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

TL;DR

This paper introduces an agent-based modeling framework for active matter systems across physical, biological, and social domains, capturing energy dynamics and collective behaviors using stochastic equations.

Contribution

It presents a unified dynamic approach to model active matter at multiple organizational levels, incorporating energy take-up, storage, and conversion processes.

Findings

Framework recasts existing models of active particles and agents

Identifies critical parameters for collective motion and phenomena

Demonstrates applicability across diverse systems

Abstract

Active matter, as other types of self-organizing systems, relies on the take-up of energy that can be used for different actions, 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 non-linear functions allow 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 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.