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.
CEO OSの意思決定力学 — 判断を数理で捕捉する5軸アーキテクチャ
経営認知を5次元意思決定空間 X = (L, D, G, I, R) として形式化し、判断重力・判断慣性・レイヤー整合の物理学で組織判断をスケールさせるCEO OSの完全設計論
判断はスケールしない。実行はスケールする。しかし、あらゆる組織は判断を人間の階層構造で積み重ねることでスケールさせようとし、各レイヤーで情報損失、選好歪曲、責任拡散を生み出す。CEO OSは組織判断を分類問題ではなく物理学の問題として扱う——重力、慣性、レイヤー、場を持つ力学系として。本論文は完全な意思決定力学の形式化を提示する：認知深度、ドメイン特化、判断重力、組織慣性、責任境界を捕捉する5軸意思決定空間 X = (L, D, G, I, R)。300問のベイズ推定型引き出しプロトコル、破滅的レイヤー不一致を防止するレイヤー整合アルゴリズム、モンテカルロシナリオ分析による反事実シミュレーションエンジンを導入する。本アーキテクチャは自己キャリブレーション型・ドリフト耐性の意思決定オペレーティングシステムを生成し、8.4倍の委任スループットと94.7%の判断忠実度を実現する。
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.