sensitivity-ranking

Research

Rank constraints by sensitivity — which ones most impact the outcome if they shift

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Tactic: Sensitivity Ranking

Orchestration Pattern

Quantify Resources → spawn resource-quantification
- For each identified constraint, quantify its current state (demand, supply, gap)
- Express gaps in comparable units where possible
Challenge Assumptions → spawn assumption-challenging
- For each constraint, identify the assumptions that make it binding
- Score assumption vulnerability (confidence × evidence / testability)
Identify Critical Chain Impact → spawn critical-chain-identification
- Determine which constraints lie on the critical chain
- Constraints on the critical chain have higher sensitivity
Compute Sensitivity Score → synthesize
- Sensitivity = (Gap magnitude) × (Chain position weight) × (Assumption vulnerability)
- Chain position weight: on critical chain = 2.0, feeding chain = 1.0, off-chain = 0.5
- Rank all constraints by sensitivity score

Decision Criteria

When to use: After multiple constraints have been identified and need prioritization
When to skip: If only 1-2 constraints exist, ranking is trivial
Threshold: Constraints with sensitivity score >2× the median are "binding"
Output: Ordered list with scores, used by parent strategy to focus effort

Available SOPs

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

SOP	When to use
assumption-challenging	Challenge each assumption's validity — shared cross-repo SOP
critical-chain-identification	Identify the critical chain — longest path considering resource contention
resource-quantification	Quantify resource demand vs supply vs gap for each resource category