Approximate Revenue Maximization in Interdependent Value Settings
Computer Science and Game Theory
2014-08-20 v1 Data Structures and Algorithms
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.
Cite
@article{arxiv.1408.4424,
title = {Approximate Revenue Maximization in Interdependent Value Settings},
author = {Shuchi Chawla and Hu Fu and Anna Karlin},
journal= {arXiv preprint arXiv:1408.4424},
year = {2014}
}