Bayesian Auctions with Efficient Queries
Jing Chen, Bo Li, Yingkai Li, Pinyan Lu

TL;DR
This paper studies the query complexity in Bayesian auction mechanisms, showing that limited distribution access can still yield near-optimal revenue in certain auction settings, challenging the assumption of perfect distribution knowledge.
Contribution
It introduces the first analysis of query complexity in Bayesian mechanisms, providing tight bounds and efficient schemes for various auction types.
Findings
Logarithmic lower bounds on query complexity for constant revenue approximation
Efficient query schemes for single-item and multi-item auctions with limited distribution access
No need for regularity or monotone hazard rate assumptions in auction design
Abstract
Generating good revenue is one of the most important problems in Bayesian auction design, and many (approximately) optimal dominant-strategy incentive compatible (DSIC) Bayesian mechanisms have been constructed for various auction settings. However, most existing studies do not consider the complexity for the seller to carry out the mechanism. It is assumed that the seller knows "each single bit" of the distributions and is able to optimize perfectly based on the entire distributions. Unfortunately, this is a strong assumption and may not hold in reality: for example, when the value distributions have exponentially large supports or do not have succinct representations. In this work we consider, for the first time, the query complexity of Bayesian mechanisms. We only allow the seller to have limited oracle accesses to the players' value distributions, via quantile queries and value…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
