The central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to a given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control law on the least amount of information possible. We focus on achieving this goal using the field of game theory by designing local agent objective functions that guarantees (i) an equivalence between the resulting Nash equilibria and the optimizers of the system level objective and (ii) that the resulting game possesses an inherent structure that can be exploited in distributed learning.