📡 MSM Analyzer Pipeline
The MSM Analyzer processes media ecosystem states through a structured evaluation pipeline. Each stage transforms raw adapter output into increasingly meaningful substrate interpretations: invariants, basins, modes, drift, and transitions. The pipeline is deterministic, substrate‑agnostic, and fully aligned with the MSM’s conceptual architecture.
The Analyzer does not interpret content. It evaluates structure, energy, and coherence across the five MSM axes.
🧱 1. Input → MediaVector#
The pipeline begins with an adapter converting raw external signals into a normalized MediaVector:
[S, D, A, N, T]
- S — Signal Integrity
- D — Distribution Topology
- A — Attention Dynamics
- N — Narrative Coherence
- T — Temporal Cadence
The Analyzer expects all values in the range 0.0–1.0.
If the adapter provides additional metadata (e.g., volatility hints, narrative conflict markers), the Analyzer may use it to refine later stages.
🧬 2. Invariant Evaluation#
The Analyzer computes strain across the MSM’s four invariants:
- Signal–Narrative Coherence
- Distribution–Attention Fit
- Temporal–Signal Stability
- Attention–Narrative Feedback
Each invariant returns a strain value:
0.0 = aligned
1.0 = broken
Invariant strain is the backbone of the pipeline.
It determines whether the system is stable, drifting, or approaching cascade conditions.
🌀 3. Basin Classification#
The Analyzer compares the vector to the six MSM 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.
Basins describe where the system is located in the media substrate topology.
🎛 4. Mode Determination#
Modes describe how the system behaves inside its basin:
- Stable
- Tension
- Drift
- Cascade
- Collapse
- Reconstruction
Mode determination uses:
- Invariant strain
- Drift magnitude
- Cadence pressure
- Attention volatility
- Narrative stability
Modes reveal whether the system is absorbing pressure, destabilizing, collapsing, or rebuilding.
🧭 5. Drift Detection#
Drift measures directional movement across the substrate:
- Δ vector
- Drift magnitude
- Drift category:
- micro
- meso
- macro
- regime_shift
Drift is the primary indicator of basin transitions and early warning signals of instability.
🔄 6. Transition Detection#
Transitions occur when the system crosses basin or mode boundaries.
The Analyzer identifies:
- from → to
- trigger type
- severity
Trigger types include:
- invariant_break
- attention_spike
- cadence_acceleration
- signal_collapse
- narrative_collapse
- reconstruction
Transitions describe why the system moved and how severe the shift was.
🧩 7. Output Assembly#
The Analyzer returns a structured object containing:
- Normalized MediaVector
- InvariantState
- BasinResult
- ModeState
- Drift
- Transition
This output can be used for:
- Monitoring
- Visualization
- Simulation
- Comparative analysis
- Longitudinal tracking
The pipeline ensures that every media ecosystem—regardless of platform, format, or scale—can be evaluated using the same structural grammar.