8,575 research outputs found
Optimal auctions through deep learning
Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30-40 years of intense research the problem remains unsolved for seemingly simple multibidder, multi-item settings. In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions. We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard pipelines. We prove generalization bounds and present extensive experiments, recovering essentially all known analytical solutions for multi-item settings, and obtaining novel mechanisms for settings in which the optimal mechanism is unknown
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
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