Fast and Simple Adaptive Elicitations

Abstract

We propose a new fast and simple elicitation procedure (FSE) for the measurement of decision models. Our procedure bounds the function of the decision model, iteratively halves the maximal distance between the bounds, and incrementally restricts the feasible space of associated parameters. It requires no distributional assumptions and relies on linear programming and approximating splines, improving tractability and descriptiveness.We apply and illustrate FSE to elicit the probability weighting function with both simulated and human respondents. Our procedure faithfully recovers the underlying functions and captures empirical regularities in probability weighting. In addition, it sheds new light on the impact of standard parametric forms on the estimation of possibility and certainty effects.

Keywords: elicitations, probability weighting function, Rank-Dependent Utility, Expected Utility

JEL Classification: C1