  Consider a consensus-driven multi-agent dynamic system. The interaction
range, which defines the set of neighbors for each agent, plays a key role in
influencing connectivity of the underlying network. In this paper, we assume
the system is under attack by a predator and explore the question of finding
the optimal interaction range that facilitates the most-efficient escape
trajectories for the group of agents. We find that for many cases of interest
the optimal interaction range is one that forces the network to break up into a
handful of disconnected graphs, each containing a subset of agents, thus
outperforming the two extreme cases corresponding to fully-connected and
fully-disconnected networks. In other words, the results indicate that some
connectivity among the agents is helpful because information is effectively
transmitted from the agents closest to the predator to others slightly farther
away, but also that too much connectivity can be detrimental to the agility of
the group, thus hampering efficient and rapid escape.
