicitation protocol, a layer alignment algorithm that prevents catastrophic layer mismatch, and a counterfactual simulation engine driven by scenario analysis. The architecture produces a self-calibrating, drift-resistant decision operating system that achieves 8.4x delegation throughput and 94.7% judgment fidelity.
Quantifying Responsibility Transfer: Does Automation Actually Reduce Responsibility?
A formal model showing why AI adoption can create an illusion of reduced responsibility while outcome responsibility remains conserved
When organizations automate decisions, responsibility is often perceived as reduced. This paper separates execution responsibility from outcome responsibility, defines a formal transfer quantity `T(h->a)`, and derives a conservation result showing that total outcome responsibility stays in the human domain even as execution is automated.
Formalizing Reversibility: A Risk Differential Analysis of Reversible vs Irreversible Decisions
A continuous-valued framework for measuring decision reversibility and calibrating governance accordingly
Not all decisions carry equal risk; reversibility is a key differentiator. A reversible pricing change and irreversible contract execution have distinct risk profiles, yet many governance systems handle them similarly. This paper defines a continuous reversibility function Rev(d) in [0,1], derives risk-amplification behavior for low-reversibility decisions, and shows why optimal gate strength is inversely related to reversibility. In reported deployments, reversibility-aware gating achieved 41% lower realized risk with 22% fewer human escalations.