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Collective motion is an intriguing phenomenon, especially considering that itarises from a set of simple rules governing local interactions betweenindividuals. In theoretical models, these rules are normally \emph{assumed} totake a particular form, possibly constrained by heuristic arguments. We proposea new class of models, which describe the individuals as \emph{agents}, capableof deciding for themselves how to act and learning from their experiences. Thelocal 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 concretescenario involving marching locusts, in order to model the phenomenon ofdensity-dependent alignment. We show that our learning agent-based model canaccount 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 suggestthat learning agent-based models are a powerful tool for studying a broaderclass of problems involving collective motion and animal agency in general.

Modelling collective motion based on the principle of agency