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

Debajyoti Ray, Daniel Golovin, Andreas Krause and Colin Camerer ()

No utvbz, OSF Preprints from Center for Open Science

Abstract: 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 show that it is difficult for subjects to manipulate by misreporting preferences, and find no evidence of manipulation.

Date: 2019-11-18
New Economics Papers: this item is included in nep-des, nep-exp and nep-upt
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/5dd1c6065b97bd000e5a3343/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:utvbz

DOI: 10.31219/osf.io/utvbz

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().