── ── 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…
Run Probabilistic Thinking on a real problem
Bring something you're actually deciding — free, in the browser.
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 forecasting AI timelines / AGI arrival / agentic reliability, or judging whether AI capex, AI valuations, or AI adoption rates justify a point-estimate bet amid genuine uncertainty
When not to use it
When the decision is routine and reversible, applying a formal method costs more than it returns.
Worked example
Forecasting AI Timelines and Agentic Reliability (2023–2026)
A worked example on the hardest kind of forecast: one where the reference class is thin, the incentives are loud, and the question is often phrased so vaguely it cannot be scored.
Install this skill (free, MIT)
npx skills add deciqAI/knowledge-skillsUseful? Star the repo — stars help other builders find it.
Related mental models
The Red Queen Effect: competitors must continuously improve just to maintain relative position — because everyone else is improving simultaneously.
The representativeness heuristic is judging probability by how closely something resembles a prototype — overriding actual base rates.
Self-renewal is the structured practice of identifying which mental models have passed their expiry date and genuinely updating — not merely annotating — them.
Status quo bias is the systematic preference for the current state over available alternatives — even when alternatives are objectively superior by the person's own values.