🧬 Invariant Evaluation

Each invariant returns a value in the range:

0.0 = aligned (no strain)
1.0 = broken (full strain)

These values form the MediaInvariantState, which drives basin classification, mode determination, drift detection, and transition analysis.

🛰 Signal–Narrative Coherence#

This invariant measures whether the system’s signal integrity (S) is strong enough to support its narrative coherence (N).

• High S + high N → aligned
• Low S + high N → unstable (narratives outrun fidelity)
• High S + low N → fragmented or conflicting narratives
• Low S + low N → collapse conditions

Strain increases when narratives become more complex or volatile than the underlying signal can support.

🌐 Distribution–Attention Fit#

This invariant measures whether the distribution topology (D) can carry the system’s attention dynamics (A) without overload.

• High D + high A → stable amplification
• Low D + high A → cascade risk
• High D + low A → underutilized network
• Low D + low A → stagnation

Strain increases when attention spikes exceed the carrying capacity of the distribution structure.

⏱ Temporal–Signal Stability#

This invariant measures whether temporal cadence (T) is moving faster than signal integrity (S) can sustain.

• Low T + high S → stable long‑form coherence
• High T + high S → accelerated but stable
• High T + low S → churn, distortion, collapse
• Low T + low S → stagnation or decay

Strain increases when cadence accelerates beyond the system’s ability to verify or maintain fidelity.

⚡ Attention–Narrative Feedback#

This invariant measures whether attention dynamics (A) are destabilizing or reinforcing narrative coherence (N).

• Moderate A + high N → stable reinforcement
• High A + high N → pressure but coherent
• High A + low N → narrative churn, conflict, collapse
• Low A + low N → stagnation

Strain increases when attention volatility destabilizes weak or conflicting narratives.

🧩 Interpreting Invariant Patterns#

Individual invariants matter, but patterns matter more. The Analyzer looks for combinations such as:

• High Distribution–Attention strain + high Attention–Narrative strain → cascade conditions
• High Signal–Narrative strain + high Temporal–Signal strain → epistemic decay
• Low strain across all invariants → stable or reconstructing systems
• Mixed strain patterns → drift or tension modes

These patterns determine the system’s behavioral mode and its likelihood of transitioning between basins.

🧭 Output: MediaInvariantState#

The Analyzer returns a structured object:

{
  signalNarrativeCoherence: number,
  distributionAttentionFit: number,
  temporalSignalStability: number,
  attentionNarrativeFeedback: number
}

This state is used by:

• Basin classification
• Mode determination
• Drift detection
• Transition analysis

Invariant strain is the backbone of the MSM Analyzer’s interpretation of media ecosystems.