  We provide experimental evaluation of a number of known and new algorithms
for approximate computation of Monroe's and Chamberlin-Courant's rules. Our
experiments, conducted both on real-life preference-aggregation data and on
synthetic data, show that even very simple and fast algorithms can in many
cases find near-perfect solutions. Our results confirm and complement very
recent theoretical analysis of Skowron et al., who have shown good lower bounds
on the quality of (some of) the algorithms that we study.
