weight-perturbation

name	weight-perturbation
description	SOP: Perturb weights to test gap-ranking stability, output a stability verdict
version	1.0.0
category	hypothesis-formation
type	sop
campaign	gap-prioritization
input	Baseline weight vector + gap scoring matrix (gap × dimension)
output	PerturbationReport — perturbation scenarios, ranking change magnitude, and stability verdict
dependencies	{"skills":["subagent-spawning"]}

Weight Perturbation

Perturb weights to test gap-ranking stability, output a stability verdict.

HARD-GATE

- The elements of the input weight vector must sum to 1.0 (±0.001 tolerance allowed) - The number of rows in the scoring matrix (number of gaps) must be ≥ 2 - At least 4 perturbation scenarios must be generated (±20% per dimension) - stability_verdict must be one of "stable" | "sensitive" | "unstable"

Pipeline

1. Precheck: Verify the weight vector is normalized; verify the scoring matrix dimensions match the weight vector length
2. Baseline ranking computation: Weight-sum the scoring matrix with the baseline weights to obtain the baseline ranking
3. Perturbation scenario generation: Apply +20% and -20% perturbations to each dimension separately (re-normalizing afterward), generating 2×n perturbation scenarios
4. Re-compute rankings: Compute a new ranking for each perturbation scenario
5. Compare change magnitude: Count the number of gaps whose ranking changed in each scenario; compute Kendall τ correlation against the baseline ranking
6. Stability verdict: stable (all scenarios τ ≥ 0.8) / sensitive (any scenario 0.5 ≤ τ < 0.8) / unstable (any scenario τ < 0.5)
7. Output: Return a PerturbationReport object

Output Format

{
  "baseline_ranking": ["gap_003", "gap_001", "gap_002"],
  "perturbation_scenarios": [
    {
      "scenario_id": "importance_+20%",
      "perturbed_weights": { "importance": 0.48, "feasibility"