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.