Signaling Schemes for Revenue Maximization

TL;DR

This paper investigates optimal signaling schemes in second-price auctions for probabilistic goods, focusing on revenue maximization, computational complexity, and the trade-off with social welfare in auction settings like display advertising.

Contribution

It introduces a framework for signaling in second-price auctions with probabilistic goods, analyzes computational hardness, and provides bounds on signals needed for optimal revenue and welfare preservation.

Findings

Computing optimal signaling schemes is generally hard.

Polynomial algorithms exist for specific cases.

At least half of the social welfare can be maintained with minimal signals.

Abstract

Signaling is an important topic in the study of asymmetric information in economic settings. In particular, the transparency of information available to a seller in an auction setting is a question of major interest. We introduce the study of signaling when conducting a second price auction of a probabilistic good whose actual instantiation is known to the auctioneer but not to the bidders. This framework can be used to model impressions selling in display advertising. We study the problem of computing a signaling scheme that maximizes the auctioneer's revenue in a Bayesian setting. While the general case is proved to be computationally hard, several cases of interest are shown to be polynomially solvable. In addition, we establish a tight bound on the minimum number of signals required to implement an optimal signaling scheme and show that at least half of the maximum social welfare can be preserved within such a scheme.