student profile: Mr Emanuele Crosato


Thesis work

Thesis title: Information Dynamics and Guided Self-Organisation of Collective Motion

Supervisors: Mikhail PROKOPENKO , Joseph LIZIER

Thesis abstract:

Collective motion is the spontaneous emergence of coherent movement in a systems of self-propelled particles. It can be observed in animals moving in groups (e.g. schools of fish, flocks of birds and colonies of insects), in bacteria, in cells and even in non-living systems.
In Complex Systems science, it is particularly important to understand how computations unfold in space and time within the collective, giving rise to emergent complex behaviour. My research investigates the validity of information-theoretic measures to quantify information dynamics during collective motion, e.g. the primitive functions of universal computation: information storage, transfer and communication. These primitive functions have maximum capacity near order-chaos phase transitions, thus criticality is an important topic of the research.
An information-theoretic approach offers the opportunity to construct a solid, formal and domain generic theory of collective motion.
Such theory could open to new applications in areas as swarm intelligence, distributed robotic systems, self-assembling artificial tissues and bio-inspired materials. Engineering could also be impacted: several life-like properties of swarms, such as autonomy, adaptation, scalability and self-assembly are particularly desired for modern artificial systems, including traffic flow regulators, energy networks controllers and group of surveillance robots operating in the field.

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