TAG ARCHIVE
risk-management
4 MARIA OS blog articles tagged risk-management, 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.
Confidence-Evidence Coupling for Agentic Governance: A Calibration Law for Safer Decisions
Couple confidence outputs to evidence sufficiency and contradiction pressure to reduce silent high-certainty failures
The coupling law ties confidence to evidence quality and provenance, improving escalation precision under uncertainty.
Actor-Critic Reinforcement Learning for Gated Autonomy: PPO-Based Policy Optimization Under Responsibility Constraints
How Proximal Policy Optimization enables medium-risk task automation while respecting human approval gates
Gated autonomy requires reinforcement learning that respects responsibility boundaries. This paper positions actor-critic methods — specifically PPO — as a core algorithm in the Control Layer, showing how the actor learns policies, the critic estimates state value, and responsibility gates constrain the action space dynamically. We derive a gate-constrained policy-gradient formulation, analyze PPO clipping behavior under trust-region constraints, and model human-in-the-loop approval as part of environment dynamics.
AML Detection Gate Optimization: Constrained Loss Minimization for Anti-Money Laundering
Formalizing gate strength as a continuous control variable to minimize the combined cost of false positives, missed detections, and investigation delay in AML compliance pipelines
AML programs face a costly tradeoff between false positives, missed detections, and investigation delay. This paper formalizes AML detection as constrained loss minimization over gate strength `g` and treats the benchmark numbers as synthetic scenario outputs, not as universal regulatory thresholds or turnkey compliance claims. The practical value of the article is in the control framework, escalation logic, and risk-based calibration structure.
Safety-First Minimax Production: Optimizing Throughput Under Hard Safety Constraints
Minimizing safety risk subject to throughput maximization constraints using minimax optimization and responsibility-gated production decisions
Manufacturing throughput and worker safety are often treated as competing objectives. This paper introduces a minimax formulation that prioritizes worst-case safety risk minimization subject to throughput-floor guarantees. The Lagrangian dual form yields gate-threshold rules for production decisions, and MARIA OS responsibility gates enforce hard safety overrides at each node. In an automotive assembly-line simulation, the framework reported 99.7% safety compliance with a 3.2% throughput reduction versus unconstrained production.