Revenue Maximization in Interdependent Value Settings

A fundamental assumption underlying much of mechanism design is that buyers know their values for the products they are interested in. We consider settings where agents receive signals related to their values from a joint distribution, and their estimated values are functions of their own as well as others’ signals. We consider revenue maximization in such settings and show that a variant of the VCG mechanism with admission control gives a constant approximation to the optimal expected revenue. 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.

This is joint work with Hu Fu and Anna Karlin.

Speaker Details

Shuchi Chawla is an Associate Professor of Computer Sciences at the University of Wisconsin-Madison. Her research interests lie in the design and analysis of algorithms, with a focus on optimization problems arising in economics. She is the recipient of an NSF CAREER award and a Sloan Foundation fellowship. She currently serves on the editorial boards of the ACM Transactions on Algorithms and the SIAM Journal on Discrete Mathematics.

Date:
Speakers:
Suchi Chawla
Affiliation:
University of Wisconsin-Madison
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Series: Microsoft Research Talks