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The other butterfly effect

monarch butterfly

The original butterfly effect

The butterfly effect is the semi-serious claim that a butterfly flapping its wings can cause a tornado half way around the world. It’s a poetic way of saying that some systems show sensitive dependence on initial conditions, that the slightest change now can make an enormous difference later. Often this comes up in the context of nonlinear, chaotic systems but it isn’t limited to that. I give an example here of a linear differential equation whose solutions start out the essentially the same but eventually diverge completely.

Once you think about these things for a while, you start to see nonlinearity and potential butterfly effects everywhere. There are tipping points everywhere waiting to be tipped!

The other butterfly effect

But a butterfly flapping its wings usually has no effect, even in sensitive or chaotic systems. You might even say especially in sensitive or chaotic systems.

Sensitive systems are not always and everywhere sensitive to everything. They are sensitive in particular ways under particular circumstances, and can otherwise be quite resistant to influence. Not only may a system be insensitive to butterflies, it may even be relatively insensitive to raging bulls. The raging bull may have little more long-term effect than a butterfly. This is what I’m calling the other butterfly effect.

Steering complex systems

In some ways chaotic systems are less sensitive to change than other systems. And this can be a good thing. Resistance to control also means resistance to unwanted tampering. Chaotic or stochastic systems can have a sort of self-healing property. They are more stable than more orderly systems, though in a different way.

The lesson that many people draw from their first exposure to complex systems is that there are high leverage points, if only you can find them and manipulate them. They want to insert a butterfly to at just the right time and place to bring about a desired outcome. Instead, we should humbly evaluate to what extent it is possible to steer complex systems at all. We should evaluate what aspects can be steered and how well they can be steered. The most effective intervention may not come from tweaking the inputs but from changing the structure of the system.

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