TAG ARCHIVE
graph-theory
2 MARIA OS blog articles tagged graph-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.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Team Design Topology: Practical Team Shapes for Throughput, Traceability, and Escalation Control
A design-oriented model for choosing between flat pools, meshes, and review cells
Enterprise agent teams should not be organized by analogy to human org charts alone. This article treats team shape as a controllable systems variable and compares flat pools, dense meshes, and hierarchical review cells using a stylized throughput model. The goal is not to derive a universal theorem, but to give operators a practical way to trade off speed, reviewer load, and responsibility traceability.
Agentic Company Structural Design: Responsibility Topology, Conflict-Driven Learning, and Self-Evolving Governance for Human-Agent Organizations
Modeling the enterprise as a responsibility topology across human-agent decision nodes
This paper explores corporate design where the primary unit is the decision node and its responsibility allocation, not only role or department labels. It introduces five linked research programs that model the enterprise as a weighted directed responsibility graph whose topology evolves through conflict-driven learning. We formalize human-agent responsibility matrices, derive scalable topology conditions, define health metrics for hybrid organizations, and model governance as a self-evolving decision graph with gate-managed policy transitions.