Constrained Signaling in Auction Design

TL;DR

This paper studies auction design where the seller must use constrained signaling to inform buyers about goods, proposing algorithms based on submodular maximization and no-regret learning to optimize welfare under these constraints.

Contribution

It introduces algorithms for computing constrained signaling schemes in auctions, addressing practical communication and legal constraints.

Findings

Algorithms for constrained signaling schemes

Use of submodular maximization techniques

Application of no-regret learning methods

Abstract

We consider the problem of an auctioneer who faces the task of selling a good (drawn from a known distribution) to a set of buyers, when the auctioneer does not have the capacity to describe to the buyers the exact identity of the good that he is selling. Instead, he must come up with a constrained signalling scheme: a (non injective) mapping from goods to signals, that satisfies the constraints of his setting. For example, the auctioneer may be able to communicate only a bounded length message for each good, or he might be legally constrained in how he can advertise the item being sold. Each candidate signaling scheme induces an incomplete-information game among the buyers, and the goal of the auctioneer is to choose the signaling scheme and accompanying auction format that optimizes welfare. In this paper, we use techniques from submodular function maximization and no-regret learning to give algorithms for computing constrained signaling schemes for a variety of constrained signaling problems.