CIF: Small: Statistical Signal Processing of Social Networks with Behavioral Economics Constraints

社交网络无处不在。通过构建新的工程模型、算法和分析，人们有强烈的动机来理解社会网络中的重要社会学现象。该项目有助于了解统计推断、人类决策和社会网络中的信息流之间的相互作用的基础研究。这项研究涉及三个相互关联的主题。第一个主题开发了新的算法，用于检测在社交网络上分享信息的人类决策者的策略变化，例如，识别Twitter上对病毒内容的反应行为。第二个主题模拟并分析了人类在社交网络上的互动如何导致诸如玻璃天花板效应和种族隔离等社会学现象。第三个主题研究了大规模社交网络中的高效轮询算法，在大规模社交网络中，只有一小部分节点可以被轮询来决定他们的决策。例如，应该对哪些节点进行民意调查，以实现对诸如玻璃天花板效应的出现等社会学现象的统计准确估计？该项目将通过对真实社会网络数据的广泛分析来验证理论主张和发现。该项目开发了新的工程模型、算法和分析，以了解统计信号处理、行为经济学(人类决策)和网络科学(社会网络中的信息流)之间的交互作用。该项目在三个相互关联的主题上进行基础研究。第一个主题研究具有行为经济学约束的多智能体信息融合和变化检测(预期决策和理性不注意)，目的是了解信息融合是如何在人类决策者之间实现的。第二个主题调查复杂的代理人如何在社会网络上相互作用，导致玻璃天花板效应和隔离这一重要的社会学现象。玻璃天花板效应指的是阻止某些群体上升到有影响力的社会地位的障碍，无论他们的资质如何。在社交网络背景下，研究人员探索了偏好依恋和同质性等个人特征是如何导致玻璃天花板效应的。第三个主题研究了统计高效的网络轮询算法的设计。在大规模的社交网络中，只有一小部分节点可以被轮询来决定他们的决策。应该对哪些节点进行民意调查，以实现对诸如玻璃天花板效应等社会学现象的统计准确估计？一些节点可能不愿透露他们的真实观点。这可能会导致不正确的民调估计。这怎么能得到补偿呢？这个项目中的理论主张和发现将通过对真实世界社交网络数据的广泛分析来验证。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Social networks are ubiquitous. There is strong motivation to understand important sociological phenomena in social networks by constructing novel engineering models, algorithms and analysis. This project contributes to fundamental research in understanding the interaction between statistical inference, human decision making and information flow in social networks. The research addresses three interrelated themes. The first theme develops novel algorithms for detecting a change in the strategies of human decision makers that share information over a social network, for example, identifying reactive behavior to viral content on Twitter. The second theme models and analyzes how humans interacting over a social network can result in sociological phenomena such as the glass-ceiling effect and segregation. The third theme studies efficient polling algorithms in large-scale social networks where only a fraction of nodes can be polled to determine their decisions. For example, which nodes should be polled to achieve a statistically accurate estimate of sociological phenomena such as the emergence of the glass-ceiling effect? The theoretical claims and findings in this project will be validated via extensive analysis of real-world social network datasets.This project develops novel engineering models, algorithms and analysis to understand the interaction between statistical signal processing, behavioral economics (human decision making) and network science (information flow in social networks). The project conducts fundamental research in three interrelated themes. The first theme studies multi-agent information fusion and change detection with behavioral economics constraints (anticipatory decision making and rational inattention); the goal is to understand how information fusion is achieved among human decision makers. The second theme investigates how sophisticated agents interacting over a social network give rise to the important sociological phenomena of the glass-ceiling effect and segregation. The glass-ceiling effect refers to the barrier that keeps certain groups from rising to influential positions in society, regardless of their qualifications. In a social-network context, the investigator explores how individual traits like preferential attachment and homophily leads to the glass-ceiling effect. The third theme studies the design of statistically efficient network polling algorithms. In large-scale social networks, only a fraction of nodes can be polled to determine their decisions. Which nodes should be polled to achieve a statistically accurate estimate of sociological phenomena such as the glass-ceiling effect? Some nodes may be reluctant to reveal their true opinion. This may lead to incorrect polling estimates. How can this be compensated for? The theoretical claims and findings in this project will be validated via extensive analysis of real-world social-network datasets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.