In a perfectly rational world the economy is characterised by a representative agent (consumer, producer, investor, etc.), who is a perfect optimiser with rational expectations about the future. But the real world is much more complex, and socio-economic systems are highly influenced by people’s differing expectations and adaptive behaviours.

Panics, bubbles, crashes, herding. Financial and economic crises are nightmares for policy-makers and citizens alike. The 2008 fall of Lehman Brothers, for example, triggered a cascade of events that had a profound impact not just on banks and stock markets, but also on the incomes and well-being of people around the world.

Developing more realistic economic models

The goal of this research theme is to develop a more realistic financial-economic, agent-based, network model that policy-makers can use to predict or manage socio-economic crises. As a starting point, we are investigating whether complexity modelling tools that have been successful in the natural sciences can be applied to complex socio-economic systems.

Understanding the complexity of economic systems requires drawing on a wide range of disciplines including not only economics but also, for instance, psychology, sociology, physics and computational science.

Research Lead

Research Group Members

External Faculty Members

J. Doyne Farmer

Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, Professor in the Mathematical Institute at the University of Oxford, and an External Professor at the Santa Fe Institute.
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Stefan Thurner

Full professor for Science of Complex Systems at the Medical University of Vienna, where he chairs Section for Science of Complex Systems, Director of the Complexity Science Hub in Vienna.
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