Barrett Christopher L, Eubank Stephen, Marathe Achla, Marathe Madhav V, Pan Zhengzheng, Swarup Samarth
Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia 24061.
Innov J. 2011;16(1).
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.
社会和技术政策领域的复杂性，如经济、环境和公共卫生等，带来了诸多挑战，这需要一种全新的建模与决策方法。在这些复杂领域中，有效制定政策和进行决策所需的信息规模庞大、分辨率精细且分布于众多数据源。因此，构建支持政策信息学的系统面临的关键挑战之一就是信息整合。我们描述了针对此问题的方法，以及我们如何构建一个多理论、多主体、多视角的系统，该系统基于大规模高性能计算基础设施，支持持续的数据获取、状态评估、决策分析以及行动分配。我们基于模拟的方法能够对政策干预结果的差异进行快速行动方案分析，进而实现应对诸如疫情爆发等紧急情况所需的短期规划。我们阐述了我们方法的基本原理和设计，并讨论了几个实际应用和潜在应用领域。
