priority-sensitivity-testing

Research

Tactic: perturb scoring weights to test the robustness of the gap ranking against weight choice

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Priority Sensitivity Testing

Apply weight perturbations to an existing gap scoring matrix to test whether the final ranking is sensitive to weight choice, thereby judging the credibility of the priority decision.

Orchestration Intent

The final ranking of a scoring matrix depends on the weight settings for each dimension. If a small change in weights drastically changes the ranking, the decision is unreliable; if the ranking remains stable across a reasonable weight range, the priority conclusion is more convincing.

This tactic first establishes baseline weights (AHP or equal weights), then systematically perturbs the weights (±20%), observes the ranking changes, and finally gives a stability verdict.

Available SOPs

SOP	Responsibility	When to call
ahp-weighting	Use the AHP method to derive a weight vector from dimension-importance judgments	First step, establish baseline weights
weight-perturbation	Apply ±20% perturbations to each dimension weight and recompute the ranking	Second step, systematic perturbation
priority-synthesis	Aggregate the ranking results across all perturbation scenarios into a stability report	Third step, synthesize conclusion

Orchestration Pattern

Default (standard flow)

ahp-weighting: derive baseline weights from the dimension-importance judgments provided by the user/strategy; use equal weights if none provided
weight-perturbation: apply +20% and -20% perturbations to each dimension in turn (the remaining dimensions are adjusted proportionally to keep the sum at 1), producing a ranking for each perturbation scenario
priority-synthesis: tally each gap's rank distribution across all perturbation scenarios and output a stability verdict

Simplified (S tier or fast mode)

Skip ahp-weighting, directly use equal weights as the baseline
Perturb only the highest-weight dimension (±20%), producing 2 perturbation scenarios
priority-synthesis gives a simplified stability report

Deep (L tier or high-risk decision)

ahp-weighting uses a full AHP pairwise comparison (pairwise comparison between dimensions)
weight-perturbation expands the perturbation range to ±30% and adds extreme scenarios (one dimension's weight set to 0)
priority-synthesis additionally outputs the Spearman rank correlation coefficient (baseline ranking vs each perturbed ranking)

Minimum Yield

A baseline weight vector (all dimension weights sum to 1)
Ranking results for at least 3 perturbation scenarios (each scenario annotated with its perturbation content)
Rank-stability statistics for each gap (highest rank, lowest rank, modal rank)
A final stability verdict: Stable (top N unchanged across all scenarios) / Partially Sensitive (1-2 changes in the top N) / Highly Sensitive (more than 2 changes in the top N)

Yield Report

After execution, report to the calling strategy:

Source of the baseline weights (AHP-derived / equal weights / user-specified)
Number of perturbation scenarios
The stability-verdict conclusion
The least stable gap (largest rank fluctuation) and a suggestion (whether more evidence is needed before deciding)

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
ahp-weighting	SOP: Use the AHP (Analytic Hierarchy Process) to determine scoring-dimension weights, outputting a weight vector
priority-synthesis	SOP: synthesize all scoring data into a final gap priority list and attack-path suggestions
weight-perturbation	SOP: Perturb weights to test gap-ranking stability, output a stability verdict