Massively Parallel Model of Evolutionary Game Dynamics

To study the emergence of cooperative behavior, we have developed a scalable parallel framework. An important aspect is the amount of history that each agent can keep. When six memory steps are taken into account, the strategy space spans 2^4096 potential strategies, requiring large populations of agents. We introduce a multi-level decomposition method that allows us to exploit both multi-node and thread-level parallel scaling while minimizing the communication overhead. We present the following contributions: (1) A production run modeling up to six memory steps for populations consisting of up to 10^18 agents, making this study one of the largest yet undertaken. (2) Results exhibiting near perfect weak scaling and 82% strong scaling efficiency up to 262,144 processors of the IBM Blue Gene/P supercomputer and 16,384 processors of the Blue Gene/Q. Our framework marks an important step in the study of game dynamics with potential applications in fields ranging from biology to economics and sociology.