  There is only one technique for prior-free optimal mechanism design that
generalizes beyond the structurally benevolent setting of digital goods. This
technique uses random sampling to estimate the distribution of agent values and
then employs the Bayesian optimal mechanism for this estimated distribution on
the remaining players. Though quite general, even for digital goods, this
random sampling auction has a complicated analysis and is known to be
suboptimal. To overcome these issues we generalize the consensus technique from
Goldberg and Hartline (2003) to structurally rich environments that include,
e.g., single-minded combinatorial auctions.
