Layer I • Signal Intake

Layer I — Signal Intake

Collects civilizational indicators across domains, normalizes scale, tags confidence, and prepares signals for system mapping.

Module View Blueprint Layer Structured Readout

Purpose: Convert heterogeneous, noisy indicators into a stable input surface with provenance and confidence weighting — ready for Layer II mapping.

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Inputs

  • Domain indicators: energy, liquidity, trust/cohesion proxies, escalation signals, ecological stress.
  • Discrete events: policy actions, disruptions, breakthroughs, chokepoint shocks, institutional fractures.
  • Constraints: time windows, region scope, model bounds, uncertainty tags, provenance status.

Outputs

  • Normalized signal surface s(t) on comparable scales.
  • Confidence weights c(t) for every signal family.
  • Anomaly marks for spikes, discontinuities, or possible distortion.

Process Sketch

Convert heterogeneous, noisy indicators into a stable input surface with provenance and confidence weighting — ready for Layer II mapping.

Raw inputs (multi-domain)
   ↓  normalize units / scale
Weighted signals (confidence)
   ↓  tag uncertainty / anomalies
Output surface s(t) + provenance
s_i(t) = norm(x_i(t)) ŝ_i(t) = c_i(t) · s_i(t) where c_i(t) ∈ [0,1] is signal confidence.
Interpretation: each raw input is normalized, then weighted by confidence before entering mapping.

Example

  • Input: rising energy chokepoint risk + falling institutional trust + liquidity strain.
  • Output: a weighted stress profile with anomaly tags and source provenance.
  • Downstream: Layer II uses this profile to map actors, couplings, and constraints.

Notes / Limits

  • Not prediction: intake is measurement, not forecasting.
  • Noise-aware: uncertainty is carried forward, not hidden.
  • Provenance first: source integrity matters as much as the number.