  I consider a setting where reviewers offer very noisy scores for several
items for the selection of high-quality ones (e.g., peer review of large
conference proceedings), whereas the owner of these items knows the true
underlying scores but prefers not to provide this information. To address this
withholding of information, in this paper, I introduce the Isotonic Mechanism,
a simple and efficient approach to improving imprecise raw scores by leveraging
certain information that the owner is incentivized to provide. This mechanism
takes the ranking of the items from best to worst provided by the owner as
input, in addition to the raw scores provided by the reviewers. It reports the
adjusted scores for the items by solving a convex optimization problem. Under
certain conditions, I show that the owner's optimal strategy is to honestly
report the true ranking of the items to her best knowledge in order to maximize
the expected utility. Moreover, I prove that the adjusted scores provided by
this owner-assisted mechanism are significantly more accurate than the raw
scores provided by the reviewers. This paper concludes with several extensions
of the Isotonic Mechanism and some refinements of the mechanism for practical
consideration.
