Replicator Dynamics of Co-Evolving Networks
arXiv:1107.5354
Abstract
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.
AAAI Complex Adaptive System Symposium, 2010
References in corpus (7)
- Emergence of scaling in random networks
- Statistical physics of social dynamics
- Coevolutionary games - a mini review
- Structure of Growing Networks: Exact Solution of the Barabasi--Albert's Model
- Nonequilibrium phase transition in the coevolution of networks and opinions
- Complex cooperative networks from evolutionary preferential attachment
- Coupled Replicator Equations for the Dynamics of Learning in Multiagent Systems