uncertainty-cascade

Research

Uncertainty cascade propagation — assign input distributions, sample via Monte Carlo, propagate through model, analyze output distribution, identify critical paths. Maps how input uncertainty flows to output uncertainty.

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Uncertainty Cascade

Map how input uncertainty flows through to output uncertainty.

Operations

distribution-assignment → monte-carlo-sampling → critical-path-identification

Available SOPs

Subagent: distribution-assignment, monte-carlo-sampling, critical-path-identification Import: paper-search

Execution Guidance

Assign distributions to all uncertain inputs (use literature for informed priors), sample (Latin Hypercube for efficiency), propagate, analyze output distribution shape and spread, identify which inputs contribute most to output variance (critical path).

Focus on: which inputs, if resolved, would most reduce output uncertainty? This directly informs research prioritization.

Minimum Yield

<HARD-GATE>
- Input distributions assigned: >= 4
- Propagation completed: yes
- Output distribution characterized: yes
- Critical path identified: >= 1 path with ranked inputs
</HARD-GATE>

Available SOPs

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

SOP	When to use
critical-path-identification	Identify which input uncertainties contribute most to output uncertainty and compute EVPI for research prioritization.
deep-insight-paper-search	AI-powered paper summary and search. Import of literature-engine/literature-search skill. AI summary level — cite as "AI-extracted" not "paper states".
distribution-assignment	Assign probability distributions to uncertain parameters based on available evidence and domain knowledge.
monte-carlo-sampling	Design and execute Monte Carlo sampling strategy for uncertainty propagation through a model.