### How to finesse complexity

**HE BEGAN, “Hi, I’m Nicky Case, and I explain complex systems in a visual, tangible, and playful way.”** He did exactly that with 207 brilliant slides and clear terminology. What system engineers call “negative feedback,” for example, Case calls “balancing loops.” They maintain a value. Likewise “positive feedback” he calls “reinforcing loops.” They increase a value

Using examples and stories such as the viciousness of the board game Monopoly and the miracle of self-organizing starlings, Case laid out the visual basics of finessing complex systems. A reinforcing loop is like a ball on the top of a hill, ready to accelerate downhill when set in motion. A balancing loop is like a ball in a valley, always returning to the bottom of the valley when perturbed.

Now consider how to deal with a situation where you have an “attractor” (a deep valley) that attracts a system toward failure:

[](https://media.longnow.org/files/2/CaseSlide2.png)

The situation is precarious for the ball because it is near a hilltop that is a reinforcing loop. If the ball is nudged over the top, it will plummet to the bottom of the balancing-loop valley and be stuck there. It would take enormous effort raise the ball out of such an attractor—which might be financial collapse or civil war. Case’s solution is not to try to move the ball, MOVE THE HILLS—identify the balancing and reinforcing loops in the system and weaken or strengthen _them_ as needed to reconfigure the whole system so that the desired condition becomes the dominant attractor.

Now add two more characteristics of the real world—dense networks and chaos (randomness). They make possible the phenomena of emergence (a whole that is different than the sum of its parts) and evolution. Evolution is made of selection (managed by reinforcing and balancing loops) plus variation (unleashed by dense networks and chaos). You cannot control evolution and should not try--that way lies totalitarianism. Our ever popular over-emphasis on selection can lead to paralyzed systems—top-down autocratic governments and frozen businesses. Case urges attention to variation, harnessing networks and chaos from the bottom up via connecting various people from various fields, experimenting with lots of solutions, and welcoming a certain amount of randomness and play. “Design _for_ evolution,” Case says, “and the system will surprise you with solutions you never thought of.”

To do that, “Make chaos your friend.”