[R] The Hive Mind is a Single Reinforcement Learning Agent: Deriving the Exact Equivalence Between Swarm Dynamics and Multi-Armed Bandits

TL;DR We prove a mathematical equivalence between collective decision-making via local imitation (such as honey bees choosing a nest site via the waggle dance) and single-agent Reinforcement Learning (RL) in a multi-armed bandit setting. Even when individual members have no learning capability and merely blindly imitate peers, the population distribution pi updates according to a macroscopic bandit RL algorithm that we call Maynard-Cross Learning (MCL), where N individuals act as N parallel environment rollouts with an effective per-sample step size of alpha = 1/N. Main intuition In nature, op