  Crowdsourcing is a mechanism by means of which groups of people are able to
execute a task by sharing ideas, efforts and resources. Thanks to the online
technologies, crowdsourcing has become in the last decade an even more utilized
process in different and diverse fields. An instance of such process is the
so-called "label aggregation problem": in practice, it is the evaluation of an
item by groups of agents, where each agent gives its own judgment of it.
Starting from the individual evaluations of their members, how can the groups
give their global assessment? In this work, by means of a game-theoretical,
evolutionary approach, we show that in most cases the majority rule (the group
evaluation is the evaluation of the majority of its members) is still the best
way to get a reliable group evaluation, even when the agents are not the best
experts of the topic at stake; on the other hand, we also show that noise
(i.e., fortuitous errors, misunderstanding, or every possible source of
non-deterministic outcomes) can undermine the efficiency of the procedure in
non-trivial situations. Therefore, in order to make the process as reliable as
possible, the presence of noise and its effects should be carefully taken into
account.
