Multiple strategy competition among structured populations

Conflicts and collaboration among agents are commonly seen in social activities where multi-agent systems are involved in. Nowadays the evolutionary game theory can effectively model the interest conflicts among agent in the swarm, among which the iterated Prisoner's Dilemma game offers a well-studied metaphor with which to explore theoretically the evolution of cooperation. The diversity of individual choices is realized by four strategies here: always-defect, always-cooperate, tit-for-tat and win-stay-lose-shift. Our attempt here is to understand the competition and collaboration behaviors by studying the evolutionary stability of these four strategies. After parameterizing the corresponding payoff matrices, strategy updating rules related with the strategies will drive the population dynamics to evolve over time. We used individual-based simulations to investigate how the individual heterogeneity influences the evolutionary emergence and subsequent stability of cooperation in a structured population. Results on complex networks provide us a clear information about the strategy competition results and the influencing factors.