TriadicFrameworks
개요

🎛 Mode Determination

Modes are driven by invariant strain, drift magnitude, cadence pressure, and attention volatility. They reveal whether the system is absorbing pressure, destabilizing, cascading, or rebuilding.

🧩 The Six MSM Modes#

The MSM defines six behavioral modes:

  • Stable — low strain, coherent narratives, balanced attention
  • Tension — early strain, rising volatility, pre‑drift conditions
  • Drift — directional movement across the substrate, weakening invariants
  • Cascade — runaway amplification, narrative churn, overloaded distribution
  • Collapse — structural failure, narrative breakdown, attention crash
  • Reconstruction — deliberate rebuilding of coherence and signal integrity

Modes are not tied to specific basins; any basin can exhibit any mode depending on structural pressure.

🧭 Inputs to Mode Determination#

The Analyzer determines mode using four primary signals:

  • Invariant strain — how close the system is to breaking physics constraints
  • Drift magnitude — how quickly the system is moving across the substrate
  • Attention volatility — spikes, churn, or burnout
  • Cadence pressure — acceleration or compression of temporal rhythms

These signals combine to reveal the system’s behavioral state.

📐 Mode Logic#

Stable#

The system is structurally aligned:

  • Low invariant strain
  • Low drift
  • Moderate or low attention volatility
  • Cadence within carrying capacity

Stable systems absorb pressure without destabilizing.

Tension#

Early signs of instability:

  • One or more invariants showing moderate strain
  • Attention volatility rising
  • Cadence beginning to accelerate
  • Drift detectable but small

Tension is the precursor to drift.

Drift#

Directional movement across the substrate:

  • Invariant strain increasing
  • Drift magnitude above threshold
  • Narrative or distribution wobble
  • Attention patterns becoming irregular

Drift indicates the system is leaving its current attractor.

Cascade#

Runaway amplification and overload:

  • High attention volatility
  • High cadence pressure
  • Distribution–Attention Fit breaking
  • Narrative coherence collapsing
  • Drift accelerating

Cascade is a high‑energy, unstable mode.

Collapse#

Structural failure:

  • Multiple invariants broken
  • Narrative coherence near zero
  • Attention crashes after overload
  • Cadence destabilized
  • Drift magnitude unpredictable

Collapse often precedes stagnation or reconstruction.

Reconstruction#

Deliberate stabilization:

  • Signal integrity rising
  • Narrative coherence improving
  • Cadence slowing
  • Attention stabilizing
  • Drift decreasing

Reconstruction is the only mode that moves the system toward stability.

🧬 Mode Thresholds#

Mode thresholds are determined by combinations of:

  • Invariant strain levels
  • Drift magnitude categories
  • Attention volatility patterns
  • Cadence acceleration or compression

Examples:

  • Moderate strain + low drift → Tension
  • High strain + directional drift → Drift
  • High A volatility + high T + broken invariants → Cascade
  • Low A + low T + low N → Collapse
  • Rising S + rising N + slowing T → Reconstruction

Modes are not binary; they reflect structural patterns.

📦 Output: MediaModeState#

The Analyzer returns:

{
  mode: string,
  driftMagnitude: number,
  dominantInvariant: keyof MediaInvariantState
}
  • mode — the behavioral state
  • driftMagnitude — how fast the system is moving
  • dominantInvariant — the invariant contributing most to strain

This output feeds directly into transition detection and longitudinal analysis.

제공: Docsbook