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Bayesian Rapid Optimal Adaptive Design (BROAD): Method and application distinguishing models of risky choice

Ray, Debajyoti and Golovin, Daniel and Krause, Andreas and Camerer, Colin (2013) Bayesian Rapid Optimal Adaptive Design (BROAD): Method and application distinguishing models of risky choice. . (Unpublished)

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Economic surveys and experiments usually present fixed questions to respondents. Rapid computation now allows adaptively optimized questions, based on previous responses, to maximize expected information. We describe a novel method of this type introduced in computer science, and apply it experimentally to six theories of risky choice. The EC² method creates equivalence classes, each consisting of a true theory and its noisy-response perturbations, and chooses questions with the goal of distinguishing between equivalence classes by cutting edges connecting them. The edge-cutting information measure is adaptively submodular, which enables a provable performance bound and lazy evaluation which saves computation. The experimental data show that most subjects, making only 30 choices, can be reliably classified as choosing according to EV or two variants of prospect theory. We also consider whether subjects should and could manipulate by misreporting preferences, and find little evidence of manipulation.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper
Krause, Andreas0000-0001-7260-9673
Camerer, Colin0000-0003-4049-1871
Additional Information:Submitted to Econometrica.
Classification Code:JEL: C520 - Model Evaluation, Validation, and Selection; C800 - Data Collection and Data Estimation Methodology; Computer Programs: General; C910 - Design of Experiments: Laboratory, Individual
Record Number:CaltechAUTHORS:20200117-110441501
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100791
Deposited By: Tony Diaz
Deposited On:17 Jan 2020 19:11
Last Modified:16 Nov 2021 17:56

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