A Large-Scale 3D Simulation of Continuous Social Dynamics using Social Particle Swarm Model on Parallel Architecture

Cooperative relationships are important to maintain high adaptivity in our societies while their structures undergo constant change. The development of information technology made a great impact on our lives and the way we interact; Social Networking Services brought about high continuity in our social interactions in two senses: One is temporal continuity by keeping us constantly connected in time in a non-discrete way, as well as in parallel; through hand-held devices for example, numerous people interact within an extended time frame and mostly are connected at the same time. The second is continuity in the closeness of social relationships. We use various kinds of SNS with different topologies of interactions and communicate with others at different frequencies, which presents continuous variations in the degree of social closeness with others. In addition, the exponential expansion of these networks in the world and consequently, the scale of interactions is another indicator of the major effect of these networks on our social relationships, allowing for larger-scale communication by alleviating the physical boundaries and increasing the rate of interactions over the globe. Based on the concept of self-driven particle models, Nishimoto et al. constructed a simple computational model for investigating such continuously changing social relationships termed social particle swarm (SPS) model (Nishimoto et al., 2013). They assumed that individuals were in a twodimensional social or psychological space, and the proximity between two individuals reflects their social or psychological closeness. Interactions between individuals in the space is regarded as changes in social or psychological relationships based on game-theoretical relationship (e.g., prisoners dilemma) between strategies of individuals. Each particle has a strategy for the prisoners dilemma game, and gets closer to (away from) neighbors from which each particle obtains positive (negative) payoffs. They observed repeated occurrences of explosive dynamics that consisted of a formation of an altruistic cluster followed by its collapse with explosive dispersal of defective particles. The similar dynamics were observed both computationally and experimentally (see (Suzuki et al., 2018)). However, it is still not clear whether and how the significant increase in the scale of social networks, as discussed above, can bring about more diversity and demonstrate emerging patterns for higher-level interactions. Such hierarchical structures and interactions between communities are ubiquitous in real societies, and we expect that larger population sizes enable us to see novel emerging patterns both structural and behavioral. Our purpose is to construct and analyze a large-scale 3D simulation of continuous social dynamics using an extended SPS model on parallel architecture. We use a 3D space as a minimal approximation of a complex of social relationships that have much larger dimensions and comprise many channels of interaction, keeping sufficient spatial locality even when there is a large number of agents. Due to the demanding computational power of large multi-agent systems’ simulations, parallel architecture as a simulation platform best fits our goal. We devised an extension of the original SPS model to 3D space and implemented it using an open source framework for multi-agent simulation on GPGPU, called Flame GPU (Richmond and Chimeh, 2011). We conduct a preliminary analysis on the behavior of the population while varying the population size.