  Strategic behavior is a fundamental problem in a variety of real-world
applications that require some form of peer assessment, such as peer grading of
homeworks, grant proposal review, conference peer review of scientific papers,
and peer assessment of employees in organizations. Since an individual's own
work is in competition with the submissions they are evaluating, they may
provide dishonest evaluations to increase the relative standing of their own
submission. This issue is typically addressed by partitioning the individuals
and assigning them to evaluate the work of only those from different subsets.
Although this method ensures strategyproofness, each submission may require a
different type of expertise for effective evaluation. In this paper, we focus
on finding an assignment of evaluators to submissions that maximizes assigned
evaluators' expertise subject to the constraint of strategyproofness. We
analyze the price of strategyproofness: that is, the amount of compromise on
the assigned evaluators' expertise required in order to get strategyproofness.
We establish several polynomial-time algorithms for strategyproof assignment
along with assignment-quality guarantees. Finally, we evaluate the methods on a
dataset from conference peer review.

Computer Science and Game Theory;Artificial Intelligence