After the total failure of traditional economic models to warn of the impending GFC, it’s clearly time to explore new methodologies for predicting catastrophic financial events and developing new policy responses.
The concept of agent-based modelling is becoming increasingly viewed by international governments and top scientists as that much-needed solution. An emerging computational simulation method that focuses on the interaction of many complex systems, it’s an approach that can better enable us to foresee the possibility of extreme events and to better understand the factors that drive them.
Essentially, agent-based modeling (or ABM) is like a flight simulator for managers and policy-makers. It’s an artificial world in which researchers can seek to explain or understand certain phenomenon by reproducing the actual mechanisms that make them happen. It may come as a surprise to learn that computer simulation games like SIMlife, SIMPark and Second Life are all based on this same methodology. The only difference in this instance is that we’re using the technology to better understand the economic environment with a view to informing policy and management decisions.
The fact that agent-based models are calibrated against the real world means we can ask specific questions of that model. For instance: how sensitive is it to different types of social and economic stimuli? How many kinds of realisations of the world are possible? Examples of how this modelling is currently being used can be found in the work of leading economists who are investigating why poor decisions were made in the lead-up to the GFC.
It’s important to acknowledge that realistic modelling of the world cannot be achieved via the use of traditional modelling systems. That’s because they’re all built on linear assumptions. However, the real world is characterised by its highly non-linear nature (think extreme events like tsunamis, volcanic eruptions and market crashes), so it’s clear a different approach is required. Now, thanks to enormous computing power, revolutionary programming capabilities and user-friendly modelling environments, we have ways of validating ABM against the real world. I believe it really does represent the way forward for the world’s policy-makers.