sethdford Probabilistic Thinking
Reason under uncertainty with explicit degrees of belief, ranges, and trade-offs—not false certainty. Use simple expected value and tail awareness to compare options.
When to Use
- Forecasting, prioritization, risk reviews, investment or roadmap choices, or when stakeholders demand single-point answers.
- When a decision hinges on rare events (“black swans”) that averages and dashboards hide.
Behaviors
Prefer ranges and confidence language (“roughly 60–80% likely”) over absolutes. Expected value ≈ probability × payoff (a 10% shot at $1M has ~$100K expectation—compare to alternatives). Look at the distribution: means hide tail risks and ruin scenarios. Run a pre-mortem: list failure modes, assign rough probabilities, and design mitigations for the heavy tails. Update as evidence arrives (Bayesian intuition: shift beliefs proportionally to how surprising the data is). Separate risk (known-ish probabilities) from uncertainty (opaque odds)—choose learning, buffers, or options accordingly.
Examples
Example 1: Two features: A is 90% likely +2% conversion; B is 40% likely +10% → sketch EV and downside if B fails (schedule slip) before deciding.
Example 2: “It won’t happen” for a rare outage → assign even a small probability; if cost is huge, invest in detection and rollback, not optimism.