CIF: Small: Collaborative Research: Inference by social sampling

CIF：小型：协作研究：社会抽样推断

• 批准号: 1440033
• 负责人: Anand Sarwate
• 金额: $ 17.58万
• 依托单位: Rutgers University New Brunswick
• 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: 2014-01-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1440033&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Inference; CIF; Small; Collaborative; Research; Inference

Learning and inference in distributed settings is an important from both a scientific and engineering perspective. A typical instance of the problem is a network of individual sensors or agents attempting to infer a global distribution that governs their local observations. By passing messages the agents can individually make inference about a global phenomenon. This research investigates communication and networking paradigms that can enable a network of individual agents to collaboratively estimate distributions over high dimensional spaces, even when individual observations are severely limited in accuracy, space, or time. In particular, the investigators study how individual decision makers can integrate two kinds of information: local observations and messages from their neighbors in the network. Both observation and messaging can be thought of as sampling : individuals sample their own environment and sample the opinions of their neighbors. Central to the approach is that the agents generate simple messages at random from an internal estimate of the global distribution of interest. The first major goal of this project is to develop a mathematical framework and analysis techniques to understand if and when this limited form of learning and communication is sufficient for an individual to estimate and learn distributions and/or global parameters governing the observations of all nodes. The technical approach is a blend of analysis techniques ranging from stochastic approximation, randomized algorithms, and statistical physics. Applications for this work range from mathematical modeling of messages and opinion formation in social networks, communication protocols for distributed optimization, and estimation of parameters in data networks. The work will cover several related problems : estimating high-dimensional histograms of data held in the network, parametric estimation using a mix of Bayesian and non-Bayesian techniques, and estimation of more complex generative models. The final part of the work is to apply these methods to peer-peer networks and social network modeling. The broader impact of this work is to further develop the interdisciplinary field of network science, which impacts both quantitative social sciences and engineering. The PIs will develop educational materials and organize research activities to help bring together different research communities interested in networks and social learning.

从科学和工程学的角度来看，分布式设置中的学习和推断是一个重要的。 该问题的一个典型实例是一个单个传感器的网络或试图推断其本地观察结果的全球分布的代理网络。 通过传递信息，代理可以单独推断全球现象。 这项研究研究了可以使单个代理网络网络在高维空间上进行协作估算分布的沟通和网络范例，即使在精确，空间或时间上的个人观察值受到严重限制。特别是，研究人员研究了个人决策者如何整合两种信息：网络中其邻居的本地观察和信息。 观察和消息传递都可以将其视为抽样：个人采样了自己的环境，并品尝了邻居的意见。 该方法的核心是，代理商是从全球利益分布的内部估计中随机生成简单的消息。 该项目的第一个主要目标是开发一种数学框架和分析技术，以了解以及何时以及何时这种有限的学习和交流形式足以让个人估算和学习分布和/或全球参数，以控制所有节点的观察结果。 技术方法是分析技术的融合，从随机近似，随机算法和统计物理学等等。 此工作的应用程序范围从社交网络中消息和意见形成的数学建模，用于分布式优化的通信协议以及数据网络中参数的估计。 这项工作将涵盖几个相关的问题：估计网络中保存的数据的高维直方图，使用贝叶斯和非贝叶斯技术的混合物进行参数估算，以及对更复杂的生成模型的估计。 工作的最后一部分是将这些方法应用于对等网络和社交网络建模。 这项工作的广泛影响是进一步发展网络科学的跨学科领域，这既影响了定量的社会科学和工程学。 PI将开发教育材料并组织研究活动，以帮助将对网络和社会学习感兴趣的不同研究社区融合在一起。