Approximate Revenue Maximization in Interdependent Value Settings

TL;DR

This paper proposes a novel auction mechanism for interdependent value settings that guarantees a constant approximation to optimal revenue without distribution assumptions, relying on single-crossing and concavity conditions.

Contribution

It introduces a variant of the generalized VCG auction with reserve prices and random admission for interdependent values, achieving constant revenue approximation.

Findings

The auction achieves a constant approximation ratio in matroid environments.

No assumptions on signal distributions are needed.

The approach relies on single-crossing and concavity conditions.

Abstract

We study revenue maximization in settings where agents' values are interdependent: each agent receives a signal drawn from a correlated distribution and agents' values are functions of all of the signals. We introduce a variant of the generalized VCG auction with reserve prices and random admission, and show that this auction gives a constant approximation to the optimal expected revenue in matroid environments. Our results do not require any assumptions on the signal distributions, however, they require the value functions to satisfy a standard single-crossing property and a concavity-type condition.