🧪 MSM Analyzer — Worked Examples
📘 Example 1 — Network System Entering Drift#
Input Vector#
{
S: 0.58,
D: 0.72,
A: 0.41,
N: 0.63,
T: 0.47
}
Invariant Evaluation#
- Signal–Narrative Coherence: 0.22
- Distribution–Attention Fit: 0.18
- Temporal–Signal Stability: 0.31
- Attention–Narrative Feedback: 0.27
Moderate strain across multiple invariants indicates early instability.
Basin Classification#
- Closest basin: Network
- Gate conditions: Satisfied
- Distance: 0.19
The system remains in the Network basin but is under pressure.
Mode Determination#
- Drift magnitude: 0.14 (meso drift)
- Dominant invariant: Temporal–Signal Stability
- Mode: Tension
The system is beginning to destabilize but has not yet entered full drift.
Drift Detection#
- Δ from previous vector: moderate
- Category: meso drift
Transition Detection#
- Previous: Network / Stable
- Current: Network / Tension
- Trigger: cadence acceleration
- Severity: 0.42
The system is shifting toward Drift mode.
📙 Example 2 — Fragment System Collapsing into Cascade#
Input Vector#
{
S: 0.29,
D: 0.34,
A: 0.88,
N: 0.21,
T: 0.91
}
Invariant Evaluation#
- Signal–Narrative Coherence: 0.71
- Distribution–Attention Fit: 0.83
- Temporal–Signal Stability: 0.92
- Attention–Narrative Feedback: 0.79
Multiple invariants are near or above collapse thresholds.
Basin Classification#
- Closest basin: Cascade
- Gate conditions: Satisfied
- Distance: 0.11
The system has entered the Cascade attractor.
Mode Determination#
- Drift magnitude: 0.37 (macro drift)
- Dominant invariant: Temporal–Signal Stability
- Mode: Cascade
The system is in a high‑energy, unstable state.
Drift Detection#
- Δ from previous vector: large
- Category: macro drift
Transition Detection#
- Previous: Fragment / Drift
- Current: Cascade / Cascade
- Trigger: attention spike + cadence acceleration
- Severity: 0.87
This is a high‑severity structural transition.
📗 Example 3 — Collapse Moving into Reconstruction#
Input Vector#
{
S: 0.44,
D: 0.39,
A: 0.27,
N: 0.36,
T: 0.33
}
Invariant Evaluation#
- Signal–Narrative Coherence: 0.41
- Distribution–Attention Fit: 0.19
- Temporal–Signal Stability: 0.22
- Attention–Narrative Feedback: 0.28
Strain is decreasing across all invariants.
Basin Classification#
- Closest basin: Reconstruction
- Gate conditions: Satisfied
- Distance: 0.17
The system is stabilizing and rebuilding.
Mode Determination#
- Drift magnitude: 0.09 (micro drift)
- Dominant invariant: Signal–Narrative Coherence
- Mode: Reconstruction
The system is recovering from collapse.
Drift Detection#
- Δ from previous vector: small
- Category: micro drift
Transition Detection#
- Previous: Collapse / Collapse
- Current: Reconstruction / Reconstruction
- Trigger: signal recovery + narrative stabilization
- Severity: 0.33
A positive‑direction transition.
📕 Example 4 — Stagnation with Minimal Drift#
Input Vector#
{
S: 0.31,
D: 0.28,
A: 0.12,
N: 0.29,
T: 0.14
}
Invariant Evaluation#
- Signal–Narrative Coherence: 0.38
- Distribution–Attention Fit: 0.09
- Temporal–Signal Stability: 0.12
- Attention–Narrative Feedback: 0.17
Low strain but also low energy.
Basin Classification#
- Closest basin: Stagnation
- Gate conditions: Satisfied
- Distance: 0.13
Mode Determination#
- Drift magnitude: 0.03 (micro drift)
- Mode: Stable
The system is quiet, low‑energy, and structurally inert.
Drift Detection#
- Category: micro drift
Transition Detection#
- No transition detected
- Trigger: none
- Severity: 0.00
The system remains in Stagnation.
📓 Example 5 — Broadcast System Under Early Pressure#
Input Vector#
{
S: 0.82,
D: 0.91,
A: 0.22,
N: 0.87,
T: 0.29
}
Invariant Evaluation#
- Signal–Narrative Coherence: 0.08
- Distribution–Attention Fit: 0.11
- Temporal–Signal Stability: 0.14
- Attention–Narrative Feedback: 0.09
Very low strain; system is stable.
Basin Classification#
- Closest basin: Broadcast
- Gate conditions: Satisfied
- Distance: 0.07
Mode Determination#
- Drift magnitude: 0.05
- Mode: Stable
Drift Detection#
- Category: micro drift
Transition Detection#
- No transition
- System remains stable
These examples give us a complete set of reference patterns for how the MSM Analyzer behaves across different structural conditions.
