Learning to bid in revenue-maximizing auctions

被引:0
|
作者
Nedelec, Thomas [1 ,2 ]
El Karoui, Noureddine [1 ,3 ]
Perchet, Vianney [1 ,2 ]
机构
[1] Criteo AI Lab, Ann Arbor, MI 48104 USA
[2] ENS Paris Saclay, CMLA, Paris, France
[3] UC, Berkeley, CA USA
关键词
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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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页数:9
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