Collaborative Research: Dynamics for Social Networks Processes: Comparing Statistical Models with Intelligent Agents

这个项目的目标是协调两种方法来建模社会网络随时间的变化：一类统计模型和智能代理模型。本研究对比了这两种方法的性质，探索了社交网络中哪些定性动态行为是有效和可解释的。主要的工具是潜在变量表示和动力系统分析。其结果是一个框架，其特点的优势和局限性的两类模型在多个设置，并提供反馈，刺激完善现有的理论社交网络和发展的新理论。社会网络理论产生了丰富的范式来描述的性质和演变的人群谁互动，可能在子群体，与对方。社会网络理论的应用范围从朋友到公司，从政治集团到宗教团体。反恐是当前特别重要的应用之一，在反恐中，网络可能具有潜在的（不可观察的）特征对它随着时间的推移的演变是特别密切相关的。然而，对社交网络动态的不同模型的优势和局限性还没有很好的理解。该项目的研究人员来自社会科学，统计学和应用数学，正在构建一个概念和操作框架，允许在多种情况下对不同模型进行原则性比较。除了理论和方法的进步，该框架使社会科学研究人员和其他分析人员能够选择一个模型，其动态特性最适合于每个应用程序。

The goal of this project is to reconcile two methods for modeling change insocial networks over time: a class of statistical models and intelligent agentmodels. The research contrasts the properties of these two approaches,exploring what qualitative dynamic behaviors in social networks are capturedusefully and interpretably by each. The primary tools are latent variablerepresentations and dynamical systems analysis. The result is a framework thatcharacterizes the strengths and limitations of the two classes of models inmultiple settings, and provides feedback that stimulates refinement ofexisting theories for social networks and development of new theory.Social network theory has produced a wealth of paradigms to describe thenature and evolution of groups of people who interact, possibly in subgroups,with one another. Settings in which social network theory has been appliedrange from friend to corporations to political blocs to religious groups.Counterterrorism is one application of particular current importance, in whichthe notion that the network might have latent (unobservable) characteristicsthat are important to its evolution over time is especially germane.Currently, however, there is not good understanding of strengths andlimitations of different models for the dynamics of social networks.The researchers on this project, drawn from the social sciences,statistics and applied mathematics, are constructing a conceptual andoperational framework that allows the principled comparison of differentmodels in multiple contexts. In addition to advances in theory andmethodology, the framework enables social sciences researchers and otheranalysts to choose a model whose dynamic properties are most appropriate toeach application.