🧭 Drift Detection

Drift detection measures directional movement across the Media Substrate. While invariants describe internal strain and basins describe structural location, drift reveals how fast and in what direction the system is moving. Drift is one of the strongest predictors of transitions, instability, and regime shifts within media ecosystems.

Drift is not sentiment, engagement, or narrative change alone. It is a vector‑level displacement across the five axes of media physics:

[S, D, A, N, T]

📐 What Drift Measures#

Drift captures changes in:

• Signal Integrity (S) — rising or falling fidelity
• Distribution Topology (D) — centralization, fragmentation, or re‑networking
• Attention Dynamics (A) — volatility, spikes, burnout
• Narrative Coherence (N) — alignment, conflict, collapse
• Temporal Cadence (T) — acceleration, compression, slowdown

A system with high drift is structurally unstable, even if its current basin appears stable.

🔢 Computing Drift#

Drift is computed as the difference between two MediaVectors:

Δ = currentVector – previousVector

Magnitude is calculated using Euclidean distance:

magnitude = sqrt(ΔS² + ΔD² + ΔA² + ΔN² + ΔT²)

This magnitude determines the drift category.

🧬 Drift Categories#

The MSM Analyzer classifies drift into four categories:

Micro Drift#

Small, routine adjustments.

• Low invariant strain
• No basin pressure
• Normal narrative or attention fluctuation

Meso Drift#

Meaningful directional movement.

• One or more invariants rising
• Early basin tension
• Narrative wobble or attention irregularity

Macro Drift#

Large structural movement.

• Multiple invariants strained
• Basin boundaries approaching
• Distribution or narrative instability

Regime Shift#

System‑level reconfiguration.

• Invariants breaking
• Basin transition imminent or underway
• Cascade, collapse, or reconstruction conditions

Regime shifts are rare but high‑impact.

🧩 Drift Signatures#

Different axes produce different drift signatures:

• High ΔA + High ΔT → cascade or burnout
• High ΔN + Low ΔS → narrative collapse
• High ΔD + High ΔN → fragmentation
• High ΔS + High ΔN → reconstruction
• High ΔT + Low ΔS → epistemic decay

These signatures help interpret why drift is occurring.

🌀 Drift and Basins#

Drift determines whether the system is:

• Settling deeper into its current basin
• Moving toward a neighboring basin
• Escaping its attractor entirely
• Crossing into cascade or collapse
• Climbing into reconstruction

Basin classification tells you where the system is.
Drift tells you where it’s going.

🎛 Drift and Modes#

Modes are strongly influenced by drift magnitude:

• Stable → micro drift
• Tension → micro or meso drift
• Drift → meso or macro drift
• Cascade → macro drift
• Collapse → macro or regime shift
• Reconstruction → meso drift with rising S and N

Drift is the primary driver of mode transitions.

🔄 Drift and Transitions#

Transitions occur when drift crosses structural thresholds:

• Basin → Basin
• Mode → Mode
• Stable → Tension → Drift → Cascade → Collapse → Reconstruction

The Analyzer uses drift magnitude and direction to determine:

• Trigger type
• Severity
• Trajectory
• Expected next basin or mode

Drift is the earliest and most reliable indicator of systemic change.

📦 Output: MediaDrift#

The Analyzer returns:

{
  delta: MediaVector,
  magnitude: number,
  category: "micro" | "meso" | "macro" | "regime_shift"
}

This output feeds directly into transition detection and longitudinal analysis.