The evolution of cooperation in self-interested agent societies: a critical study

We study the phenomenon of evolution of cooperation in a society of self-interested agents using repeated games in graphs. A repeated game in a graph is a multiple round game, where, in each round, an agent gains payoff by playing a game with its neighbors and updates its action (state) by using the actions and/or payoffs of its neighbors. The interaction model between the agents is a two-player, two-action (cooperate and defect) Prisoner’s Dilemma (PD) game (a prototypical model for interaction between self-interested agents). The conventional wisdom is that the presence of network structure enhances cooperation and current models use multiagent simulation to show evolution of cooperation. However, these results are based on particular combination of interaction game, network model and state update rules (e.g., PD game on a grid with imitate your best neighbor rule leads to evolution of cooperation). The state-of-theart lacks a comprehensive picture of the dependence of the emergence of cooperation on model parameters like network topology, interaction game, state update rules and initial fraction of cooperators. We perform a thorough study of the phenomenon of evolution of cooperation using (a) a set of popular categories of networks, namely, grid, random networks, scale-free networks, and small-world networks and (b) a set of cognitively motivated update rules. Our simulation results show that the evolution of cooperation in networked systems is quite nuanced and depends on the combination of network type, update rules and the initial fraction of cooperating agents. We also provide an analysis to support our simulation results.