A unified duality-based approach to Bayesian mechanism design

We provide a duality framework for Bayesian Mechanism Design. Specifically, we show that the dual problem to revenue maximization is a search over virtual transformations. This approach yields a unified view of several recent breakthroughs in algorithmic mechanism design, and enables some new breakthroughs as well. In this talk, I'll:

1) Provide a brief overview of the challenges of multi-dimensional mechanism design.

2) Construct a duality framework to resolve these problems.

3) Apply the framework to derive the 6-approximation of Babaioff et al. for a single additive bidder.

4) State some newer results achieved through this framework.