  Communication or influence networks are probably the most controllable of all
factors that are known to impact on the problem-solving capability of
task-forces. In the case connections are costly, it is necessary to implement a
policy to allocate them to the individuals. Here we use an agent-based model to
study how distinct allocation policies affect the performance of a group of
agents whose task is to find 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 neighborhoods. The
larger the influence neighborhood of an agent, the more links, and hence
information, the agent receives. We find that the elitist policy in which
agents with above-average fitness have their influence neighborhoods amplified,
whereas agents with below-average fitness have theirs deflated, is optimal for
smooth landscapes, provided the group size is not too small. For rugged
landscapes, however, the elitist policy can perform very poorly for certain
group sizes. In addition, we find that the egalitarian policy, in which the
size of the influence neighborhood is the same for all agents, is optimal for
both smooth and rugged landscapes in the case of small groups. The welfarist
policy, in which the actions of the elitist policy are reversed, is always
suboptimal, i.e., depending on the group size it is outperformed by either the
elitist or the egalitarian policies.
