Decompose complex questions into sub-questions
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
Key takeaways
- What it is: Break a hard forecasting question into smaller, estimable pieces and aggregate them.
- Why it works: Forecasting a complex outcome directly invites an availability-based gut estimate, which is sensitive to framing and salient recent events. Decomposing into sub-questions forces the analysis to address each component independently, catches missed dependencies, and makes the estimate auditable. Aggregating well-calibrated sub-estimates produces more accurate total estimates than directly estimating the whole.
- Evidence: Backed by observational / correlational evidence.
- Avoid: Decomposing into components that feel tractable rather than ones that are causally relevant, which produces a precise answer to the wrong question.
Why it works
Forecasting a complex outcome directly invites an availability-based gut estimate, which is sensitive to framing and salient recent events. Decomposing into sub-questions forces the analysis to address each component independently, catches missed dependencies, and makes the estimate auditable. Aggregating well-calibrated sub-estimates produces more accurate total estimates than directly estimating the whole.
How to do it
- 1Write the forecast question explicitly at the top of a page.
- 2Ask: "What would need to be true for this to happen?" List three to five necessary components.
- 3Estimate each component separately using available evidence and base rates.
- 4Combine the sub-estimates: if each component is independent, the joint probability is the product of the sub-probabilities.
- 5Note where the components interact or are correlated and adjust accordingly.
What the evidence says
ObservationalTetlock found decomposition to be a characteristic practice of superforecasters. Separately, Fermi estimation (a form of decomposition) has shown accurate results in contexts ranging from physics to population estimation. The mechanism — forcing component-level rigor — is well supported.
Honest caveat: Decomposition errors can compound if sub-questions are not truly independent or if a key component is omitted; the quality of the decomposition matters as much as the act of decomposing.
- — Tetlock & Gardner (2015), Superforecasting
Common mistake
Decomposing into components that feel tractable rather than ones that are causally relevant, which produces a precise answer to the wrong question.
IX Coach walks you through structured decomposition for complex goals, making each component visible and estimable so your overall confidence is grounded rather than gestured.
Practice this with IX Coach →