TAG ARCHIVE
co-evolution
2 MARIA OS blog articles tagged co-evolution, 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.
Human-AI Co-Evolution as a Coupled Dynamical System: Meta-Cognition Mediated Stability in Nonlinear Agent-Human Interactions
A formal dynamical-systems treatment of human-AI interaction stability and how metacognitive control helps reduce capability decay and trust instability
We model the human-AI interaction loop as a coupled dynamical system `X_t = (H_t, A_t)` and analyze stability under metacognition-mediated control through spectral-radius conditions on the coupled Jacobian. Simulations across 1,000 trajectories report 94.2% trust-band stability and 87.6% capability preservation versus uncontrolled baselines.
Human-AI Co-Evolution as a Constrained Optimal Control Problem: Designing Socially Adaptive Agentic Operating Systems
A rigorous optimal control framework for governing human-AI co-evolution under multi-objective cost functions, partial observability, and hard safety constraints
We reformulate human-AI co-evolution as a constrained optimal-control problem. By defining a multi-objective cost function over task quality, human capability preservation, trust stability, and risk suppression, and solving Bellman-style recursions under hard constraints, we characterize co-evolution policies that Meta Cognition can approximate in MARIA OS. We extend the framework to POMDP settings for partial observability of human cognitive states and derive conditions linked to long-run social stability.