Auction Design with a Bit of Information

Abstract

Consider a revenue-maximizing seller who can access a binary signal about two bidders` joint values. We explore what kind of information is most valuable to the seller by studying three classes of signals, each capturing a distinct dimension of bidders` values: their overall level (demand), their relative strength (ranking), and their dispersion while preserving bidder anonymity (competitiveness). We characterize the optimal signal and corresponding auction mechanism within each class, and find that competitiveness signals are particularly effective. Under certain regularity conditions, the optimal competitiveness signal yields at least as much revenue as any ranking signal or demand signal. Moreover, for signals that induce a monotone allocation, the optimal competitiveness signal yields at least as much revenue as any other binary signal.