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

Noise and fluctuations can undermine the efficiency of Majority Rule in Group Evaluation problems