Emergent aggregation from collective foraging

Abstract:Collective behaviour in living systems is usually modelled as the outcome of a \emph{direct} social drive: agents are rewarded, or hard-wired, to align with or approach their neighbours. Here we show that aggregation can instead emerge from an \emph{indirect} objective. We let reinforcement learning foragers, initially performing a random walk, optimize their dynamics from a purely individual reward for finding replenishable targets, while perceiving only their conspecifics and never the targets themselves. As the visual range grows, the agents undergo a sharp crossover from an environment-tuned individual search to a scale-agnostic collective one, and this crossover coincides with the onset of spatial aggregation. Thus a collective phase arises as a by-product of optimal foraging, without any direct reward for grouping. A minimal analytical first-passage model reproduces the transition as a crossover between the two search strategies. Our results identify indirect, resource-driven reward as a generic route to emergent collective phenomena.

Statistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Adaptation and Self-Organizing Systems (nlin.AO); Biological Physics (physics.bio-ph)

arXiv:2608.28046 [cond-mat.stat-mech]

(or arXiv:2608.28046v1 [cond-mat.stat-mech] for this version)

https://doi.org/10.48550/arXiv.2608.28046 arXiv-issued DOI via DataCite (pending registration)