A new mathematical analysis establishes a surprising equivalence between collective decision-making in bee swarms and reinforcement learning.
Researchers Karthik Soma, Yann Bouteiller, Heiko Hamann and Giovanni Beltrame demonstrate that a population following simple imitation rules can behave mathematically like a single learning agent evaluating multiple alternatives simultaneously. Their analysis uses a simplified model of honeybee nest-site selection, in which scouts advertise candidate locations through waggle dances and other bees imitate successful discoveries. The resulting collective learning process corresponds to a reinforcement-learning algorithm the researchers call Maynard-Cross Learning.
https://link.springer.com/article/10.1007/s11721-026-00264-7
