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.
Related
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BibTeX
@misc{munozgil2026emergent,
title = {{Emergent aggregation from collective foraging}},
author = {Gorka Muñoz-Gil and Andrea López-Incera and Vide Ramsten and Giovanni Volpe and Thomas Müller and Hans J. Briegel},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28046},
eprint = {2608.28046},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28046},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Emergent aggregation from collective foraging
AU - Gorka Muñoz-Gil
AU - Andrea López-Incera
AU - Vide Ramsten
AU - Giovanni Volpe
AU - Thomas Müller
AU - Hans J. Briegel
PY - 2026
DA - 2026/08/28
PB - arXiv
DO - 10.48550/arxiv.2608.28046
UR - https://arxiv.org/abs/2608.28046
AB - 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.
ER -