uncertainty-propagation

name	uncertainty-propagation
description	Propagate input uncertainties through the model via Monte Carlo sampling. Identifies which input uncertainties contribute most to output uncertainty.
dependencies	{"tactics":["uncertainty-cascade"],"sops":["critical-path-identification","distribution-assignment","monte-carlo-sampling"]}

Uncertainty Propagation

Map how input uncertainty flows to output uncertainty.

Budget

Base SOP	Target	±10% Range
web-search	20	18–22
web-research	10	9–11
paper-overview	25	22–28
paper-search	20	18–22
paper-research	10	9–11

State Ledger

<HARD-GATE>
| SOP | Done | Target | % |
|-----|------|--------|---|
| web-search | ? | 20 | ? |
| web-research | ? | 10 | ? |
| paper-overview | ? | 25 | ? |
| paper-search | ? | 20 | ? |
| paper-research | ? | 10 | ? |
Budget Gate: OPEN/CLOSED (>=80% required to exit)
</HARD-GATE>

Available Tactics

• uncertainty-cascade

Available SOPs

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

Execution Guidance

Assign probability distributions to uncertain inputs, propagate through the model via Monte Carlo sampling, analyze output distribution, identify which input uncertainties contribute most.

Available Tactics

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

Tactic	When to use
uncertainty-cascade	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.

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.
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.