Learning synchronization in networked complex systems using genetic algorithms

Being able to learn the synchronization behavior of a networked complex system has profound implications for studying and modeling many natural and artificial phenomena, such as the spread of diseases, emergence of social trends, as well as more effective agent based distillation models. In order to study the practicality of learning synchronization behavior, we utilize the spatial iterated prisoner´s dilemma game, which is played on a variety of complex network topologies. Players synchronize their interactions with other players, depending on the strategy they employ in the game. A genetic algorithm is used in order to attempt to learn the synchronization behavior of the players with respect to a target network. Our results indicate that it is impractical to learn the synchronization behavior on a network using only the strategy payoff information, and that more information is likely required to assist the learning process.