  Approval-based multiwinner voting rules have recently received much attention
in the Computational Social Choice literature. Such rules aggregate approval
ballots and determine a winning committee of alternatives. To assess
effectiveness, we propose to employ new noise models that are specifically
tailored for approval votes and committees. These models take as input a ground
truth committee and return random approval votes to be thought of as noisy
estimates of the ground truth. A minimum robustness requirement for an
approval-based multiwinner voting rule is to return the ground truth when
applied to profiles with sufficiently many noisy votes. Our results indicate
that approval-based multiwinner voting is always robust to reasonable noise. We
further refine this finding by presenting a hierarchy of rules in terms of how
robust to noise they are.
