Parameter-Free Elicitation of Utility and Probability Weighting Functions

TLDR

The paper proposes a two‑step method to elicit utility functions and decision weights under rank‑dependent expected utility theory and cumulative prospect theory. The method involves a two‑step elicitation process applied in an experimental study to obtain individual utility and probability weighting functions for monetary outcomes in both gain and loss domains. The parameter‑free method yields concave utility functions for gains, convex for losses, and probability weighting functions that satisfy subadditivity, align with prior parametric estimates, and show a higher elevation for losses than gains.

Abstract

This paper proposes a two-step method to successively elicit utility functions and decision weights under rank-dependent expected utility theory and its “more descriptive” version: cumulative prospect theory. The novelty of the method is that it is parameter-free, and thus elicits the whole individual preference functional without imposing any prior restriction. This method is used in an experimental study to elicit individual utility and probability weighting functions for monetary outcomes in the gain and loss domains. Concave utility functions are obtained for gains and convex utility functions for losses. The elicited weighting functions satisfy upper and lower subadditivity and are consistent with previous parametric estimations. The data also show that the probability weighting function for losses is more “elevated” than for gains.