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Approximate Revenue Maximization in Interdependent Value Settings by Chawla, Shuchi; Fu, Hu; Karlin, Anna is a scholarly article available to read on EtoBox.

What is Approximate Revenue Maximization in Interdependent Value Settings about?

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.

Author

Chawla, Shuchi; Fu, Hu; Karlin, Anna

Published

2014

Language

EN