assumption-constraint

Business

Which assumptions are most fragile? — Vulnerability ranking + impact assessment of experiment assumptions

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Strategy: Assumption Constraint

Methodology

Systematic assumption vulnerability analysis:

Extraction: Surface all implicit and explicit assumptions
Scoring: Quantify vulnerability (confidence × evidence / testability)
Impact assessment: Blast radius × recovery cost
Prioritization: Vulnerability × Impact = Priority
Validation planning: Cheapest test that resolves uncertainty

Assumption categories:

Category	Examples
Technical	Method convergence, architecture suitability
Data	Availability, quality, representativeness
Resource	Sufficiency of compute, time, expertise
Environmental	Tool stability, API access, policy
Theoretical	Effect existence, measurability, magnitude

Execution Flow

Challenge Assumptions → call assumption-challenging SOP
- Input: experiment plan, hypothesis
- Output: assumption inventory with validity assessment
Quantify Validation Cost → call resource-quantification SOP
- Input: validation experiments for top assumptions
- Output: cost to validate each assumption
Rank Sensitivity → invoke sensitivity-ranking tactic
- Determine which assumptions are most binding
Report → synthesize vulnerability assessment
- Top-5 fragile assumptions with validation paths
- Binding assumption constraint identification

Budget Gate

Resource	Budget	Notes
Subagent calls	≤5	2 SOPs + synthesis
Iterations	≤2	Re-rank if new assumptions surface
Output size	≤3000 tokens	Ranked table + validation plan

Available Tactics

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

Tactic	When to use
sensitivity-ranking	Rank constraints by sensitivity — which ones most impact the outcome if they shift

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
resource-quantification	Quantify resource demand vs supply vs gap for each resource category