III: Small: Inferring first movers in large-scale socio-technical networks

III：小型：推断大规模社会技术网络中的先行者

• 批准号: 1538827
• 负责人: Vijay Subramanian
• 金额: $ 32.96万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-11-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1538827&HistoricalAwards=false
• 关键词: III; Small; Inferring; first; movers

This project is studying statistical inference from datasets tracking the diffusion of new ideas or behaviors through a population. Game theoretic models for the diffusion are utilized in which members of the population decide to adopt a technology by maximizing a pay-off that depends on an underlying network structure. Example questions include the identification of "first movers" and the most likely series of actions that result in a given observed state of the network. Algorithms are being developed for characterizing the maximum likelihood estimate of first movers for an evolutionary game theoretic framework with smoothed best response dynamics. Additionally algorithms to identify influential nodes and the network graph along with the associate payoff functions are being studied. The associated modeling and analysis build upon foundations in probability and statistics, Markov processes, statistical mechanics, optimization and game theory.Understanding diffusions in social networks is broadly applicable across society including areas such as marketing, economics and social sciences; efforts are being made to disseminate the results of this work to such fields as well as to incorporate ideas into undergraduate and graduate courses in EECS.

该项目正在研究从数据集跟踪新想法或行为在人群中的传播的统计推断。利用博弈论模型的扩散，其中成员的人口决定采用一种技术，通过最大限度地提高回报，这取决于一个底层的网络结构。示例问题包括识别“先行者”以及导致网络给定观测状态的最可能的一系列动作。算法正在开发的特点最大似然估计的第一个移动的进化博弈论框架与平滑的最佳响应动态。此外，识别有影响力的节点和网络图沿着以及相关的支付函数的算法正在研究中。相关的建模和分析建立在概率和统计，马尔可夫过程，统计力学，最优化和博弈论的基础上。了解社交网络中的扩散是广泛适用于整个社会，包括市场营销，经济和社会科学等领域，正在努力传播这项工作的成果，这些领域以及将想法纳入本科和研究生课程EECS。