── ── Cognitive bias
Probabilistic Thinking
Most reasoning is binary: will it happen, or won't it? That framing discards the most useful information — the degree of confidence — and produces predictions that cannot be checked, updated, or scored. Probabilistic thinking replaces binary with calibrated probability estimates: numbers anchored in base rates, updated with evidence, and scored after the fact. Rooted in Bayes (1763), Knight's risk-vs-uncertainty…
How it works
Run the Probability Estimate. Base rate first, then evidence, then update, then calibration check.
1. Precise question + deadline. "Will the deal close?" → "Will customer X sign ≥$50K by 2026-09-30?" 2. Anchor in a base rate. Historical fraction of similar situations. No base rate = Knightian territory → report range, not point. 3. Evidence for and against. Each signal moves estimate ↑ or ↓. Be uncharitable about both sides. 4. Bayesian update (plain language). For each signal: P(evidence · outcome happens) vs P(evidence · doesn't happen). The ratio drives the shift. 5. Number + confidence interval. Not "70-ish" — "68%, 80% CI 55–80%." 6. Most-informative next evidence. If nothing would move your estimate, you have a belief, not an estimate. 7. Calibration log. Record estimate, date, resolution criteria. Score after: did 70%-calls land 70% of the time?
When to use it
- reasoning about an uncertain outcome (forecast, diagnosis, pipeline conversion, hire, deal close, geopolitical event)
- when binary "will/won't" predictions are being made
- when a vivid story is replacing a base rate
- when "I'm 90% sure" appears with no calibration evidence
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
Tetlock, IARPA, and the Good Judgment Project (2011–2015)
A worked example. Not pundit theater — peer-reviewed and US-funded.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skills