Optimal Auctions with Signaling Bidders

Abstract

The authors study optimal auctions in a symmetric private values setting, where bidders’ care about winning the object and a receiver’s inference about their type. They reestablish revenue equivalence when bidders’ signaling concerns are linear, and the auction makes participation observable via an entry fee. With convex signaling concerns, optimal auctions are fully transparent: every standard auction, which reveals all bids yields maximal revenue. With concave signaling concerns there is no general revenue ranking. The authors highlight a trade-off between maximizing revenue derived from signaling, and extracting information from bidders. Their methodology combines tools from mechanism design with tools from Bayesian persuasion.