  We consider control of heterogeneous players repeatedly playing an
anti-coordination network game. In an anti-coordination game, each player has
an incentive to differentiate its action from its neighbors. At each round of
play, players take actions according to a learning algorithm that mimics the
iterated elimination of strictly dominated strategies. We show that the
learning dynamics may fail to reach anti-coordination in certain scenarios. We
formulate an optimization problem with the objective to reach maximum
anti-coordination while minimizing the number of players to control. We
consider both static and dynamic control policy formulations. Relating the
problem to a minimum vertex cover problem on bipartite networks, we develop a
feasible dynamic policy that is efficient to compute. Solving for optimal
policies on benchmark networks show that the vertex cover based policy can be a
loose upper bound when there is a potential to make use of cascades caused by
the learning dynamics of uncontrolled players. We propose an algorithm that
finds feasible, though possibly suboptimal, policies by sequentially adding
players to control considering their cascade potential. Numerical experiments
on random networks show the cascade-based algorithm can lower the control
effort significantly compared to simpler control schemes.
