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Optimal design of experiments to identify latent behavioral types

Published online by Cambridge University Press:  14 March 2025

Stefano Balietti Open the ORCID record for Stefano Balietti [Opens in a new window] ,
Brennan Klein Open the ORCID record for Brennan Klein [Opens in a new window]  and
Christoph Riedl Open the ORCID record for Christoph Riedl [Opens in a new window]
Stefano Balietti
Affiliation:
Mannheim Center for European Social Science Research (MZES), Mannheim University, A5, 6, 68159 Mannheim, Germany Alfred-Weber Institute of Economics (AWI), Heidelberg University, Bergheimerstraße 20, 69115 Heidelberg, Germany
Brennan Klein
Affiliation:
Network Science Institute, Northeastern University, Boston, MA 02115, USA
Christoph Riedl*
Affiliation:
Network Science Institute, Northeastern University, Boston, MA 02115, USA D’Amore-McKim School of Business, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA Institute for Quantitative Social Science, Harvard University, Cambridge, MA 02138, USA
*
[email protected]
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Abstract

Bayesian optimal experiments that maximize the information gained from collected data are critical to efficiently identify behavioral models. We extend a seminal method for designing Bayesian optimal experiments by introducing two computational improvements that make the procedure tractable: (1) a search algorithm from artificial intelligence that efficiently explores the space of possible design parameters, and (2) a sampling procedure which evaluates each design parameter combination more efficiently. We apply our procedure to a game of imperfect information to evaluate and quantify the computational improvements. We then collect data across five different experimental designs to compare the ability of the optimal experimental design to discriminate among competing behavioral models against the experimental designs chosen by a “wisdom of experts” prediction experiment. We find that data from the experiment suggested by the optimal design approach requires significantly less data to distinguish behavioral models (i.e., test hypotheses) than data from the experiment suggested by experts. Substantively, we find that reinforcement learning best explains human decision-making in the imperfect information game and that behavior is not adequately described by the Bayesian Nash equilibrium. Our procedure is general and computationally efficient and can be applied to dynamically optimize online experiments.

Keywords

Optimal experimental designBehavioral typesExpert predictionActive learning

JEL classification

C80: General C90: General C72: Noncooperative Games
Type
Original Paper
Information
Experimental Economics , Volume 24 , Issue 3 , September 2021 , pp. 772 - 799
Copyright
Copyright © 2020 Economic Science Association

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10683-020-09680-w) contains supplementary material, which is available to authorized users.

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