Fast and Simple Adaptive Elicitations

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 FSE to elicit the probability weighting function in three studies: a simulation and two experiments. Our results demonstrate FSE’s ability to faithfully recover the function. Both in the laboratory and online, FSE reflects the choices made by a respondent more precisely than alternative elicitation procedures and standard functional forms. Importantly, FSE also predicts the out-of-sample choices of a representative online sample more accurately than its alternatives. Taken together, our results shed new light on the prevalence of possibility and certainty effects, and convey general implications for experimental modeling and design.

This paper was accepted by Manel Baucells, behavioral economics and decision analysis.

Funding: This work was supported by INSEAD R&D and by a grant from Sorbonne University.

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.01348.