Cells within the economy can increase productivity, but under the paradigm of increasing returns, the ability of an agent to gain in productivity depends upon their willingness to invest in education. While many observers have noted the role of increasing returns in high technology industries over the years, until the advent of dynamic modeling and complexity theory, there has not been a way to model this phenomenon. There currently exists a rudimentary ability to analyze these phenomena with rigorous computer experiments. Within these computerized experiments, analysts are able to pull the levers and dials and examine possible outcomes. Clearly, there are a multiplicity of possible outcomes in any complex adaptive system, Arthur asserted, but it is not clear how to relate these outcomes to public policy.
Small events can drive a system into a cycle of activity.
Within these cycles of activity, there may be timing windows when new paths of action are possible. In other cases where the system is locked-in, policymakers may attempt to solve a problem, but no change may be possible. Arthur likened the problem to water streaming into a rugged landscape. At different times one could imagine tilting the system to achieve desired outcomes. When the water has settled into valleys, little change may be possible.