Scope–Invariance Geometry (SIG)
A formal structural principles for analyzing artificial intelligence, economic systems, and institutional transformation.
What Is the Framework?
Scope–Invariance Geometry (SIG) is a formal structural framework for classifying transformations in terms of scope, invariants, and semantic regime boundaries. Rather than predicting outcomes, SIG identifies structurally admissible configurations and necessary limits under scaling conditions. It is designed to analyze systems where coordination density, operator compression, and cross-layer feedback produce structural transitions.
Core Structural Dimensions
The framework evaluates systems along several structural axes:
• Scope Levels — the scale at which transformation operates
• Operator Cardinality — distribution and compression of decision authority
• Semantic Boundaries — limits of structurally admissible transformation
• Regime Stability — structural drift, border conditions, and failure modes
These dimensions form a consistent classification schema for evaluating AI architectures and institutional systems.
Why Structural Analysis?
Most discourse around artificial intelligence focuses on capabilities or forecasts. Structural analysis instead examines invariants and constraints. As systems scale, certain transitions become necessary while others become structurally impossible. The framework provides tools to distinguish narrative claims from structurally admissible configurations.
From Framework to Engine
The Structural Analysis Engine operationalizes these principles. It translates structural geometry into systematic classification outputs, enabling evaluation of AI architectures, institutional proposals, and systemic trajectories.
Public release is currently in development.
