📡 Media Substrate Model Analyzer#

The Media Substrate Model (MSM) Analyzer interprets the structural behavior of media ecosystems using the MSM substrate: vectors, invariants, basins, modes, drift, and transitions. It provides a consistent way to evaluate how attention, narrative, signal integrity, distribution topology, and temporal cadence interact to produce stability, fragmentation, cascades, or reconstruction.

The Analyzer does not interpret content or ideology. It evaluates structure, energy, and coherence across the five MSM axes.

🧭 Purpose and Orientation#

Media ecosystems behave according to their own physics. Attention surges, narrative decay, cadence acceleration, and distribution fragmentation create predictable structural patterns. The MSM Analyzer captures these patterns by:

• Converting adapter output into normalized MSM vectors
• Evaluating invariant strain
• Classifying basin membership
• Determining behavioral mode
• Measuring drift across time
• Detecting transitions and their triggers

The Analyzer is substrate‑agnostic: any media environment—social platforms, news flows, narrative corpora, or synthetic simulations—can be evaluated using the same structural grammar.

📐 The MSM Vector#

All analysis begins with the five‑axis MediaVector:

[S, D, A, N, T]

• S — Signal Integrity
• D — Distribution Topology
• A — Attention Dynamics
• N — Narrative Coherence
• T — Temporal Cadence

Each axis is normalized to [0.0, 1.0].
The Analyzer treats the vector as a structural fingerprint of the media state at a given moment.

🧩 Invariant Evaluation#

The Analyzer computes strain across MSM’s four core invariants:

• Signal–Narrative Coherence
• Distribution–Attention Fit
• Temporal–Signal Stability
• Attention–Narrative Feedback

Each invariant returns a strain value from 0.0 (aligned) to 1.0 (broken).
Invariant strain determines whether the system is stable, drifting, or approaching cascade conditions.

🌀 Basin Classification#

The Analyzer compares the vector to MSM’s six canonical basins:

• Broadcast
• Network
• Fragment
• Cascade
• Stagnation
• Reconstruction

Classification uses:

• Distance to canonical vectors
• Gate conditions
• Invariant strain patterns

If no basin’s gates are satisfied, the system is classified as Unstable / Transitional.

🎛 Behavioral Mode#

Modes describe how the system behaves inside a basin:

• Stable
• Tension
• Drift
• Cascade
• Collapse
• Reconstruction

Mode determination uses invariant strain, drift magnitude, cadence pressure, and attention volatility.
Modes reveal whether the system is absorbing pressure, destabilizing, or rebuilding.

🧬 Drift Detection#

Drift measures directional movement across the substrate:

• Δ vector
• Drift magnitude
• Category: micro, meso, macro, regime_shift

Drift is the primary indicator of transitions between basins or modes.

🔄 Transition Detection#

Transitions occur when the system crosses basin or mode boundaries.
Triggers include:

• Invariant breaks
• Attention spikes
• Cadence acceleration
• Signal collapse
• Narrative collapse
• Reconstruction investment

Each transition includes a severity score and a structural explanation.

🔌 Adapter Integration#

Adapters convert raw external signals into MSM vectors.
The Analyzer expects:

• A normalized MediaVector
• Optional metadata
• Optional narrative/attention/signal hints

Adapters allow the Analyzer to ingest any media environment without platform‑specific assumptions.

📚 Documentation Map#

The MSM Analyzer documentation includes:

• pipeline.md — full evaluation flow
• invariants.md — invariant computation
• basin_classification.md — attractor logic
• mode_determination.md — behavioral states
• drift_detection.md — movement across the substrate
• transition_detection.md — structural shifts
• adapter_integration.md — adapter expectations
• examples.md — worked examples
• schema.json — Analyzer I/O schema
• schema.json.md — Analyzer I/O schema web copy
• index.html — HTML canonical example site