TAG ARCHIVE
gate-design
2 MARIA OS blog articles tagged gate-design, 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.
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.
MAX vs Average Scoring: A Mathematical Analysis of Fail-Closed Gate Design
Why average-score gates structurally fail and how MAX-based scoring achieves zero false-acceptance under defined conditions
Average-score gating can dilute critical risk signals by construction. For example, a low score in one domain may mask a high score in another under arithmetic averaging. This paper analyzes why MAX-based scoring removes that masking effect in fail-closed designs, and reports zero false acceptance under the stated conditions in evaluated datasets.
The Lagrange Problem of Gate Optimization: Finding the Optimal Point Between Safety and Speed
Constrained optimization of governance gates using Lagrange multipliers and KKT conditions
Every governance gate imposes two costs: the cost of errors it fails to catch (misjudgment cost) and the cost of delays it introduces (latency cost). These costs move in opposite directions. Stronger gates catch more errors but delay more decisions. This paper formulates the tradeoff as a constrained optimization problem, derives optimal gate strength per risk tier using Lagrange multipliers, and provides closed-form solutions under practical assumptions.