  In this paper we consider a distributed coordination game played by a large
number of agents with finite information sets, which characterizes emergence of
a single dominant attribute out of a large number of competitors. Formally, $N$
agents play a coordination game repeatedly which has exactly $N$ Nash
equilibria and all of the equilibria are equally preferred by the agents. The
problem is to select one equilibrium out of $N$ possible equilibria in the
least number of attempts. We propose a number of heuristic rules based on
reinforcement learning to solve the coordination problem. We see that the
agents self-organize into clusters with varying intensities depending on the
heuristic rule applied although all clusters but one are transitory in most
cases. Finally, we characterize a trade-off in terms of the time requirement to
achieve a degree of stability in strategies and the efficiency of such a
solution.
