Applications of $\alpha$-strongly regular distributions to Bayesian auctions

TL;DR

This paper explores how auction revenue guarantees based on MHR distributions extend to the broader class of alpha-strongly regular distributions, analyzing the impact on auction mechanisms and revenue with approximate distribution knowledge.

Contribution

It demonstrates that revenue bounds for auctions under MHR distributions extend gracefully to alpha-strongly regular distributions and examines mechanisms with approximate distribution knowledge.

Findings

Revenue bounds degrade gracefully for alpha-strongly regular distributions.

Auction mechanisms can be adapted to work with approximate distribution knowledge.

Expected revenue depends on the number of samples used to estimate distributions.

Abstract

Two classes of distributions that are widely used in the analysis of Bayesian auctions are the Monotone Hazard Rate (MHR) and Regular distributions. They can both be characterized in terms of the rate of change of the associated virtual value functions: for MHR distributions the condition is that for values $v < v'$, $\phi(v') - \phi(v) \ge v' - v$, and for regular distributions, $\phi(v') - \phi(v) \ge 0$. Cole and Roughgarden introduced the interpolating class of $\alpha$-Strongly Regular distributions ($\alpha$-SR distributions for short), for which $\phi(v') - \phi(v) \ge \alpha(v' - v)$, for $0 \le \alpha \le 1$. In this paper, we investigate five distinct auction settings for which good expected revenue bounds are known when the bidders' valuations are given by MHR distributions. In every case, we show that these bounds degrade gracefully when extended to $\alpha$-SR distributions. For four of these settings, the auction mechanism requires knowledge of these distribution(s) (in the other setting, the distributions are needed only to ensure good bounds on the expected revenue). In these cases we also investigate what happens when the distributions are known only approximately via samples, specifically how to modify the mechanisms so that they remain effective and how the expected revenue depends on the number of samples.