In an ambitious endeavour to reshape economic forecasting, Professor Doyne Farmer and his team are developing a sophisticated economic model that aims to provide clarity amidst the complexities of the global economy. This initiative seeks to emulate the transformative impact of Google Maps on traffic navigation, potentially revolutionising how we approach economic planning and climate action.
The Vision of a Super Simulator
At the heart of Farmer’s ambition lies a groundbreaking “super simulator” that would offer a comprehensive model of the global economy, with each company represented as an individual entity. This model, which Farmer estimates could be built for $100 million, promises to deliver forecasts with unprecedented accuracy by accounting for the dynamic decision-making processes of thousands of economic actors.
Farmer, a complexity scientist based at Oxford University, draws from a rich interdisciplinary background that spans cosmology, chaos theory, and theoretical biology. His experience in successfully applying complex models to real-world scenarios, such as predicting market behaviours and analysing the impact of the Covid-19 pandemic on the UK economy, underpins his confidence in this new approach.
“This model could have changed the course of the 2008 financial crisis,” Farmer reflects, noting that had such a tool existed in 2006, it would likely have prompted preemptive measures that could have mitigated the ensuing $10 trillion loss.
Complexity Economics: A New Paradigm
Traditional economic models often falter, either oversimplifying reality or becoming too convoluted for practical application. Farmer posits that complexity economics offers a viable alternative, allowing for a more nuanced understanding of economic interactions. By modelling the behaviour of economic agents based on simplified rules—rather than the unrealistic assumption of perfect rationality—this approach can simulate millions of decision-makers, significantly enhancing predictive capabilities.
“For many years, mainstream economics has assumed that agents make flawless decisions based on complete information,” he explains. “In reality, this is far from true. Our models better reflect actual decision-making processes, which are often based on imitation or trial and error.”
Furthermore, the incorporation of machine learning allows these agents to adapt their strategies in response to changing market conditions, addressing the limitations of traditional equilibrium-based models that fail to account for the inherent fluctuations of real-world economies.
Tackling the Climate Crisis
With growing urgency, Farmer has turned his focus towards the existential threat of climate change. He asserts that the inadequacies of current economic models are most glaringly evident in their failure to accurately predict the trajectory of renewable energy development. “Our existing models have consistently underestimated the rapid decline in costs and the swift adoption of clean technologies,” he remarks.
The first phase of Farmer’s ambitious project will concentrate on the energy sector. The model will encompass the operations of 30,000 energy companies and their associated 160,000 assets, informed by a rich dataset spanning 25 years. This detailed representation will enable simulations of global energy supply and pricing, significantly improving the ability to chart a sustainable path toward a green economy.
A study conducted by Farmer and his colleagues in 2022 indicated that a rapid shift to clean energy could yield trillions of dollars in savings, underscoring the potential economic benefits of a well-informed transition.
Overcoming Historical Limitations
Farmer attributes the stagnation of economic modelling to a combination of rigid academic traditions and historical limitations in computational power. He critiques the dominance of a narrow perspective that emerged in the 1960s, which prioritised perfect rationality in economic actors. “Economics has become too insular,” he argues, advocating for a more interdisciplinary approach akin to that of physicists, who favour practical simulations over theoretical abstractions.
As Farmer accelerates his efforts to complete the complexity model within the next decade, he envisions a tool that could empower policymakers and business leaders to make more informed decisions, navigating both opportunities and challenges with greater precision.
Why it Matters
The development of this super simulator could mark a pivotal moment in economic planning, particularly in the context of the climate crisis. By providing clearer insights into the dynamics of the global economy, it holds the potential to catalyse a faster transition to sustainable energy practices, ultimately saving billions and reshaping the approach to both economic and environmental policy. In an era where the stakes have never been higher, Farmer’s vision could offer a beacon of hope for a more resilient and responsible global economy.
