Computational Modelling of Collective Decision Making

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.

One particularly important type of collective decision making is task allocation: how does a group allocate individuals to different tasks so that the goals of the groups are achieved. This is, of course, a central problem for any type of organisation, and most organisations try to solve that via centralised planning and coordination mechanisms. However, not all groups are coordinated centrally —— many act in a self-organised way, such that the group-level behaviour emerges from the actions of individuals, who only have limited, locally information on what is going on. 

Such self-organised behaviour is common in biology, 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 used behaviour. An ant colony must solve complex task allocation problems: a broad spectrum of tasks, from nest building, hygiene, and brood care to foraging, exploration, and defence needs to be addressed simultaneously so that the colony can thrive and survive. Task allocation in ant colonies is almost exclusively self-organised and even after many years of research it is still a fascinating puzzle how a colony manages to achieve its goals without any central control  in the presence of ever changing environmental conditions and internal colony demands.

The project investigates the mechanisms of self-organised task allocation in insect colonies. How do independently acting insects achieve a colony-wide optimal or adequate allocation of workforce? How is the required information communicated in the colony? 

It will also touch 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 this to have evolved, and the answer to this question may hold the ultimate key to understanding task allocation in depth. 

Beyond biology, the insights gained will also be a key to novel bio-inspired technologies, for example in autonomous multi-agent systems and swarm robotics.  

The project builds 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 work closely with biologists who provide experimental data to verify the theory, and a certain amount of interest in interdisciplinary work is required.