Mitchell Waldrop opened his remarks by noting several complex and difficult public policy issues-post-cold war geopolitics, health care reform, economic growth, values and community, global sustainability, nationwide political realignments-that have proved difficult to understand and hard to manage. Waldrop asserted that these problems are complex in the sense that they are:

Systems that have these characteristics-including biological systems like a coral reef, the human brain, animal populations; economic systems like national or regional economies; and political systems like towns and nations-have been dubbed complex adaptive systems (CAS).

The fact that certain public policy issues are complex has been obvious for a long time, Waldrop noted. What is different now is the emerging science of complexity and the ability this science gives to gain insight into complex situations. Using new research tools made possible by advanced computing, analysts have recognized that similar systems are found throughout the natural world-cells, brains, immune systems, ecosystems-with many characteristic behaviors in common. The beginnings of a conceptual framework of the patterns of complex adaptive systems is emerging across scientific disciplines. These concepts offer the possibility for new theories and models for analysis, and ultimately, perhaps, for control.

The science of complexity has found that collective behavior and self-organization has patterns of activity. Things that start out random don't necessarily stay random, Waldrop noted. As examples, he cited the fact that hydrogen molecules start out with certain properties, but when combined with oxygen, they take on the properties of water, a property that would not obtain to any single molecule. This collective behavior is self-organizing: there is no head molecule in charge of creating water. Other examples of self-organizing systems are hurricanes, air traffic control systems, and the Internet.

The good news about self-organizing systems is that organization from the bottom up creates a robust, flexible and spontaneous system, able to adapt quickly to change. Free market economies, a coral reef, weather, are all examples. The bad news, according to Waldrop, is that self-organization can cause lock-in around bad outcomes: stagflation, the underclass, storms, the Balkans. The phenomenon of lock-in is not forever. Complex systems never settle down to equilibrium; they have the property of perpetual novelty. In fact, evolution of events and systems tend to push complex adaptive systems to the edge of chaos in which upheaval and change at the periphery upset the system.

What are the benefits from viewing science, or public policy, from the viewpoint of discovering in the science of complexity? Instead of focusing on cause and effect, the science of complexity focuses attention on the massively parallel coevolution. Instead of searching for eternal equilibrium, the science of complexity focuses attention on the perpetual novelty and upheaval inherent in complex adaptive systems. Finally, instead of searching for a perfect solution to problems, the science of complexity focuses attention on finding a framework for adaptation. Structures that are successful are able to adapt: e.g., the U.S. Constitution, the human mind.

The science of complexity offers the possibility of developing theory, models, and predictions of possible future events. These types of dynamic models may be applicable to policymaking. Such models may not be able to predict actual outcomes, but they may be able to provide insight about the kinds of things that might happen within a complex adaptive system when some factors change. Dynamic modeling offers particular benefits to the soft sciences where testing ideas and theories has been exceedingly difficult. Such models can help clarify assumptions, show consequences of these assumptions, suggest new questions and new experiments, and develop new intuitions about interactions and collective phenomena.

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Author: Caroline Wagner

Rand Corporation

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