Sanjay Dominik Jena, Andrea Lodi, Claudio Sole
arXiv 8 Sep 2021 · Econometrics · publishedINFORMS journal on computing (2022) · 7 citations (OpenAlex)
arXiv:2109.03882 · PDF · DOI · OpenAlex · Extracted main text
The Random Utility Maximization model is by far the most adopted framework to estimate consumer choice behavior. However, behavioral economics has provided strong empirical evidence of irrational choice behavior, such as halo effects, that are incompatible with this framework. Models belonging to the Random Utility Maximization family may therefore not accurately capture such irrational behavior. Hence, more general choice models, overcoming such limitations, have been proposed. However, the flexibility of such models comes at the price of increased risk of overfitting. As such, estimating such models remains a challenge. In this work, we propose an estimation method for the recently proposed Generalized Stochastic Preference choice model, which subsumes the family of Random Utility Maximization models and is capable of capturing halo effects. Specifically, we show how to use partially-ranked preferences to efficiently model rational and irrational customer types from transaction data. Our estimation procedure is based on column generation, where relevant customer types are efficiently extracted by expanding a tree-like data structure containing the customer behaviors. Further, we propose a new dominance rule among customer types whose effect is to prioritize low orders of interactions among products. An extensive set of experiments assesses the predictive accuracy of the proposed approach. Our results show that accounting for irrational preferences can boost predictive accuracy by 12.5% on average, when tested on a real-world dataset from a large chain of grocery and drug stores.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jena, Sanjay Dominik, Andrea Lodi, Hugo Palmer, Claudio Sole (2020) A partially ranked choice model for large-scale data-driven assortment optimization self | 1.000 | 18 | 6 | 100% |
| 2 | Berbeglia, G (2018) The generalized stochastic preference choice model | 1.000 | 16 | 7 | 100% |
| 3 | Jagabathula, S., P. Rusmevichientong (2019) The limit of rationality in choice modeling: Formulation, computation, and implications | 1.000 | 9 | 4 | 100% |
| 4 | van Ryzin, G., G. Vulcano (2015) A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models | 1.000 | 8 | 5 | 100% |
| 5 | Bertsimas, D., V. Misic (2016) Data-driven assortment optimization | 1.000 | 5 | 4 | 100% |
| 6 | Berbeglia, G., A. Garassino, G. Vulcano (2018) A comparative empirical study of discrete choice models in retail operations | 1.000 | 5 | 4 | 100% |
| 7 | Maragheh, R. Y., A. Chronopoulou, J. M. Davis (2018) A customer choice model with halo effect | 1.000 | 5 | 3 | 100% |
| 8 | Farias, V. F., S. Jagabathula, D. Shah (2013) A nonparametric approach to modeling choice with limited data | 1.000 | 5 | 3 | 100% |
| 9 | Ragain, S., J. Ugander (2016) Pairwise choice markov chains | 0.928 | 4 | 3 | 100% |
| 10 | Chen, Y., V. Misic (2019) Decision forest: A nonparametric approach to modeling irrational choice | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 49 scored citations.