On Optimal Multi-Dimensional Mechanism Design
  We efficiently solve the optimal multi-dimensional mechanism design problem for independent bidders with arbitrary demand constraints when either the number of bidders is a constant or the number of items is a constant. In the first setting, we need that each bidder's values for the items are sampled from a possibly correlated, item-symmetric distribution, allowing different distributions for each bidder. In the second setting, we allow the values of each bidder for the items to be arbitrarily correlated, but assume that the distribution of bidder types is bidder-symmetric.   For all eps>0, we obtain an additive eps-approximation, when the value distributions are bounded, or a multiplicative (1-eps)-approximation when the value distributions are unbounded, but satisfy the Monotone Hazard Rate condition, covering a widely studied class of distributions in Economics. Our runtime is polynomial in max{#items,#bidders}, and not the size of the support of the joint distribution of all bidders' values for all items, which is typically exponential in both the number of items and the number of bidders. Our mechanisms are randomized, explicitly price bundles, and can sometimes accommodate budget constraints.   Our results are enabled by establishing several new tools and structural properties of Bayesian mechanisms. We provide a symmetrization technique turning any truthful mechanism into one that has the same revenue and respects all symmetries in the underlying value distributions. We also prove that item-symmetric mechanisms satisfy a natural monotonicity property which, unlike cyclic-monotonicity, can be harnessed algorithmically. Finally, we provide a technique that turns any given eps-BIC mechanism (i.e. one where incentive constraints are violated by eps) into a truly-BIC mechanism at the cost of O(sqrt{eps}) revenue. We expect our tools to be used beyond the settings we consider here. 
Constantinos Daskalakis and S. Matthew Weinberg
cs.GT