Self-organization in a distributed coordination game through heuristic rules
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Date
2016Author
Agarwal, Shubham
Ghosh, Diptesh
Chakrabarti, Anindya
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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.
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