Learning to bid in revenue-maximizing auctions

Thomas Nedelec; Noureddine El Karoui; Vianney Perchet

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arXiv · 1902.10427

Learning to bid in revenue-maximizing auctions

Thomas Nedelec·Noureddine El Karoui·Vianney Perchet

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Abstract

We consider the problem of the optimization of bidding strategies in prior-dependent revenue-maximizing auctions, when the seller fixes the reserve prices based on the bid distributions. Our study is done in the setting where one bidder is strategic. Using a variational approach, we study the complexity of the original objective and we introduce a relaxation of the objective functional in order to use gradient descent methods. Our approach is simple, general and can be applied to various value distributions and revenue-maximizing mechanisms. The new strategies we derive yield massive uplifts compared to the traditional truthfully bidding strategy.

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BibTeX RIS

Thomas Nedelec, Noureddine El Karoui, Vianney Perchet. 2019-05-14. Learning to bid in revenue-maximizing auctions. https://arxiv.org/abs/1902.10427

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