A Data-Driven Approach to Modeling Choice

A Data-Driven Approach to Modeling Choice

We visit the following fundamental problem: For a 'generic' model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a p...

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Bibliographic Details
Main Authors: Farias, Vivek F. (Contributor), Jagabathula, Srikanth (Contributor), Shah, Devavrat (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Laboratory for Information and Decision Systems (Contributor), Sloan School of Management (Contributor)
Format: Article
Language:English
Published: Neural Information Processing Systems Foundation, 2015-02-20T15:15:23Z.
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Summary:We visit the following fundamental problem: For a 'generic' model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a particular assortment of choices? This problem is central to areas within operations research, marketing and econometrics. We present a framework to answer such questions and design a number of tractable algorithms (from a data and computational standpoint) for the same.
National Science Foundation (U.S.) (CAREER CNS 0546590)

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