Many economic models are based on perfectly rational agents, and don't take into account the high degree of unpredictability in human behaviour. At the IAS we aim to develop more realistic economic models to help policy-makers make better predictions.
In a perfectly rational world the economy is characterised by an average 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. Crises like these 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.
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 example psychology, sociology, physics and computational science.
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.
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.