Fast and Simple Adaptive Elicitations

86 Pages Posted: 5 May 2020 Last revised: 27 Aug 2023

See all articles by Nicolò Bertani

Nicolò Bertani

Catholic University of Portugal (UCP) - Catolica Lisbon School of Business and Economics

Abdellah Boukhatem

affiliation not provided to SSRN

Enrico Diecidue

INSEAD – Decision Sciences

Patrice Perny

affiliation not provided to SSRN

Paolo Viappiani

affiliation not provided to SSRN

Date Written: August 25, 2023

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

Suggested Citation

Bertani, Nicolò and Boukhatem, Abdellah and Diecidue, Enrico and Perny, Patrice and Viappiani, Paolo, Fast and Simple Adaptive Elicitations (August 25, 2023). Available at SSRN: https://ssrn.com/abstract=3569625 or http://dx.doi.org/10.2139/ssrn.3569625

Nicolò Bertani

Catholic University of Portugal (UCP) - Catolica Lisbon School of Business and Economics ( email )

Palma de Cima
Lisbon, 1649-023
Portugal

Abdellah Boukhatem

affiliation not provided to SSRN

Enrico Diecidue (Contact Author)

INSEAD – Decision Sciences ( email )

France

Patrice Perny

affiliation not provided to SSRN

Paolo Viappiani

affiliation not provided to SSRN

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