yogsoth-ai 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. |