Learning to bid in revenue-maximizing auctions
Computer Science and Game Theory
2019-05-15 v3
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.
Cite
@article{arxiv.1902.10427,
title = {Learning to bid in revenue-maximizing auctions},
author = {Thomas Nedelec and Noureddine El Karoui and Vianney Perchet},
journal= {arXiv preprint arXiv:1902.10427},
year = {2019}
}