The CABDyN Complexity Network was established in 2003. We engage in research on complex systems and networks, with a particular focus on the social, economic, financial, communication and infrastructural networks that underpin most of modern life.

The acronym CABDyN stands for Complex Agent-Based Dynamic Networks, and reflects some of the techniques such as complex network analysis and agent-based modelling that we typically use to understand these systems.


Our aim is to define shared research questions and transferable and generalisable methods and techniques to enable a better understanding of the dynamic and functional properties of network structures encountered in different contexts and disciplines.


We develop new statistical measures to characterise network structures and properties, so the key features of incompletely mapped or noisy empirical networks can be summarised efficiently.

  • The INET Oxford Complexity Economics Programme applies insights and methodologies from complex systems theory to develop a deeper understanding of economic phenomena. The group utilises a range approaches including agent-based modelling, network theory, statistical physics, evolutionary theory, information theory, and non-linear dynamic modelling.  
  • Forecasting Financial Crises. The focus is to significantly improve our understanding of systemic risk in financial markets and if possible to forecast global financial instabilities. The group aims to provide a novel, integrated, and network-oriented approach to understanding financial crises.  

  • Supply Chain Mapping. Understanding firms’ supply chains has become both a key issue for business research, and a central issue for corporations. Firms do not compete as islands of activity, but in complex webs of other organisations. Firms’ fates are tied up with those of their supply chain partners.   

  • Complexity, Resilience and Risk. Growing complexity lies at the heart of many of the challenges that society faces, with important implications for systemic resilience and risk. This research cluster use an interdisciplinary perspective and methods from complexity science to address key questions.  

  • Self-organising Adaptive Technology Underlying Resilient Networks. Large-scale service-based and ICT networks are increasingly the basis for critical UK infrastructure and economic activity. However, there is an urgent need to develop and extend the underlying science and engineering principles required to support such complex systems.