Much of the classical work on algorithms for multiarmed bandits studies rewards
that are stationary over time. By contrast, we study multiarmed bandit (MAB)
games, where rewards obtained by an agent also depend on how many
other agents choose the same arm (as might be the case in many competitive or
cooperative scenarios). Such systems are naturally nonstationary due to the
interdependent evolution of agents, and in general MAB games can be intractable
to analyze using typical equilibrium concepts (such as perfect Bayesian
equilibrium). We introduce a general model of multiarmed bandit games, and study
a notion of equilibrium inspired by a large system approximation known as mean
field equilibrium. In such an equilibrium, the proportion of agents playing the
various arms, called the