Evolving leraning rules and emergence of cooperation in spatial prisioner´s dilemma

Abstract

In the evolutionary Prisoner’s dilemma (PD) game, agent splay with each other and update their strategies in every generation according to some microscopic dynamical rule. Inits spatial version, agents do not play with every other but, instead, interactonly with their neighbours, thus mimicking the existing of a social orcontactnetwork that defines who interacts with whom. In this work, we explore evolutionary, spatial PD systems consisting of two types of agents, each with a certain update (reproduction, learning) rule. We investigate two different scenarios: in the first case, update rules remain fixed for theen tire evolution of the system; in the second case, agents update both strategy and update rule in every generation. We show that in a well mixed population the evolutionary out come is always full defection. We subsequently focus on two strategy competition with nearest neighbour interactions on the contact network and synchronised update of strategies. Our results show that, for an important range of the parameter sof the game, the final state of the system is largely different from that a rising from the usual setup of a single, fixed dynamical rule. Furthermore, the results are also very different if update rules are fixed or evolve with the strategies. In these respect, we have studied representative update rules, finding that some of them may become extinct while others prevail. We describe the new and rich variety of final out comes that arise from this coevolutionary dynamics. We include examples of other neighbourhoods and asynchronous updating that confirm the robustness of our conclusions. Our results pave the way to an evolutionary rationale for modelling social interactions through game theory with a preferred set of update rules.