yogsoth-ai Assumption Excavation
A three-phase tactic that surfaces hidden assumptions, challenges each one adversarially, and maps which assumptions are load-bearing for the conclusion. Decisions often rest on unstated beliefs — this tactic makes them explicit and tests their strength.
Stages
- Assumption Extraction — Systematically surface all assumptions underlying the decision, with confidence levels
- Assumption Challenge — For each assumption, construct the strongest counter-argument and identify alternatives
- Conclusion Sensitivity — Map which assumptions, if wrong, would change the conclusion
Available SOPs
| SOP | Phase | Purpose |
|---|---|---|
| assumption-extraction | Extract | Surface hidden assumptions with confidence |
| assumption-challenge | Challenge | Attack each assumption adversarially |
| conclusion-sensitivity | Sensitivity | Map load-bearing assumptions |
Execution Guidance
- Extract minimum 5 assumptions per decision
- Challenge ALL assumptions, not just obvious ones
- Confidence levels: HIGH (>80%), MEDIUM (50-80%), LOW (<50%)
- Critical assumption = conclusion changes if assumption is wrong
- Focus mitigation efforts on critical + low-confidence assumptions
Minimum Yield
= 5 assumptions extracted with confidence levels
- Challenge argument for each assumption
- Alternative assumption for each (what if the opposite is true?)
- Sensitivity map showing which assumptions are critical
- List of critical assumptions requiring mitigation
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| conclusion-sensitivity | Map which assumptions are load-bearing by assessing how the conclusion changes if each assumption fails. |
| convergence-assumption-challenge | Construct the strongest counter-argument against a specific assumption and propose alternatives. |
| convergence-assumption-extraction | Systematically surface hidden assumptions underlying a decision with confidence levels. |