The Value of Probabilistic Thinking: Spies, Crime, and Lightning Strikes
Farnam Street (Shane Parrish) · 2018
"Good decisions under uncertainty require three specific probabilistic skills: Bayesian updating (weighing new evidence against prior knowledge), recognizing fat-tailed distributions (where extreme outliers are far more common than a normal bell curve implies), and correcting for asymmetric estimation errors (like investors who systematically overestimate their own returns)."
Bell-curve ('normal') distributions have predictable, bounded extremes (you'll never meet a man ten times the height of average); fat-tailed distributions (wealth, terrorism risk, market crashes) have no such natural cap, meaning rare extreme events are far more likely and far more consequential than bell-curve intuition suggests — so comparing a fat-tailed risk (terrorism) to a bell-curve risk (slipping on stairs) using only recent-year death counts, as some commentators do, misunderstands which kind of distribution you're actually in.
The asymmetry point adds a further correction: probability estimates themselves tend to be systematically biased in a particular direction depending on context — professional investors who publicly project 20-40% annual returns almost always underperform that projection (since the long-run US stock market has returned roughly 7-8% annually before fees), while commuters who leave 'on time' almost always arrive late rather than early, showing that estimation errors aren't random noise but a directional pattern you can and should correct for.
What distinguishes a 'fat-tailed' distribution (like wealth or market crashes) from a normal bell-curve distribution (like human height)?
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The explanation above is written with AI assistance. These are the originals — go to them to check it.
- The Pygmalion Effect: Proving Them Right (related Farnam Street piece)Farnam Street
- Annie Duke — Thinking in BetsFarnam Street
Descriptions Aren't Prescriptions
"A representation of reality (a map, a grammar book, a law, a mental model) can be read either descriptively (this is how things currently are) or prescriptively (this is how things must be) — confusing the two, especially when the description becomes outdated, causes real, avoidable problems."