  We study the explore-exploit tradeoff in distributed cooperative
decision-making using the context of the multiarmed bandit (MAB) problem. For
the distributed cooperative MAB problem, we design the cooperative UCB
algorithm that comprises two interleaved distributed processes: (i) running
consensus algorithms for estimation of rewards, and (ii)
upper-confidence-bound-based heuristics for selection of arms. We rigorously
analyze the performance of the cooperative UCB algorithm and characterize the
influence of communication graph structure on the decision-making performance
of the group.
