Complexity Science

Complexity Science

Complexity & Networks science deals with collective dynamics. This is an interdisciplinary research area which aims to improve our understanding of complex systems, that is, systems composed of a large number of interacting parts.

Complexity & Networks science is currently undergoing rapid growth worldwide caused by the challenge to understand problems in economics, medicine, epidemiology, transport, neuroscience, pattern recognition, community detection, city development, crime, ecology, climate change, carbon cycle, and data reconstruction, to name a few. 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.

Our approach is to work through collaborative studies of concrete examples within specific fields. 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 economics, medicine, neuroscience, evolutionary biology, transport, and engineering.

We consider statistical mechanics as a mathematical methodology to understand how properties at systems level emerge from the level of the system-components and their interactions. This often involves the application of probability theory, and a number of mathematical techniques. Yet they can be applied to many different areas as mentioned, 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.

We have a vision of the complex challenge that we all face in the future. To achieve sustainability requires understanding the cause and effect of collective complex interactions. This enables us to make informed policies and measures to address issues.  We believe in collaboration and bring researchers together from across the college and from other universities in the UK and abroad.