Professor Doyne Farmer, a complexity scientist at Oxford University, has proposed building a super-simulator of the global economy for $100m (£80m). The model would individually represent every company, making realistic decisions that adapt as the economy changes, producing forecasts of unprecedented clarity.
Farmer, who previously beat the casino at roulette using a wearable computer and later founded a successful automated trading firm, believes the simulator could prevent financial crises and improve climate policy. He compares it to Google Maps for economic planning, offering intelligent answers to economic questions.
The 2008 financial crash cost the world about $10tn. Farmer argues that if the US central bank had access to such a model in 2006, it could have foreseen the disaster and acted to reduce losses. Even a 1% reduction in losses would repay the $100m investment a thousand times over.
Farmer's team has already built retrospective models of US real estate transactions and the UK economy's response to the Covid pandemic. Now, they are focusing on the climate crisis, where Farmer says traditional economic models have failed dramatically, consistently underestimating the speed and cost reductions of renewable energy.
The first step is a model of the global energy sector, encompassing 30,000 companies and 160,000 assets, based on 25 years of operational data. Each company is represented by a digital agent that simulates decision-making. The model aims to identify the best path to a green energy future, with a 2022 study suggesting a rapid transition could save trillions of dollars.
Complexity economics addresses two flaws in mainstream models: the assumption of perfect rationality and the inability to handle dynamic change. By modelling realistic, adaptive behaviour, Farmer hopes to provide transformative insights for economic and environmental policy.
