What is Probability weighting?
Probability weighting describes how people transform objective probabilities into subjective decision weights that systematically distort rational calculation. Small probabilities are overweighted and moderate-to-high probabilities are underweighted.
How it works
Kahneman and Tversky’s prospect theory formalized this with a probability weighting function that is steep near 0% and 100% but relatively flat in between. A 1% chance of winning feels much larger than 1%, while an 80% chance feels much less certain than 80%. This produces a fourfold pattern: risk-seeking for small-probability gains (lottery tickets), risk-aversion for small-probability losses (insurance), risk-aversion for high-probability gains (preferring certainty), and risk-seeking for high-probability losses (gambling to avoid certain loss).
Applied example
Earthquake insurance sells at premiums far above actuarially fair prices because homeowners overweight the small probability of a devastating quake. Conversely, people underweight the quite likely risk of running out of retirement savings.
Why it matters
Probability weighting is essential for understanding why insurance, gambling, and extreme-risk industries behave as they do, and why actuarial reasoning alone fails to predict consumer behavior.
