  In linear models of consensus dynamics, the state of the various agents
converges to a value which is a convex combination of the agents' initial
states. We call it democratic if in the large scale limit (number of agents
going to infinity) the vector of convex weights converges to 0 uniformly.
  Democracy is a relevant property which naturally shows up when we deal with
opinion dynamic models and cooperative algorithms such as consensus over a
network: it says that each agent's measure/opinion is going to play a
negligeable role in the asymptotic behavior of the global system. It can be
seen as a relaxation of average consensus, where all agents have exactly the
same weight in the final value, which becomes negligible for a large number of
agents.
