TAG ARCHIVE
optimization
3 MARIA OS blog articles tagged optimization. Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation. This canonical topic archive supports 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.
Multi-Universe Strategic Optimization: Minimax Theory for CEO Decision Systems
Worst-case utility optimization across parallel business universes and its implementation in MARIA OS
CEO decisions are multi-objective: each strategy affects Finance, Market, HR, and Regulatory universes with partially conflicting goals. This paper formalizes the problem as a minimax game over universe-utility vectors, derives `StrategyScore S = min_i U_i` as a robust objective candidate, constructs conflict matrices from inter-universe correlations, and characterizes a computable Pareto frontier. We connect the framework to MARIA OS MAX-gate design and report simulation results where minimax-oriented policies improved worst-case outcomes by 34% versus weighted-average baselines while retaining 91% of best-case upside.
Fail-Closed Gate Design for Agent Governance: Responsibility Decomposition and Optimal Human Escalation
Responsibility decomposition-point control for enterprise AI agents
When an AI agent modifies production code, calls external APIs, or alters contracts, responsibility boundaries must remain explicit. This paper formalizes fail-closed gates as a core architectural primitive for responsibility decomposition in multi-agent systems. We derive gate configurations via constrained optimization and use internal simulations to illustrate how a 30/70 human-agent ratio can preserve responsibility coverage while reducing decision latency versus full human review.
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.