Co-evolutionary Learning in the N-player Iterated Prisoner's Dilemma with a Structured Environment

Co-evolutionary learning is a process where a set of agents mutually adapt via strategic interactions. In this paper, we consider the ability of co-evolutionary learning to evolve cooperative strategies in structured populations using the N-player Iterated Prisoner's Dilemma (NIPD). To do so, we examine the effects of both fixed and random neighbourhood structures on the evolution of cooperative behaviour in a lattice-based NIPD model. Our main focus is to gain a deeper understanding on how co-evolutionary learning could work well in a spatially structured environment. The numerical experiments demonstrate that, while some recent studies have shown that neighbourhood structures encourage cooperation to emerge, the topological arrangement of the neighbourhood structures is an important factor that determines the level of cooperation.