Complex systems show the capacity to aggregate information and to display
coordinated activity. In the case of social systems the interaction of
different individuals leads to the emergence of norms, trends in political
positions, opinions, cultural traits, and even scientific progress. Examples of
collective behavior can be observed in activities like the Wikipedia and Linux,
where individuals aggregate their knowledge for the benefit of the community,
and citizen science, where the potential of collectives to solve complex
problems is exploited. Here, we conducted an online experiment to investigate
the performance of a collective when solving a guessing problem in which each
actor is endowed with partial information and placed as the nodes of an
interaction network. We measure the performance of the collective in terms of
the temporal evolution of the accuracy, finding no statistical difference in
the performance for two classes of networks, regular lattices and random
networks. We also determine that a Bayesian description captures the behavior
pattern the individuals follow in aggregating information from neighbors to
make decisions. In comparison with other simple decision models, the strategy
followed by the players reveals a suboptimal performance of the collective. Our
contribution provides the basis for the micro-macro connection between
individual based descriptions and collective phenomena.Comment: 9 pages, 9 figure