Self organization in a distributed coordination game through heuristic rules
Abstract 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 pure strategy 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 versus the efficiency of such a solution.