Collective self-optimization of communicating active particles

TL;DR

This paper introduces a minimal model for active particles that respond to shared resources, demonstrating how many-body interactions enable collective self-optimization and pattern formation, with implications for active matter and biological systems.

Contribution

It develops a novel many-body interaction model for active agents responding to shared resources, showing how collective self-organization emerges through aperiodic pattern formation.

Findings

Many-body interactions induce self-organization.

Collective patterns optimize resource use.

Model applies to biological and synthetic active systems.

Abstract

The quest on how to collectively self-organize in order to maximize the survival chances of the members of a social group requires finding an optimal compromise between maximizing the well-being of an individual and that of the group. Here we develop a minimal model describing active individuals which consume or produce, and respond to a shared resource, such as the oxygen concentration for aerotactic bacteria or the temperature field for penguins, while urging for an optimal resource value. Notably, this model can be approximated by an attraction-repulsion model, but in general it features many-body interactions. While the former prevents some individuals from closely approaching the optimal value of the shared resource field, the collective many-body interactions induce aperiodic patterns, allowing the group to collectively self-optimize. Arguably, the proposed optimal-field-based collective interactions represent a generic concept at the interface of active matter physics, collective behavior, and microbiological chemotaxis. This concept might serve as a useful ingredient to optimize ensembles of synthetic active agents or to help unveiling aspects of the communication rules which certain social groups use to maximize their survival chances.