Co-evolutionary learning in the N-player iterated prisoner's dilemma with a structured environment

Abstract

Co-evolutionary learning is a process where a set of agents mutually adapt via strategic interactions. In this paper, we consider the ability of coevolutionary 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 coop..