Empirical analysis and theoretical modelling of self-organized collective behaviour in three-dimensions: from insect swarms and bird flocks to new schemes of distributed coordination
Funding Body: European Research Council – ERC Starting Grant 2010
(2010-2015, EUR 1.1M)
Collective phenomena are well known in physics, being at the core of phase transitions in condensed matter. They have been deeply investigated, providing with conceptual and methodological tools that can be usefully applied also in other fields. In biology, for example, collective behaviour is widespread, occurring at several scales and levels of complexity. Animal groups – like insect swarms and bird flocks – are paradigmatic cases of emergent self-organization. There is no leader to guide individuals towards the common patterns. Rather, collective behaviour arises spontaneously as a consequence of the local interactions between individuals, much as it happens in ordering phenomena in condensed matter systems. Interestingly, group behaviour often has functional motivations, exhibiting collective skills that go beyond individual abilities. A crucial issue is therefore to understand how self-organization emerges in animal aggregations and how behavioural rules at the individual level regulate collective efficiency and group function.
There are several models of swarming and flocking, which produce collective behaviour starting from simple rules followed by the individuals. However, due to the difficulties of obtaining large-scale data in three dimensions (3D), these models are not based on empirical information and are not tested against quantitative observations. SWARM aims at providing new knowledge about self-organization and collective behaviour in 3D animal aggregations. To do that, SWARM will export concepts and methods from physics, and will integrate empirical work, data analysis and theoretical modelling. In particular,
- We will perform extensive field studies on insect swarms and bird flocks. Using stereoscopic photography and innovative algorithmic procedures based on statistical physics, we will reconstruct individual 3D positions and trajectories in groups of thousands individuals.
- Having data on large groups, we will perform a statistical characterization of collective behaviour; obtain information on the interactions between group members, and on the rules followed by individuals.
- We will develop new empirically based models of 3D animal collective behaviour, using multi-agent modeling and adapting numerical techniques from simulations on many-particle systems.
SWARM will have a strong impact in ethology and behavioural ecology. Dealing with several species, endowed with specific individual abilities and facing different collective tasks, will allow us to investigate the link between sensory/cognitive features and group behaviour. In animal groups individual strategies are selected by evolution to achieve functioning and overall efficiency at collective level. Empirically based information on these strategies will not only lead to more appropriate models, but also help to design new efficient schemes of distributed coordination, with important implications in control theory, artificial intelligence and cooperative robotics.