A New Approach to Modeling Choice with Limited Data

A framework to answer questions about how to predict revenues from offering a particular assortment of choices and design a number of tractable algorithms from a data and computational standpoint is presented.

Abstract

We visit the following 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 is a central problem in operations research and marketing. We present a framework to answer such questions and design a number of tractable algorithms from a data and computational standpoint for the same. 1.