Intensity Matching

The cognitive process of translating between different scales of intensity—mapping a value from one dimension to an 'equivalent' intensity on another. When asked 'if Julie read fluently at age 4, how tall would she be as an adult?', people answer around 6 feet—matching the percentile rank of exceptional-for-age reading to an equivalent percentile for height. Kahneman identified intensity matching as the mechanism behind many prediction errors: people translate the intensity of evidence (this candidate seems very impressive) directly to the intensity of the prediction (they'll be a top performer)—without accounting for regression to the mean.

When to use it

When predictions about future performance seem to match the intensity of current impressions too perfectly; when interview enthusiasm translates directly to hiring confidence; when evidence evaluation needs to account for regression to the mean; when designing prediction processes that need to avoid intensity-matching errors.

How it can help

When you notice yourself translating intensity between domains—'this evidence is very strong, so the outcome will be very extreme'—pause and check for regression to the mean. The strength of a signal rarely maps linearly to the extremity of the outcome. A very impressive interview (strong signal intensity) predicts above-average performance, not stellar performance—because interviews are imperfect predictors. The correction: when making predictions, reduce the intensity of your prediction relative to the intensity of your evidence. Your prediction should be less extreme than your evidence suggests.