The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866916375967039488 |
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| author | Cesa-Bianchi, Nicolò Cesari, Tommaso Colomboni, Roberto Fusco, Federico Leonardi, Stefano |
| author_facet | Cesa-Bianchi, Nicolò Cesari, Tommaso Colomboni, Roberto Fusco, Federico Leonardi, Stefano |
| contents | We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder discovers the item's value only if the auction is won. Our main contribution is a complete characterization, up to logarithmic factors, of the minimax regret in terms of the auction's \emph{transparency}, which controls the amount of information on competing bids disclosed by the auctioneer at the end of each auction. Our results hold under different assumptions (stochastic, adversarial, and their smoothed variants) on the environment generating the bidder's valuations and competing bids. These minimax rates reveal how the interplay between transparency and the nature of the environment affects how fast one can learn to bid optimally in first-price auctions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_09478 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations Cesa-Bianchi, Nicolò Cesari, Tommaso Colomboni, Roberto Fusco, Federico Leonardi, Stefano Computer Science and Game Theory Data Structures and Algorithms Machine Learning We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder discovers the item's value only if the auction is won. Our main contribution is a complete characterization, up to logarithmic factors, of the minimax regret in terms of the auction's \emph{transparency}, which controls the amount of information on competing bids disclosed by the auctioneer at the end of each auction. Our results hold under different assumptions (stochastic, adversarial, and their smoothed variants) on the environment generating the bidder's valuations and competing bids. These minimax rates reveal how the interplay between transparency and the nature of the environment affects how fast one can learn to bid optimally in first-price auctions. |
| title | The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations |
| topic | Computer Science and Game Theory Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/2307.09478 |