A Nonparametric Approach to Modeling Choice with Limited Data

TL;DR

This paper introduces a nonparametric framework for predicting revenues based on limited consumer choice data, advancing the automation of choice model selection in operations management.

Contribution

It develops a novel nonparametric approach and algorithms to predict revenues from limited choice data, addressing the challenge of model identification in consumer choice modeling.

Findings

Framework effectively predicts revenues with limited data

Algorithms are computationally tractable and practical

Advances automation in choice model selection

Abstract

A central push in operations models over the last decade has been the incorporation of models of customer choice. Real world implementations of many of these models face the formidable stumbling block of simply identifying the `right' model of choice to use. Thus motivated, 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 information about these distributions), how may one predict revenues from offering a particular assortment of choices? We present a framework to answer such questions and design a number of tractable algorithms from a data and computational standpoint for the same. This paper thus takes a significant step towards `automating' the crucial task of choice model selection in the context of operational decision problems.