  Nowadays, several crowdsourcing projects exploit social choice methods for
computing an aggregate ranking of alternatives given individual rankings
provided by workers. Motivated by such systems, we consider a setting where
each worker is asked to rank a fixed (small) number of alternatives and, then,
a positional scoring rule is used to compute the aggregate ranking. Among the
apparently infinite such rules, what is the best one to use? To answer this
question, we assume that we have partial access to an underlying true ranking.
Then, the important optimization problem to be solved is to compute the
positional scoring rule whose outcome, when applied to the profile of
individual rankings, is as close as possible to the part of the underlying true
ranking we know. We study this fundamental problem from a theoretical viewpoint
and present positive and negative complexity results and, furthermore,
complement our theoretical findings with experiments on real-world and
synthetic data.
