Robustly Optimal Auctions with Unknown Resale Opportunities

Abstract

The standard revenue-maximizing auction discriminates against a priori stronger bidders so as to reduce their information rents. We show that such discrimination is no longer optimal when the auction's winner may resell to another bidder, and the auctioneer has non-Bayesian uncertainty about such resale opportunities. We identify a "worst-case" resale scenario, in which bidders' values become publicly known after the auction and losing bidders compete Bertrand-style to buy the object from the winner. With this form of resale, misallocation no longer reduces the information rents of the high-value bidder, as he could still secure the same rents by buying the object in resale. Under regularity assumptions, we show that revenue is maximized by a version of the Vickrey auction with bidder-specific reserve prices, first proposed by Ausubel and Cramton (2004). The proof of optimality involves constructing Lagrange multipliers on a double continuum of binding non-local incentive constraints.

Keywords: auctions with resale; duality in auction design; non-local incentive constraints; robust revenue maximization; ACV auction; worst-case

JEL Classification: D44, D82