Overview
A growing number of economists and social scientists view the economy as a ‘complex adaptive system’ - a distributed network of dynamically interacting, heterogeneous agents, whose behaviours, strategies and relationships evolve over time. Under such a view the economy is more akin to an ecosystem, the brain, or the internet than to the mechanistic models of traditional theory.
The Complexity Economics Programme is applying leading-edge tools from complex systems science to generate new insights into a wide range of economic problems. The group utilises methods such as network analysis and agent-based computer simulations to incorporate realistic portrayals of human behaviour and institutions in its models and better understand how economic systems evolve dynamically over time. This approach enables researchers to see how macro patterns in the economy, such as financial crises, emerge out of micro level behaviours, interactions, and structures. The group is applying these techniques to issues including financial system stability, innovation, and growth, and is also collaborating with the Economics, Inequality & Opportunity programme on inequality and employment, and the Economics of Sustainability programme on issues related to sustainable growth.
The group includes scholars from a number of disciplines, including economics, maths, physics, geography and computer science. The programme is partnered with Oxford’s School of Geography, Mathematical Institute, Department of Computer Science and the Saïd Business School. The programme’s work has generated significant interest from policymakers. Interactions with policymakers include the Bank of England, European Central Bank, New York Federal Reserve, Deutsche Bundesbank, European Commission, IMF, OECD, UK HM Treasury, UK Department for Business, Energy and Industrial Strategy, US Department of Energy, US Senate, and various policy think tanks in the US and UK.
If you would like to keep up to date with our publications, news and events, follow @inet-complexity.bsky.social on BlueSky, @INET_Complexity on X (formerly Twitter) or contact complexity@inet.ox.ac.uk for more information.
