Structured Human Intelligence
Time turns behavior into infrastructure.
Longitudinal Behavioral Governance (LBG) defines how intelligent systems—human and artificial—adapt over time when governance is present or absent.
This repository is the canonical public reference for LBG. It explains why governance must precede agent deployment, how behavioral drift emerges without structure, and where traditional policy, ethics, and point-in-time audits fail.
- A field definition, not an implementation
- A citation anchor for governance rationale
- A theoretical foundation for standards such as HHI-GOV-01
- Not a toolkit
- Not an audit product
- Not a case study library
- Not personal narrative
Implementation, scoring, audits, and client work are governed separately.
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CANON/LBG-01.md
Longitudinal Behavioral Governance: Why Governance Must Precede Agents -
DIAGRAMS/governance-absence-progression.png
Single canonical progression model -
STANDARDS/relationship-to-HHI-GOV-01.md
Scope boundary and standards alignment
All defined terms used in this repository follow the
Hollow House Institute Governance Glossary:
→ https://github.com/hollowhouseinstitute/Hollow_House_Standards_Library/blob/main/glossary.md
Terminology is controlled. Definitions are authoritative.
This repository is published by Hollow House Institute.
All contents are governed by the HHI Master License.
See: LICENSE/HHI-Master-License.txt
LBG defines the behavioral risk surface.
HHI-GOV-01 specifies the minimum structural requirements to control it.
This repository inherits governance authority from the HHI Governance Export — Core.
All execution, datasets, research, and audits are bound to its standards and constraints.
Authority is enforced through explicit Decision Boundaries, escalation thresholds, and Stop Authority conditions.