Making Sense of Chaos
J. Doyne Farmer argues that while AI and statistical models are useful in economics, they are inherently limited because past events rarely mirror present circumstances, and the world is constantly evolving. His research focuses on economics, particularly agent-based modeling, financial instability, and technological progress. In Making Sense of Chaos: A Better Economics for a Better World, Farmer emphasizes that statistical analysis can help us understand the present but can infer future trends only if underlying behaviors remain consistent. He advocates for complexity economics, which analyzes networks of balance sheets from the bottom up, treating the economy as a dynamic system using “as-is” reasoning rather than hypothetical “as-if” models. Unlike traditional models, complexity economics builds from the granular interactions between agents, emphasizing dynamic systems and the inherent uncertainty of economic behaviors.
Making Sense of Chaos challenges mainstream economics for its inability to provide reliable quantitative insights, particularly when dealing with inequality and climate change. He argues that complexity economics, which incorporates uncertainty, presents an appealing alternative for tackling significant challenges like climate change, where many outcomes are unknowable and probabilities cannot be accurately assigned. Farmer is optimistic about the potential of complexity economics to address the global economy, though he acknowledges that obtaining comprehensive data remains a challenge.
In Part V of Making Sense of Chaos, Farmer envisions a future where agent-based models of the global economy, integrated with environmental, sociological, and political models, can drive transformative policy solutions. He argues that advances in cloud computing and data technology could make these envisioned models a reality, enabling unprecedented reliability in economic modeling. Farmer concludes that adopting complexity economics will require overcoming academic resistance and leveraging big data to its fullest potential. Ultimately, Farmer’s vision in Making Sense of Chaos challenges us to rethink economic modeling by embracing complexity and uncertainty. His work demonstrates that our current tools barely scratch the surface of what is possible and offers hope for addressing humanity’s most pressing challenges through a revolutionary approach to understanding the global economy.
