Making Sense of Chaos summary

Author J. Doyne Farmer first started thinking about economics when he was an undergrad at Stanford University in 1971. His professor was clear and rational, but Farmer found himself questioning the fundamental ideas of economic equilibrium. 

One of the principles of traditional economics is that the market is always working to achieve an equilibrium – a state where the quantity of goods or services supplied equals the quantity demanded. When it reaches an equilibrium, there aren’t any inherent pressures for the price to change because the market forces of supply and demand are in harmony.

Right away, Farmer had his doubts about this assumption. How could economists simply believe that prices would always adjust perfectly to where supply meets demand – especially when real-world markets rarely behave that predictably?

But at the time, Farmer was working to become a physicist, not an economist. It wasn’t until years later, while working at the Santa Fe Institute, that he met a group of physicists who were like-minded in their skepticism and interested in exploring economics from a different perspective.

They saw economic systems as dynamic and constantly evolving – much like the natural systems they were used to studying. This difference in viewpoint sparked a lot of debate and friction. But it also opened the door to new ways of thinking about economics, particularly through the study of complex systems.

Complex systems are nonlinear systems, where the whole is more than the sum of its parts. Take an ant colony, for instance. Each ant, on its own, is a simple creature. But together, ants create sophisticated systems capable of farming, fighting, and building – all without any central control. The behavior of the colony emerges from the interactions among the ants rather than being directed by a single leader. This idea of emergent behavior is crucial to understanding complex systems.

Farmer argues that traditional economic models, which rely on that fixed-point equilibrium, fail to capture the true nature of economic systems. In reality, economies are more like chaotic systems, where outcomes are influenced by initial conditions and can never fully settle into a steady state.

To prove this, Farmer and his colleagues built models that incorporate chaos and complexity to better predict the economy. Armed with deeper insights and advanced technology, Farmer’s team has managed to predict seemingly random events, like the spin of a roulette wheel. 

Using similar techniques, we might also be able to make better predictions about economic variables like GDP, unemployment, and inflation. In the sections ahead, we’ll see how Farmer plans to do just that.