Optimisation of a honeybee-colony's energetics via social learning based on queuing delays
Natural selection shaped the foraging-related processes of honeybees in such a way that a colony can react to changing environmental conditions optimally. To investigate this complex dynamic social system, we developed a multi-agent model of the nectar flow inside and outside of a honeybee colony. In a honeybee colony, a temporal caste collects nectar in the environment. These foragers bring their harvest into the colony, where they unload their nectar loads to one or more storer bees. Our model predicts that a cohort of foragers, collecting nectar from a single nectar source, is able to detect changes in quality in other food sources they have never visited, via the nectar processing system of the colony. We identified two novel pathways of forager-to-forager communication. Foragers can gain information about changes in the nectar flow in the environment via changes in their mean waiting time for unloadings and the number of experienced multiple unloadings. This way two distinct groups of foragers that forage on different nectar sources and that never communicate directly can share information via a third cohort of worker bees. We show that this noisy and loosely knotted social network allows a colony to perform collective information processing, so that a single forager has all necessary information available to be able to 'tune' its social behaviour, like dancing or dance-following. This way the net nectar gain of the colony is increased.
multi-agent model social insects decentralised decision-making