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

该项目正在研究从追踪新思想或行为在人群中扩散的数据集进行的统计推断。利用了扩散的博弈论模型，其中人口成员通过最大化取决于基础网络结构的回报来决定采用一项技术。示例问题包括“先行者”的识别以及最有可能导致网络的给定观察状态的一系列操作。对于具有平滑的最佳响应动态的进化博弈论框架，正在开发用于表征先行者的最大似然估计的算法。此外，还研究了识别有影响力的节点和网络图的算法以及相关的收益函数。相关的建模和分析建立在概率统计、马尔可夫过程、统计力学、最优化和博弈论的基础上。理解社会网络中的传播广泛适用于整个社会，包括市场营销、经济学和社会科学领域；正在努力将这项工作的成果传播到这些领域，并将想法纳入EECS的本科生和研究生课程。

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.