TAG ARCHIVE
decision-graph
4 MARIA OS blog articles tagged decision-graph, 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.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Autonomous Industrial Holding: A Decision-Structured Architecture for Capital x Physical x Ethical Enterprise Control
How MARIA OS transforms the traditional holding company into a self-monitoring, fail-closed enterprise organism that simultaneously governs capital allocation, physical operations, and ethical compliance
The traditional holding company governs capital. The traditional manufacturer governs machines. The traditional compliance department governs ethics. None of them govern all three simultaneously, and this separation is the structural origin of every corporate catastrophe where financial optimization overrides physical safety or ethical constraint. This paper introduces the Autonomous Industrial Holding — a decision-structured architecture built on MARIA OS that unifies capital allocation, physical-world operations, and ethical governance into a single fail-closed organism. We formalize the holding state as the Cartesian product of independent Universe states, derive a six-step Capital-Physical Circulation Loop as a discrete dynamical system with Lyapunov stability guarantees, prove convergence conditions for the capital-physical-ethics feedback cycle, and present a five-year evolution scenario from initial deployment to full self-monitoring, self-optimizing operation.
Agentic Ethics Lab: Designing a Corporate Research Institute for Structural Ethics in AI Governance
A four-division, gate-governed research architecture that transforms ethics from philosophical declaration into executable, auditable, and evolvable system infrastructure
Ethics declarations without structural enforcement are organizational theater. This paper presents the Agentic Ethics Lab — a corporate research institute embedded within the MARIA OS governance architecture, operating as a first-class Universe with four specialized divisions: Ethics Formalization, Ethical Learning, Agentic Company Design, and Governance & Adoption. Each division runs agent-human hybrid teams under fail-closed research gates. We formalize the lab's architecture using decision graph theory, prove that self-referential governance research preserves safety invariants, and demonstrate that a corporate research institute with no revenue targets but strategic alignment outperforms both pure academic and pure product research in responsible AI advancement.
Investment Decision Lab: Designing Agentic R&D Teams for Multi-Universe Capital Allocation
A fail-closed, conflict-aware research architecture that transforms investment decisions from single-metric optimization into multi-universe responsibility-governed capital deployment
Capital allocation without structural governance is organizational gambling. This paper presents the Investment Decision Lab — an agentic R&D institute embedded within the MARIA OS governance architecture, operating as a first-class Universe with two specialized teams: Multi-Universe Investment Core Lab (Team I-A) and Capital Allocation & Simulation Lab (Team I-B). Each team runs agent-human hybrid research under a four-level investment gate policy (RG-I0 through RG-I3) with fail-closed capital deployment. We formalize multi-universe investment scoring using min-gate aggregation, derive conflict-aware portfolio optimization under multi-objective constraints, prove Monte Carlo convergence for sandbox venture simulation, and introduce the Investment Philosophy Drift Dashboard. The result is an investment infrastructure where no capital moves without passing through responsibility gates — and where human judgment governs every deployment decision.
A Formal Model of Responsibility Decomposition Points in Human-AI Decision Systems
Why responsibility is a computable threshold, not a philosophical debate - and how to implement it
Existing AI governance frameworks rely on qualitative guidelines to determine when human oversight is required. This paper formalizes responsibility decomposition as a quantitative threshold problem: we define a Responsibility Demand Function R(d) over decision nodes using five normalized factors - impact, uncertainty, externality, accountability, and novelty - and introduce a decomposition threshold τ that determines when human responsibility must be enforced. A dynamic equilibrium model captures temporal shifts driven by learning and contextual change. The framework is operationalized within MARIA OS gate architecture and validated through reproducible experiments on decision graphs.