Unusual Estimates of Probability Weighting Functions

I present new estimates of the probability weighting functions found in rank-dependent theories of choice under risk. These estimates are unusual in two senses. First, they are free of functional form assumptions about both utility and weighting functions, and they are entirely based on binary discrete choices and not on matching or valuation tasks, though they depend on assumptions concerning the nature of probabilistic choice under risk. Second, estimated weighting functions contradict widely held priors of an inverse-s shape: Instead I usually find populations dominated by “optimists” who uniformly overweight best outcomes in risky options. A salience-based theory of choice may help to explain this. Additionally, the choice pairs I use here mostly do not provoke similarity-based simplifications. In a third experiment, I show that the presence of choice pairs that provoke similarity-based computational shortcuts does indeed flatten estimated probability weighting functions.