Robust Stackelberg buyers in repeated auctions

TL;DR

This paper introduces robust strategies for bidders in repeated auctions with an initial exploration phase, demonstrating significant utility gains and robustness to various uncertainties, thereby enhancing bidder outcomes and seller revenue.

Contribution

It presents practical, simple, and robust bidding strategies in a two-stage auction process, extending prior work by addressing non-discounted buyers and robustness to errors.

Findings

Bidders can achieve large utility uplifts with the proposed strategies.

Strategies are robust to sampling errors, bidder approximation errors, and mechanism changes.

The seller's revenue is precisely quantified against non-discounted buyers.

Abstract

We consider the practical and classical setting where the seller is using an exploration stage to learn the value distributions of the bidders before running a revenue-maximizing auction in a exploitation phase. In this two-stage process, we exhibit practical, simple and robust strategies with large utility uplifts for the bidders. We quantify precisely the seller revenue against non-discounted buyers, complementing recent studies that had focused on impatient/heavily discounted buyers. We also prove the robustness of these shading strategies to sample approximation error of the seller, to bidder's approximation error of the competition and to possible change of the mechanisms.