  A continuous-time Markov process is proposed to analyze how a group of humans
solves a complex task, consisting in the search of the optimal set of decisions
on a fitness landscape. Individuals change their opinions driven by two
different forces: (i) the self-interest, which pushes them to increase their
own fitness values, and (ii) the social interactions, which push individuals to
reduce the diversity of their opinions in order to reach consensus. Results
show that the performance of the group is strongly affected by the strength of
social interactions and by the level of knowledge of the individuals.
Increasing the strength of social interactions improves the performance of the
team. However, too strong social interactions slow down the search of the
optimal solution and worsen the performance of the group. In particular, we
find that the threshold value of the social interaction strength, which leads
to the emergence of a superior intelligence of the group, is just the critical
threshold at which the consensus among the members sets in. We also prove that
a moderate level of knowledge is already enough to guarantee high performance
of the group in making decisions.

Multiagent Systems;Artificial Intelligence;Adaptation and Self-Organizing Systems;Physics and Society