Probability
What's my base rate before I saw this evidence?
Prompts
Before any decision under uncertainty:
- "What's my base rate?" — Start with how often this happens in general
- "What would change my confidence by 10%?" — Identify the evidence that matters
- "Am I updating enough—or too much?" — Calibrate against your tendency
- "What would prove me wrong?" — Name the falsifying evidence
Core Principles
Explicit Priors — Start with base rates before incorporating new evidence.
- In the Steve example: 20:1 farmer-to-librarian ratio anchors the calculation
- In investing: 90% of crypto startups fail should anchor initial risk assessments
Iterative Updates — Use Bayes' formula to update multiplicatively.
- If a startup CEO has a strong track record (evidence), update success probability
- Don't reset beliefs—compound them
Process Over Outcomes — Evaluate decisions based on information available at the time.
- A 16.7% posterior can be rational even if wrong
- Good process sometimes produces bad outcomes—that's probability
The Framework
| Step | Question | Action |
|---|---|---|
| 1. Priors | What's the base rate? | Quantify before seeing evidence |
| 2. Evidence | What new information exists? | Identify likelihood ratios |
| 3. Update | How much should this change belief? | Apply Bayes multiplicatively |
| 4. Decision | What does the posterior imply? | Act on updated probability |
Practice Applications
| Scenario | Bayesian Approach | Example |
|---|---|---|
| Investment | Update valuations using market signals | Rebalance if inflation likelihood spikes |
| Negotiations | Model opponent priors, update with behavioral cues | Adjust offers if counterparty hesitates |
| Product Launches | Use A/B testing to iteratively refine | Beta test → Measure → Scale |
Execution Checklist
- Explore/Exploit — Balance experimentation (updating priors) with leveraging known strategies
- Reversible vs Irreversible — Apply Bayesian for reversible; game theory for irreversible
- Quantify Intuition — Assign confidence scores (e.g., "60% sure") and track calibration
- Sensitivity Analysis — "What if the ratio is 50:1 instead of 20:1?"
- Premortems — "Why might this NOT be true despite my evidence?"
Combating Biases
Ego Mitigation — Your prior beliefs aren't sacred. Evidence updates them.
Information Asymmetry — When one party knows more, treat gaps as signals to investigate.
Anchoring — Be aware that the first number you see biases subsequent estimates.
danger
Your ego is the enemy when it comes to making good decisions
Learn Through Games
Play games that teach probability through stakes:
- Poker — Read opponents, size bets, manage bankroll
- Prediction Markets — Put money where your beliefs are
- Calibration Training — Track predictions, measure accuracy
Bets gamify commitment. They force:
- Explore or exploit?
- One-way or reversible?
Context
- Decision Making — The broader decision framework
- Predictions — Master the prediction game
- Process Modelling — Build reliable systems