Government policies have the potential to transform our lives in extraordinary ways. They can influence how much tax you pay, immigration laws and practices, school funding, parking fines, health care, family planning, and so much more. Often, these policies are born out of the political ideologies of governing bodies and aim to achieve specific goals such as reducing population growth by controlling birth rates or encouraging economic growth through visas, quotas and other regulations that affect migration patterns.
Policy making is an extraordinarily challenging and at times, complex task. The most effective policy makers are those who can harness ideas and resources to create policies that improve society and benefit as many people as possible. But sometimes, despite the best of intentions and the most talented minds, policy decisions can have unexpected consequences and fail to deliver on their intended benefits. These policy failures can’t always be rectified with smarter experts or more data. Rather, they stem from the fact that complex public policies often occur in systems where standard decision theory based on prediction, evaluation and control fails to capture how these policies operate.
The reason why is simple: Complex systems are self-organizing and non-linear, meaning that the overall effect of a policy cannot be predicted from its constituent parts. As a result, these types of policies tend to produce “emergent phenomena” that you simply can’t anticipate — or even understand until you run the system (Watts, 2011). Examples of emergent phenomena include: