Probability weighting
We overweight rare events and the jump to certainty, and underweight middling probabilities.
What it means
Probability weighting is the finding that people do not treat objective probabilities linearly when making risky choices; instead they pass them through a decision-weight function that systematically distorts them. Small probabilities are overweighted, which makes both lottery tickets and insurance attractive, while moderate-to-large probabilities are underweighted, and the transition from merely probable to certain carries disproportionate weight. The resulting weighting curve is typically inverse-S-shaped, steep near zero and near one and flatter in the middle, reflecting limited discrimination among intermediate odds. This component of (cumulative) prospect theory accounts for the certainty and possibility effects and for the coexistence of gambling and insurance in the same person. A nuance is that weighting depends on how probabilities are described, on whether outcomes are experienced rather than stated (the description–experience gap, where rare events are sometimes underweighted), and on emotional salience. It matters because it predicts demand for warranties, lotteries, and catastrophe insurance, and explains why communicating risk as 'one in a million' rarely calms or alarms people in proportion to the true odds.
Examples
People pay eagerly for both lottery tickets (a tiny chance of a huge gain) and insurance (a tiny chance of a huge loss), overweighting the rare outcome in each.
At the till, shoppers pay £40 to insure a £200 phone against a small chance of a cracked screen — the tiny risk feels far bigger than the price implies.
A treatment that lifts a cure rate from 95% to 100% is worth far more to patients than one lifting it from 60% to 65%, though both add five points.
First described in Kahneman & Tversky (1979); Tversky & Kahneman (1992).