A common theme within the Centre is the fact that much of the research exploits the richness of real-world data sets in order to study three fundamental questions in Complexity science:
- Emerging phenomena
- Adaptation and evolution
- Structure and stability of systems
Most projects undertaken are cross disciplinary and collaborative in nature, and a diverse range of research is currently being undertaken within this framework, so that there is ample opportunity to identify commonalities of theoretical and practical importance to our studies.
Within this construct, we divide our research into four key areas:
Fundamental complexity and networks
We study a range of different systems to find general principles behind the dynamics and the emergent structures we see. We use concepts and approaches from statistical mechanics and information theory to produce better tools to monitor systems and to improve our overall understanding of complex systems. Since complex systems consist of many parts, network theory is central to our activities and we develop new network tools for our applications.
Ecology, anatomy and biodiversity
Biological systems offer many examples of complexity in action. Ecology, especially from the evolutionary viewpoint, is a prototype example of emergence of hierarchies e.g. organisms, species, ecosystems. We also study emergent phenomena in biological systems such as out work on heart arrhythmia. Finally, we see complexity at work with many animal, the social behaviour in ants or sparrows, or the way the antlion digs a deadly trap made of fine sand grains.
People interact in many different ways yet we see clear social trends and preferences in society that are not easily explained from the level of the individual.
Working with experts in other fields, we develop models to explain features we find in data coming from a wide range of areas, from the behaviour of ants and sparrows to information flow in citation networks via questions coming out of archaeology.
Risk, economic and financial complexity
The application and use of complexity methods to economic systems, the financial industry and capital markets provides us with an enhanced perspective of the dynamics of the diffusion of risk across distinct market agents. Methods from complexity can address the weaknesses arising from the endogenous and static nature of risk management systems and prudential frameworks - where risk is quantified, compartmentalised, and managed in isolation which played a significant role in the emergence of financial and economic crises.