  The idea of a collective intelligence behind the complex natural structures
built by organisms suggests that the organization of social networks is
selected so as to optimize problem-solving competence at the group-level. Here
we study the influence of the social network topology on the performance of a
group of agents whose task is to locate the global maxima of NK fitness
landscapes. Agents cooperate by broadcasting messages informing on their
fitness and use this information to imitate the fittest agent in their
influence networks. In the case those messages convey accurate information on
the proximity of the solution (i.e., for smooth fitness landscapes) we find
that high connectivity as well as centralization boost the group performance.
For rugged landscapes, however, these characteristics are beneficial for small
groups only. For large groups, it is advantageous to slow down the information
transmission through the network to avoid local maximum traps. Long-range links
and modularity have marginal effects on the performance of the group, except
for a very narrow region of the model parameters.
