Learning to bid in revenue-maximizing auctions

TL;DR

This paper develops a new gradient-based method for optimizing bidding strategies in revenue-maximizing auctions with a strategic bidder, leading to significant improvements over truthful bidding.

Contribution

It introduces a variational relaxation approach for bid optimization in prior-dependent auctions, applicable across various distributions and mechanisms.

Findings

Derived strategies outperform truthful bidding significantly

The approach is simple, general, and adaptable

Achieves massive uplifts in revenue compared to traditional strategies

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.