Our research group tries to decipher the rules that govern decision making in social groups, from animals that forage and hunt in groups to humans that work in teams. We also study groups of artificial agents and how these can make optimal or near-optimal decisions..
One particularly central type of collective decision making is task allocation: how does a group allocate its members to different tasks so that the goals of the groups are achieved. Organisations usually try to solve this problem by using centralised planning and coordination mechanisms. However, not all groups are coordinated centrally —— many act in a self-organised way, such that group-level behaviour emerges from individual actions.
This kind of self-organised behaviour is common in biological systems. It is the basis of how migrating birds steer their flocks, how fish schools hunt, and how ants swarm when they forage. Ants are indeed a prototypical model system for the study of self-organised behaviour. An ant colony must solve complex task allocation problems: a broad spectrum of tasks needs to be addressed simultaneously so that the colony can survive and thrive, from nest building, nest hygiene, and brood care to foraging, exploration, and defence. Task allocation in ant colonies is almost exclusively self-organised and even after decades of research it is still a fascinating puzzle how a colony manages to achieve its goals without any central control and despite the fact that environmental conditions are ever changing.
The project will investigate the mechanisms of self-organised task allocation in insect colonies. How do independently acting insects achieve a colony-wide adequate allocation of the workforce? How is the required information communicated in the colony?
It will also aim to shed light on one of the deepest questions: Why does a colony allow many of its workers to be free-riders, who apparently do not contribute any useful work to the colony but consume its shared resources? This is a most puzzling question —— typically, a large fraction of workers in social insect colonies are just “lazy”. There appears to be no good biological justification for why this should have evolved, and the answer to this question may hold the ultimate key to understanding task allocation in depth.
Beyond biology, the insights gained may provide the basis of novel bio-inspired technologies, for example in swarm robotics and autonomous multi-agent systems.
The project will build on well established computational and mathematical modelling techniques to achieve its aims. Departure points will be agent-based simulations, optimisation models, and evolutionary game theory. We will work closely with biologists who provide experimental data to verify the theory, and a certain amount of interest in interdisciplinary work is required.
Required: Interest in interdisciplinary work, strong mathematical background, reasonable coding skills, preferably experience with scientific computation, numerical methods.