  Problem solving (e.g., drug design, traffic engineering, software
development) by task forces represents a substantial portion of the economy of
developed countries. Here we use an agent-based model of cooperative problem
solving systems to study the influence of diversity on the performance of a
task force. We assume that agents cooperate by exchanging information on their
partial success and use that information to imitate the more successful agent
in the system -- the model. The agents differ only in their propensities to
copy the model. We find that, for easy tasks, the optimal organization is a
homogeneous system composed of agents with the highest possible copy
propensities. For difficult tasks, we find that diversity can prevent the
system from being trapped in sub-optimal solutions. However, when the system
size is adjusted to maximize performance the homogeneous systems outperform the
heterogeneous systems, i.e., for optimal performance, sameness should be
preferred to diversity.
