Most of our networked world is a pool of buzzing, fresh interaction - not only hard to predict, but constantly on the sharp edge of making something new. Scientists like Holland call this “emergence”, the way that bottom-up interactions —- between cells or chips or traders or cars - create a larger order, often something that was not there before. The fundamental uncertainty of this process means it’s often excluded from the way we look at the world. It’s easier to assume a predictable, linear, complicated logic is at work. An “a leads to b and c” sort of logic: revolution leads to freedom which leads to democracy, for instance. That such predictions are often wrong - and that we’re so often surprised by events in economics or politics - is a reminder that compicated systems are often complex, lit with mechanisms that almost gleefully snap off the fingers of meddling, confident planners. Too often we look at some puzzle - Iraq, income inequality - and think itis merely “complicated.” We should know better. “Macro models failed to predict the crisis and seemed incapable of explaining what was happening to the economy in a convincing manner,” the European Central Banker Jean-Claude Trichet lamented in the aftermath of 2008s cascading, complex financial crises, when markets and officials discovered that the problem with their system was not merely that it was “too big to fail” but also “too connected to manage” - and possibly “too complex to comprehend.” Trichet sounded a little shell-shocked. “As a policy maker during the crisis I found the available models of little help. In fact, I would go further: In the face of the crisis, we felt abandoned by the conventional tools.”1"4 This sense of abandonment comes from an attempt to use a mechanical way of thinking in age of complexity. 145 When you think an air force can simply pound an insurgency to sand or that some old reliable business should survive because it rests upon billions of dollars of infrastructure, you miss