Near-Optimal Multi-Unit Auctions with Ordered Bidders

TL;DR

This paper introduces prior-free multi-unit auction mechanisms with ordered bidders that achieve near-optimal revenue guarantees, applicable in both unlimited and limited supply scenarios, by comparing to a monotone price benchmark.

Contribution

It presents the first prior-free auctions with constant-factor approximation guarantees for ordered bidders in multi-unit settings.

Findings

Achieves constant-factor approximation to the monotone price benchmark.

Works in both unlimited and limited supply environments.

Provides auctions that are near-optimal across various Bayesian models.

Abstract

We construct prior-free auctions with constant-factor approximation guarantees with ordered bidders, in both unlimited and limited supply settings. We compare the expected revenue of our auctions on a bid vector to the monotone price benchmark, the maximum revenue that can be obtained from a bid vector using supply-respecting prices that are nonincreasing in the bidder ordering and bounded above by the second-highest bid. As a consequence, our auctions are simultaneously near-optimal in a wide range of Bayesian multi-unit environments.