  We propose a simple model of network co-evolution in a game-dynamical system
of interacting agents that play repeated games with their neighbors, and adapt
their behaviors and network links based on the outcome of those games. The
adaptation is achieved through a simple reinforcement learning scheme. We show
that the collective evolution of such a system can be described by
appropriately defined replicator dynamics equations. In particular, we suggest
an appropriate factorization of the agents' strategies that results in a
coupled system of equations characterizing the evolution of both strategies and
network structure, and illustrate the framework on two simple examples.
