Methodology
Operational application of Scope–Invariance Geometry (SIG)
Structural AI applies Scope–Invariance Geometry through a formal classification procedure. The objective is not to predict outcomes but to determine structural admissibility, regime boundaries, and invariant constraints under scaling conditions.
Analytical Procedure
Structural evaluation follows a consistent sequence:
Scope Identification
Determine the scale at which transformation is claimed to operate (local, systemic, cross-regime).
Operator Mapping
Identify decision authority distribution, compression patterns, and coordination density shifts.
Semantic Boundary Detection
Evaluate whether proposed transformations remain within structurally admissible regime limits.
Regime Classification
Classify the system into stable, border, or drift-prone structural configurations.
Admissibility Assessment
Determine whether the claim or architecture is structurally consistent under scaling conditions.
Classification Outputs
Structural analysis produces formal descriptors rather than narrative conclusions. Typical outputs include:
• Scope Level Classification
• Operator Cardinality Assessment
• Border or Drift Indicators
• Structural Consistency Determination
• Regime Stability Evaluation
These outputs form a repeatable classification schema.
Constraints and Non-Goals
The methodology does not attempt to forecast specific technological milestones or predict market outcomes. Its function is structural evaluation — identifying invariant constraints and necessary boundary conditions that apply regardless of narrative framing.
From Methodology to Engine
The Structural Analysis Engine implements this methodology in systematic form. It translates the procedural steps into structured inputs and classification outputs, enabling consistent evaluation of AI architectures and institutional claims.
Public release is currently in development.
