The complexities of social and technological policy domains, such as the economy, the environment, and public health present challenges that require a new approach to modeling and decision making. The information required for effective policy and decision making in these complex domains is massive in scale, fine-grained in resolution, and distributed over many data sources. Thus, one of the key challenges in building systems to support policy informatics is information integration. We describe our approach to this problem, and how we are building a multi-theory, multi-actor, multi-perspective system that supports continual data uptake, state assessment, decision analysis, and action assignment based on large-scale high-performance computing infrastructures. Our simulation-based approach allows rapid course-of-action analysis to bound variances in outcomes of policy interventions, which in turn allows the short time-scale planning required in response to emergencies such as epidemic outbreaks. We present the rationale and design of our methodology and discuss several areas of actual and potential application.