Inspired by these events, John quickly developed a theory for how leverage causes booms and busts. He did this in 2000, eight years before the 2008 financial crisis.6 Whereas Minsky simply presented the idea that waves of investor pessimism and optimism could influence borrowing and possibly cause a crash, John’s mathematical theory linked

In fact, it would have been really surprising to see simple chaos at work in economic data. The economy is inherently complicated, with many different interacting factors. Even if much of economic change is endogenous, much is not. Moreover, because the economy changes slowly, in timescales measured in years, there is effectively very little data

based on historical data alone, complicated chaos is impossible to distinguish from a random process.

The making of a laptop illustrates how the economy operates like a big metabolism. The production network has a structure similar to the proto-life model described earlier. Their definitions are the same except for a change in noun phrases: A metabolism is a network of chemical reactions that transforms a food set of chemical inputs into other

Chaos can be simple or complicated. Simple chaos and complicated chaos lie at two ends of the same spectrum, but their behavior is quite different. In simple chaos (like the Lorenz equations), the motion can be described using only a few variables. For complicated chaos, like the weather, many factors act independently, and many variables are

When a house is put on the market, the seller looks for recent sales of comparable houses and marks his or her own house up by a small amount, typically about 5 per cent. Then if the house doesn’t sell after a month, the seller marks it down by about 10 per cent, and continues marking it down until either the house sells or is taken off the market.

Chaos is characterized by two essential properties: The first is sensitive dependence on initial conditions, and the second is endogenous motion, meaning that, even though there are no external shocks, the system never settles down to rest.

Representing a system as a network begins by identifying its building blocks and their interactions: asking, ‘What are the most important nodes?’ and ‘Are there communities? If so, what are they?’ (Mathematically speaking, a community is a set of nodes that interact with each other much more strongly than they do with other nodes.)

The conclusion was clear: Games converge to a static equilibrium if they are non-competitive or simple; conversely, if they are both competitive and complicated, they are unlikely to converge – instead, their dynamics will be chaotic.