Collective Homeostasis and Time-resolved Models of Self-organised Task Allocation

Abstract

One of the main factors behind the amazing ecological success of social insects is their ability to flexibly allocate the colony's workforce to all the different tasks it has to address. Insights into the self-organised task allocation methods used for this have given rise to the design of an important class of bio-inspired algorithms for network control, industrial optimisation, and other applications. The most widely used class of models for self-organised task allocation, which also forms the core of these algorithms, are response threshold models. We revisit response threshold models with new experiments using temperature regulation in bumblebee colonies as the model system. We show that standard response threshold models do not fit our experiments and present an alternative behavioural model. This captures a fine-grained, time resolved picture of task engagement, which enables us to investigate task allocation with a different set of statistical methods. Using these we show that our model fits the experiment well and explains its salient aspects. We compare the effectiveness of our model behaviour with that of response threshold models and demonstrate that it can lead to more efficient task management when demands fluctuate. Our results have the potential to provide a basis for the design of more efficient task allocation algorithms for dynamic environments and to elucidate important biological questions, such as the functional role of inter-individual variation.