Social Particle Swarm: Explosive particle dynamics based on cooperative/defective forces

We propose that self-driven particles systems (SDP) can provide a tool for the analysis of social dynamics, and propose a new SDP-based model for the study of social dynamics: Social Particle Swarm. Each particle represents an individual member of a society (the swarm) and has a strategy value for a state. Each particle's movement is controlled by payoff values, that are derived from the interaction of its own strategy and the strategies of its neighbors using a payoff matrix that represents the nature of their social interaction, taking into account the distance between them. Specifically, a particle will approach others that provide it benefits and distance itself from particles from whom it incurs losses. In this work, we adopt a payoff matrix that represents Prisoner's Dilemma and conducted experiments to analyze the social dynamics displayed by the movement of particles. We classified the states of the system into three quasi-stable states and focused on the most remarkable state among them. We discovered in this state the emergence of a cyclic process composed of the formation of an altruistic cluster followed by its collapse with explosive dispersal of particles. The results showed remarkable parallels with recent findings from studies on adaptive network dynamics. We also observed that the diversity of the tendency to cooperate among individuals plays an important role for the occurrence of this cyclic dynamic.