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

这个项目是通过追踪新思想或行为在人群中传播的数据集来研究统计推断。扩散的博弈论模型被利用，在该模型中，群体成员决定采用一种技术，通过最大化依赖于底层网络结构的收益。示例问题包括识别“先动者”，以及导致给定网络观察状态的最可能的一系列动作。算法正在开发表征的最大似然估计的先行者演化博弈论框架与平滑的最佳响应动力学。此外，还研究了识别影响节点和网络图以及相关收益函数的算法。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，优化和博弈论的基础上。理解社会网络中的扩散广泛适用于整个社会，包括市场营销、经济学和社会科学等领域；目前正在努力将这项工作的成果传播到这些领域，并将这些想法纳入欧共体共同体的本科和研究生课程。

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.