Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917033883467776 |
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| author | Potfer, Marius Perchet, Vianney |
| author_facet | Potfer, Marius Perchet, Vianney |
| contents | Repeated multi-unit auctions, where a seller allocates multiple identical items over many rounds, are common mechanisms in electricity markets and treasury auctions. We compare the two predominant formats: uniform-price and discriminatory auctions, focusing on the perspective of a single bidder learning to bid against stochastic adversaries. We characterize the learning difficulty in each format, showing that the regret scales similarly for both auction formats under both full-information and bandit feedback, as $\tildeΘ ( \sqrt{T} )$ and $\tildeΘ ( T^{2/3} )$, respectively. However, analysis beyond worst-case regret reveals structural differences: uniform-price auctions may admit faster learning rates, with regret scaling as $\tildeΘ ( \sqrt{T} )$ in settings where discriminatory auctions remain at $\tildeΘ ( T^{2/3} )$. Finally, we provide a specific analysis for auctions in which the other participants are symmetric and have unit-demand, and show that in these instances, a similar regret rate separation appears. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_19591 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization Potfer, Marius Perchet, Vianney Computer Science and Game Theory Machine Learning Repeated multi-unit auctions, where a seller allocates multiple identical items over many rounds, are common mechanisms in electricity markets and treasury auctions. We compare the two predominant formats: uniform-price and discriminatory auctions, focusing on the perspective of a single bidder learning to bid against stochastic adversaries. We characterize the learning difficulty in each format, showing that the regret scales similarly for both auction formats under both full-information and bandit feedback, as $\tildeΘ ( \sqrt{T} )$ and $\tildeΘ ( T^{2/3} )$, respectively. However, analysis beyond worst-case regret reveals structural differences: uniform-price auctions may admit faster learning rates, with regret scaling as $\tildeΘ ( \sqrt{T} )$ in settings where discriminatory auctions remain at $\tildeΘ ( T^{2/3} )$. Finally, we provide a specific analysis for auctions in which the other participants are symmetric and have unit-demand, and show that in these instances, a similar regret rate separation appears. |
| title | Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization |
| topic | Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2510.19591 |