Insurance and Probability Weighting Functions

Abstract

Evidence shows that (i) people overweight low probabilities and underweight high probabilities, but (ii) ignore events of extremely low probability and treat extremely high probability events as certain. Decision models, such as rank dependent utility (RDU) and cumulative prospect theory (CP), use probability weighting functions. Existing probability weighting functions incorporate (i) but not (ii). Our contribution is threefold. First, we show that this would lead people, even in the presence of fixed costs and actuarially unfair premiums, to insure fully against losses of sufficiently low probability. This is contrary to the evidence. Second, we introduce a new class of probability weighting functions, which we call higher order Prelec probability weighting functions, that incorporate (i) and (ii). Third, we show that if RDU or CP are combined with our new probability weighting function, then a decision maker will not buy insurance against a loss of sufficiently low probability; in agreement with the evidence. We also show that our weighting function solves the St. Petersburg paradox that reemerges under RDU and CP.