Modelling collective motion based on the principle of agency

TL;DR

This paper introduces a novel agent-based model for collective motion where individuals learn local interaction rules through experience, demonstrated on locusts, and capable of reproducing known collective behavior equations without predefining interaction rules.

Contribution

The paper presents a learning-based agent model that derives local interaction rules from experience, eliminating the need for heuristic assumptions in collective motion modeling.

Findings

Model reproduces Fokker-Planck equation for collective motion

Agents learn appropriate local interactions without prior assumptions

Applicable to broader problems involving animal agency

Abstract

Collective motion is an intriguing phenomenon, especially considering that it arises from a set of simple rules governing local interactions between individuals. In theoretical models, these rules are normally \emph{assumed} to take a particular form, possibly constrained by heuristic arguments. We propose a new class of models, which describe the individuals as \emph{agents}, capable of deciding for themselves how to act and learning from their experiences. The local interaction rules do not need to be postulated in this model, since they \emph{emerge} from the learning process. We apply this ansatz to a concrete scenario involving marching locusts, in order to model the phenomenon of density-dependent alignment. We show that our learning agent-based model can account for a Fokker-Planck equation that describes the collective motion and, most notably, that the agents can learn the appropriate local interactions, requiring no strong previous assumptions on their form. These results suggest that learning agent-based models are a powerful tool for studying a broader class of problems involving collective motion and animal agency in general.