Complexity and Networks

Complexity Science is transdisciplinary. The aim is to improve our understanding of how complex systems consisting of large numbers of interacting components work through collaborative studies of concrete examples within specific fields.

Complexity Science is currently undergoing a rapid growth worldwide caused by the challenge to understand problems in, e.g., neuroscience, communication and transport networks, engineering, biology and sociology. Despite the diverse nature of such systems it is being discovered that commonalities do indeed exist.  For example the interactions among the constituents often give rise to emergent hierarchical network structures that undergo intermittent evolution in time. 

At Imperial, quantitative modelling using methods from a wide range of mathematics, statistical mechanics and network theory is applied in collaborative efforts within projects in neuroscience, evolutionary biology, medicine, engineering and sociology producing new insights of fundamental importance and of immediate relevance to applications.

The coherent combination of applied science will, when performed in collaboration with research in fundamental aspects of complex systems theory, provide the ideal forum to move complexity science beyond the confines of idealised modelling. Simple laboratory experiments are typically too idealised to be able to address the most interesting research questions in complexity science. Researchers therefore have to resort to data obtained from real systems. Hence theoretical investigations in complexity science should be carried out in close collaboration with researchers investigating a variety of real systems to make it possible to identify commonalities of theoretical practical importance.

The Complexity and Networks programme brings together researchers from across the college and from other universities in UK and abroad. 

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