TAG ARCHIVE

organizational-structure

1 MARIA OS blog articles tagged organizational-structure, organized as a Bonginkan topic archive for search engines and LLM retrieval.

1 article|Published by Bonginkan

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.

IntelligenceFebruary 14, 202630 min read

Random Forest for Interpretable Organizational Decision Trees: Extracting Governance Logic from Ensemble Structure

How bagging-based tree ensembles reveal decision-branch structure, critical governance variables, and auditable policy trees

While gradient boosting often targets predictive accuracy, random forests provide a complementary strength: structural interpretability. This paper positions random forests as an interpretability engine within the Decision Layer (Layer 2), showing how ensemble structure surfaces governance logic, highlights key variables through permutation/impurity importance, and yields auditable policy trees. In evaluated workloads, random-forest feature importance reached 0.93 rank correlation with domain-expert rankings, extracted trees matched 89% of documented governance policies, and out-of-bag error supported validation in data-constrained settings.

random-forestdecision-treeinterpretabilityfeature-importanceorganizational-structurevariable-extractionexplainable-AIagentic-companygovernanceMARIA OS