  We consider the issue of strategic behaviour in various peer-assessment
tasks, including peer grading of exams or homeworks and peer review in hiring
or promotions. When a peer-assessment task is competitive (e.g., when students
are graded on a curve), agents may be incentivized to misreport evaluations in
order to improve their own final standing. Our focus is on designing methods
for detection of such manipulations. Specifically, we consider a setting in
which agents evaluate a subset of their peers and output rankings that are
later aggregated to form a final ordering. In this paper, we investigate a
statistical framework for this problem and design a principled test for
detecting strategic behaviour. We prove that our test has strong false alarm
guarantees and evaluate its detection ability in practical settings. For this,
we design and execute an experiment that elicits strategic behaviour from
subjects and release a dataset of patterns of strategic behaviour that may be
of independent interest. We then use the collected data to conduct a series of
real and semi-synthetic evaluations that demonstrate a strong detection power
of our test.

Multiagent Systems;Computer Science and Game Theory;Machine Learning