@COMMENT This file was generated by bib2html.pl <http://www.cs.cmu.edu/~pfr/misc_software/index.html#bib2html> version 0.90
@COMMENT written by Patrick Riley <http://www.cs.cmu.edu/~pfr>
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@article{OR21-Albert,
author={Michael Albert and Vincent Conitzer and Giuseppe Lopomo and Peter Stone},
title={Mechanism Design for Correlated Valuations: Efficient Methods for Revenue Maximization},
journal={Operations Research}, 
doi={10.1287/opre.2020.2092},
month="March",
year="2021",
abstract={
          Traditionally, much of the focus of the mechanism/auction
          design community has been on revenue optimal mechanisms for
          settings where bidders' valuations are independent. However,
          in settings where valuations are correlated, much stronger
          results are possible. For example, the entire surplus of
          efficient allocations can be extracted as revenue. These
          stronger results are true, in theory, under generic
          conditions on parameter values. However, in practice, they
          are rarely, if ever, implementable because of the stringent
          requirement that the mechanism designer knows the
          distribution of the bidders types exactly. In this work, we
          provide a computationally efficient and sample efficient
          method for designing mechanisms that can robustly handle
          imprecise estimates of the distribution over bidder
          valuations. This method guarantees that the selected
          mechanism will perform at least as well as any ex post
          mechanism with high probability. The mechanism also performs
          nearly optimally with sufficient information and
          correlation. Furthermore, we show that when the distribution
          is not known and must be estimated from samples from the
          true distribution, a sufficiently high degree of correlation
          is essential to implement optimal mechanisms. Finally, we
          demonstrate through simulations that this new mechanism
          design paradigm generates mechanisms that perform
          significantly better than traditional mechanism design
          techniques given sufficient samples.
},
}