III: Medium: Design and analysis of experiments on networked populations

III：媒介：网络群体实验的设计和分析

• 批准号: 1409177
• 负责人: Edoardo Airoldi
• 金额: $ 112.08万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1409177&HistoricalAwards=false
• 关键词: III; Medium; Design; analysis; experiments

Modern technology enables the collection and manipulation of detailed digital traces about online and offline social processes, at scale. Apps that run on social media platforms, for instance, provide the opportunity to engage with hundreds of millions of users, within an environment whose parameters for any individual can be specified/changed in response to the behavior of others. Technically, in such applications we often have access to so-called social network information, which is typically not leveraged by existing strategies to design and analyze experiments. Tools for knowledge acquisition and manipulation in such networked systems are key. In particular, algorithmic and statistical strategies to design and analyze experiments that leverage information about connectivity among the components of a system of interest individuals, and can operate at scale, will provide necessary stepping stones for tackling many important open problems and policy questions.This research develops an integrated research and educational program that addresses three key problems: (1) how to remove bias from popular link-tracing algorithms used to acquire data on networked populations; (2) how to design and evaluate new network sampling algorithms that optimize a given objective; and (3) how to design randomized experiments on networked populations that enable the estimation of causal effects, rather than associative effects. It will develop two case studies to demonstrate these tools in practice: (1) A large-scale study of the effects of education and network capital on upward mobility, in the United States and Europe; and (2) An empirical analysis of causal strategies to improve of root-cause analysis of latency in distributed software systems.For further information see the project web site located at: http://www.people.fas.harvard.edu/~airoldi/iis-design_analysis_experiments_networks.html

现代技术使收集和操纵有关在线和离线社会流程的详细数字痕迹大规模。例如，在社交媒体平台上运行的应用程序提供了与数以亿用户互动的机会，该环境可以根据他人的行为来指定/更改任何个人的参数。从技术上讲，在这样的应用中，我们通常可以访问所谓的社交网络信息，通常无法通过设计和分析实验的策略来利用这些信息。此类网络系统中知识获取和操纵的工具是关键。 In particular, algorithmic and statistical strategies to design and analyze experiments that leverage information about connectivity among the components of a system of interest individuals, and can operate at scale, will provide necessary stepping stones for tackling many important open problems and policy questions.This research develops an integrated research and educational program that addresses three key problems: (1) how to remove bias from popular link-tracing algorithms used to acquire data on networked populations; （2）如何设计和评估优化给定目标的新网络采样算法； （3）如何在网络种群上设计随机实验，以估计因果效应而不是关联效应。它将开发两个案例研究以证明这些工具在实践中：（1）对美国和欧洲的教育和网络资本对向上流动性的影响的大规模研究； （2）对分布式软件系统中的根本原因分析的因果策略进行的经验分析。有关更多信息，请参阅位于以下网站的项目网站：http：//wwwww.people.fas.harvard.edu/ eedu/~airoldi/~airoldi/iis-design_analisy_experiments_experiments_experiments_experiment_networks.htworks.htworks.htworks.htmless.htmle