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| Main Authors: | , , |
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| Format: | Preprint |
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2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2204.12723 |
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| _version_ | 1866909255766900736 |
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| author | Xie, Haitian Zhu, Ying Shishkin, Denis |
| author_facet | Xie, Haitian Zhu, Ying Shishkin, Denis |
| contents | The classic third degree price discrimination (3PD) model requires the knowledge of the distribution of buyer valuations and the covariate to set the price conditioned on the covariate. In terms of generating revenue, the classic result shows that 3PD is at least as good as uniform pricing. What if the seller has to set a price based only on a sample of observations from the underlying distribution? Is it still obvious that the seller should engage in 3PD? This paper sheds light on these fundamental questions. In particular, the comparison of the revenue performance between 3PD and uniform pricing is ambiguous overall when prices are set based on samples. This finding is in the nature of statistical learning under uncertainty: a curse of dimensionality, but also other small sample complications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_12723 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | On the limitations of data-based price discrimination Xie, Haitian Zhu, Ying Shishkin, Denis Computer Science and Game Theory Information Theory Machine Learning Theoretical Economics 62B10, 62R07, 91B24, 94A16 The classic third degree price discrimination (3PD) model requires the knowledge of the distribution of buyer valuations and the covariate to set the price conditioned on the covariate. In terms of generating revenue, the classic result shows that 3PD is at least as good as uniform pricing. What if the seller has to set a price based only on a sample of observations from the underlying distribution? Is it still obvious that the seller should engage in 3PD? This paper sheds light on these fundamental questions. In particular, the comparison of the revenue performance between 3PD and uniform pricing is ambiguous overall when prices are set based on samples. This finding is in the nature of statistical learning under uncertainty: a curse of dimensionality, but also other small sample complications. |
| title | On the limitations of data-based price discrimination |
| topic | Computer Science and Game Theory Information Theory Machine Learning Theoretical Economics 62B10, 62R07, 91B24, 94A16 |
| url | https://arxiv.org/abs/2204.12723 |