34 research outputs found
Adversarial learning for revenue-maximizing auctions
We introduce a new numerical framework to learn optimal bidding strategies in
repeated auctions when the seller uses past bids to optimize her mechanism.
Crucially, we do not assume that the bidders know what optimization mechanism
is used by the seller. We recover essentially all state-of-the-art analytical
results for the single-item framework derived previously in the setup where the
bidder knows the optimization mechanism used by the seller and extend our
approach to multi-item settings, in which no optimal shading strategies were
previously known. Our approach yields substantial increases in bidder utility
in all settings. Our approach also has a strong potential for practical usage
since it provides a simple way to optimize bidding strategies on modern
marketplaces where buyers face unknown data-driven mechanisms
Learning to bid in revenue-maximizing auctions
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