| The postmodern policy movement recognizes that policy is a complex phenomenon, involving multiple constituencies, interdependencies, degrees of choice, and behavioral change. In essence, policy is a dynamical system. This study asserts that existing models are insufficient for studying policy systems in the same way that linear models are insufficient for studying natural systems. Instead, the study proposes a nonlinear model that captures policy relationships in a few simple rules.;Connecting complexity theory and information theory to policy design is plausible because the nominal differences between the hard sciences and the soft sciences are narrowing. There is no logical reason for the intrinsic complexity of public policy to be greater than natural science. A broad literature review of these three theories created a framework for envisioning policy dynamics, including: (1) Understanding information as policy's structure. (2) Defining information as order emerging from data. (3) Perceiving the connection between information and perturbations in systems. (4) Extending these information concepts into the policy realm by proposing that all policy components, traditionally viewed as abstract elements or events, can be understood as information flows.;Consistent with complexity theory, the research followed a three-step process---study the structure, trace the information flows, and model the policy dynamics. The experiment demonstrates how dynamic modeling can expand understanding of possible policy outcomes. The findings suggest that an anticipatory policy approach may be an effective way to examine the interaction between external pressure and internal tension rather than analyzing individual elements. |
