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；最近对禽流感流行和金融传染的担忧就是很好的例子。其他的例子包括：观点、时尚、趋势、规范、分组扩散、计算机网络中的蠕虫传播和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，在非常大的动态社会技术网络上运行，其中多种传染和行为同时通过重复的相互作用共同进化。MTML-Sim将使用基于计算代理的方法在个体层面上解决，以表示和计算共同进化的动态。目标是将MTML-Sim扩展到数十亿个由多层次社交和信息网络连接的独立代理，并在未来几年内上线的100,000个处理器的千兆级架构上有效地执行。MTML-Sim将提供实用的方法，在传染病传播引起的大规模级联之前和期间支持学术研究人员和政策制定者。该项目的更广泛影响将通过在级联开始之前用于识别社会技术网络中的关键要素的工具，以及基于对动态发展的社会技术网络中个人和群体行为影响的理解来控制级联的潜在干预措施来实现。

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.