Collaborative Research: Coupled Models of Diffusion and Individual Behavior Over Extremely Large Social Networks

该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。观点、态度、信仰和疾病等传染病在全国人口中的传播是一个众所周知的复杂问题；最近对禽流感疫情的恐惧和金融传染就是很好的例子。其他例子包括：观点、时尚、趋势、规范、数据包扩散、计算机网络中的蠕虫传播以及传感器网络中的数据库复制。在这个项目中，研究人员建议开发MTML-SIM：一个多理论多层次(MTML)建模框架，运行在超大型动态社会技术网络上，其中多种传染病和行为通过重复相互作用同时共同进化。MTML-SIM将使用基于计算代理的方法来表示和计算共同进化的动力学。其目标是将MTML-Sim扩展到通过多层社交和信息网络连接的数十亿个单独的代理，并在将在未来几年上线的100,000个处理器的千万亿级架构上高效执行。MTML-SIM将为学术研究人员以及政策制定者提供实用方法，在传染病蔓延造成的大规模级联之前和期间提供支持。该项目的更广泛影响将通过以下方式实现：在级联开始之前确定社会技术网络中的关键要素的工具，以及根据对动态发展的社会技术网络中个人和群体行为的影响的了解而采取的控制级联的潜在干预措施。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The spread of contagions such as opinions, attitudes, beliefs, and diseases across a national population is a well-known complex problem; the recent fear of avian flu epidemics and financial contagion serve as excellent examples. Other examples include: opinions, fads, trends, norms, packet diffusion, worm propagation in computer networks and database replication in sensor networks. In this project the investigators propose to develop MTML-Sim: a Multi-Theory Multi-Level (MTML) modeling framework, operating over extremely large dynamic socio-technical networks, in which multiple contagions and behaviors are simultaneously co-evolving by repeated interactions. MTML-Sim will use computational agent-based methods resolved at an individual level to represent and compute the co-evolving dynamics. The goal is to scale MTML-Sim to billions of individual agents connected by multi-level social and information networks, and efficiently execute on 100,000-processor petascale architectures that will come online in the next few years. MTML-Sim will provide practical methods to support academic researchers as well as policy makers before and during large-scale cascades caused by the spread of contagion. The broader impacts of the project will be achieved by tools used to identify critical elements in the socio-technical networks before the start of cascades, and by potential interventions for controlling the cascades based on the understanding of the effects of individual and group behavior within the dynamically evolving socio-technical networks.