{"ID":2851732,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19591","arxiv_id":"2510.19591","title":"Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.19591","url_pdf":"https://arxiv.org/pdf/2510.19591v1","authors":"[\"Marius Potfer\",\"Vianney Perchet\"]","published":"2025-10-22T13:41:27Z","proceeding":"cs.GT","tasks":"[\"cs.GT\",\"stat.ML\"]","methods":"[]","has_code":false}