An agent-based framework of active matter with applications in biological and social systems

Abstract

Active matter, as other types of self-organizing systems, relies on the take-up of energy that can be used for different activities, such as active motion or structure formation. Here we provide an agent-based framework to model these processes at different levels of organization, physical, biological and social, using the same dynamic approach. Driving variables describe the take-up, storage and conversion of energy, whereas driven variables describe the energy consuming activities. The stochastic dynamics of both types of variables follow a modified Langevin equation. Additional nonlinear functions allow one to encode system-specific hypotheses about the relation between driving and driven variables. To demonstrate the applicability of this framework, we recast a number of existing models of Brownian agents and active Brownian particles. Specifically, active motion, clustering and self-wiring of networks based on chemotactic interactions, online communication and polarization of opinions based on emotional influence are discussed. The framework allows one to obtain critical parameters for active motion and the emergence of collective phenomena. This highlights the role of energy take-up and dissipation in obtaining different dynamic regimes.