Giovanni Beltrame

When honeybees choose a nest site, individuals blindly switch to the option advertised most strongly by neighbors, and this local imitation is shown to be equivalent, at the group level, to a single online reinforcement-learning agent solving a multi-armed bandit. That macro-agent updates nest-site probabilities by a rule named Maynard-Cross Learning, with each scout acting as one parallel action-reward sample. The derivation covers the weighted voter model, a simplified description, and simulations indicate roughly 500 scouts suffice to track the macro-dynamics and converge on the best site.

When honeybees choose a nest site, individuals blindly switch to the option advertised most strongly by neighbors, and this local imitation is shown to be equivalent, at the group level, to a single online reinforcement-learning agent solving a multi-armed bandit. That macro-agent updates nest-site probabilities by a rule named Maynard-Cross Learning, with each scout acting as one parallel action-reward sample. The derivation covers the weighted voter model, a simplified description, and simulations indicate roughly 500 scouts suffice to track the macro-dynamics and converge on the best site.