sensitivity-ranking

name	sensitivity-ranking
description	Rank constraints by sensitivity — which ones most impact the outcome if they shift
version	1.0.0
category	experiment-execution
type	tactic
orchestrates	["resource-quantification","assumption-challenging","critical-chain-identification"]
dependencies	{"sops":["assumption-challenging","critical-chain-identification","resource-quantification"]}

Tactic: Sensitivity Ranking

Orchestration Pattern

1. Quantify Resources → spawn resource-quantification

   • For each identified constraint, quantify its current state (demand, supply, gap)
   • Express gaps in comparable units where possible

2. Challenge Assumptions → spawn assumption-challenging

   • For each constraint, identify the assumptions that make it binding
   • Score assumption vulnerability (confidence × evidence / testability)

3. Identify Critical Chain Impact → spawn critical-chain-identification

   • Determine which constraints lie on the critical chain
   • Constraints on the critical chain have higher sensitivity

4. 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