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In many animal species, individuals move in groups as they perform seasonal migrations, travel to food sources and return to safe havens, often over considerable distances. The movement of these groups is commonly self-organised, arising from simple local interactions between individuals rather than from a hierarchical command centre. As such, moving groups are examples of biological, self-organised complex systems. The units of a complex system, be they nerve cells in a brain, ants in an ant colony or firms in an economy, interact with each other to produce extraordinary emergent properties of the whole system that are not predictable from the behaviour of the units themselves. Examples of such emergent properties include consciousness, a bull run on the stock market, or the impressive evasive manoeuvres of fish schools when in the presence of predators. Self-organised group movement is not restricted to relatively "simple" creatures such as insect swarms or schools of fish, but may even include "intelligent" species like us. One of the most disastrous examples of collective human group movement is crowd stampede induced by panic, often leading to fatalities as people are crushed or trampled.

The key to understanding group behaviour and its manifestations, such as crowd panic or insect swarms, is to understand the nature of the local rules that individuals in the group follow, and to formalise these in simulation models. In this project I investigate the fundamental issue of how a self-organised moving group "decides" where it is going. My main focus is on honeybee swarms, but hopefully this will soon be expanded to include locusts and Mormon crickets in collaboration with Steve Simpson (School of Biological Sciences, University of Sydney) and international collaborators