  Problem-solving competence at group level is influenced by the structure of
the social networks and so it may shed light on the organization patterns of
gregarious animals. Here we use an agent-based model to investigate whether the
ubiquity of hierarchical networks in nature could be explained as the result of
a selection pressure favoring problem-solving efficiency. The task of the
agents is to find the global maxima of NK fitness landscapes and the agents
cooperate by broadcasting messages informing on their fitness to the group.
This information is then used to imitate, with a certain probability, the
fittest agent in their influence networks. For rugged landscapes, we find that
the modular organization of the hierarchical network with its high degree of
clustering eases the escape from the local maxima, resulting in a superior
performance as compared with the scale-free and the random networks. The
optimal performance in a rugged landscape is achieved by letting the main hub
to be only slightly more propense to imitate the other agents than vice versa.
The performance is greatly harmed when the main hub carries out the search
independently of the rest of the group as well as when it compulsively imitates
the other agents.
