A Nonparametric Approach to Modeling Choice with Limited Data
Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the “right” model of choice to use. Because models of choice are inherently high-dimensional obj...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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2014
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| Online Access: | http://hdl.handle.net/1721.1/87677 https://orcid.org/0000-0002-5856-9246 https://orcid.org/0000-0003-0737-3259 |
| _version_ | 1811079596776357888 |
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| author | Farias, Vivek F. Jagabathula, Srikanth Shah, Devavrat |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Farias, Vivek F. Jagabathula, Srikanth Shah, Devavrat |
| author_sort | Farias, Vivek F. |
| collection | MIT |
| description | Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the “right” model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. 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? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward “automating” the crucial task of choice model selection. |
| first_indexed | 2024-09-23T11:17:32Z |
| format | Article |
| id | mit-1721.1/87677 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:17:32Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | mit-1721.1/876772022-10-01T02:36:07Z A Nonparametric Approach to Modeling Choice with Limited Data Farias, Vivek F. Jagabathula, Srikanth Shah, Devavrat Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Sloan School of Management Farias, Vivek F. Shah, Devavrat Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the “right” model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. 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? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward “automating” the crucial task of choice model selection. 2014-06-06T14:53:09Z 2014-06-06T14:53:09Z 2013-02 Article http://purl.org/eprint/type/JournalArticle 0025-1909 1526-5501 http://hdl.handle.net/1721.1/87677 Farias, Vivek F., Srikanth Jagabathula, and Devavrat Shah. “A Nonparametric Approach to Modeling Choice with Limited Data.” Management Science 59, no. 2 (February 2013): 305–322. https://orcid.org/0000-0002-5856-9246 https://orcid.org/0000-0003-0737-3259 en_US http://dx.doi.org/10.1287/mnsc.1120.1610 Management Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf arXiv |
| spellingShingle | Farias, Vivek F. Jagabathula, Srikanth Shah, Devavrat A Nonparametric Approach to Modeling Choice with Limited Data |
| title | A Nonparametric Approach to Modeling Choice with Limited Data |
| title_full | A Nonparametric Approach to Modeling Choice with Limited Data |
| title_fullStr | A Nonparametric Approach to Modeling Choice with Limited Data |
| title_full_unstemmed | A Nonparametric Approach to Modeling Choice with Limited Data |
| title_short | A Nonparametric Approach to Modeling Choice with Limited Data |
| title_sort | nonparametric approach to modeling choice with limited data |
| url | http://hdl.handle.net/1721.1/87677 https://orcid.org/0000-0002-5856-9246 https://orcid.org/0000-0003-0737-3259 |
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