For decades, mainstream economic modeling has struggled to accurately predict major crises – from the 2008 financial collapse to the ongoing climate emergency. Now, a veteran scientist and former market-beater is proposing a radical solution: a super-simulator that models every company on Earth, making realistic decisions as the economy changes. The cost? Roughly $100 million.
The Failure of Traditional Models
Traditional economic models operate under assumptions that simply don’t hold in the real world. They either oversimplify reality to the point of uselessness or become computationally impossible when scaled to represent the full complexity of global markets. This has led to consistent failures in forecasting, costing trillions in financial losses and hindering effective climate policy.
The problem isn’t just accuracy; it’s the fundamental approach. Existing models assume perfect rationality, meaning agents (companies, consumers) know everything and make optimal decisions. This is unrealistic. Real-world actors learn through trial and error, imitate others, and operate in an environment of incomplete information.
The Complexity Solution
Complexity economics offers a different path. By simulating millions of agents, each with simple but evolving rules, the system can generate emergent behavior that mirrors real-world patterns. This approach dramatically reduces computing demands while increasing realism. As Prof. Doyne Farmer, a physicist turned economist, explains: “We want to do for economic planning what Google Maps did for traffic planning… an intelligent and useful answer.”
Farmer’s track record speaks for itself. He famously exploited flaws in casino roulette in the 1970s using early digital computers, then built automated trading systems that outperformed Wall Street in the 1990s. He now applies this same analytical rigor to economic modeling.
Applying Complexity to Climate Change
The climate crisis represents a particularly acute failure of traditional models. Current projections have consistently underestimated the speed of renewable energy adoption and cost declines. Farmer’s team is building a model encompassing 30,000 energy companies and 160,000 assets, simulating their decision-making in a dynamic environment.
Early results suggest the potential for significant savings: a rapid transition to clean energy could save the world trillions of dollars. The model isn’t just theoretical; it’s grounded in 25 years of real-world data, allowing for more accurate forecasting and strategic planning.
Why Now?
Three key factors make this approach viable today:
- Computing Power: Modern computers are capable of handling the computational demands of large-scale agent-based simulations.
- Data Availability: Vast datasets on economic activity, energy production, and company behavior provide the raw material for realistic modeling.
- Theoretical Advances: Complexity science offers new tools – including machine learning – to handle evolving strategies and systemic shocks.
A Call for Investment
Farmer’s team is actively seeking funding to accelerate development. The $100 million price tag is a bargain, given the potential to mitigate trillions in economic losses and optimize climate policy. As Farmer puts it: “If somebody wants to give us millions of dollars to help build these models, we’ve got our hat out.”
The development of this complexity model isn’t just a scientific endeavor; it’s an urgent necessity. By abandoning outdated assumptions and embracing the real-world messiness of economic systems, we can finally create tools that accurately predict and navigate the crises of the 21st century.
