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

现代技术能够大规模收集和操纵有关线上和线下社会过程的详细数字痕迹。例如，在社交媒体平台上运行的应用程序提供了与数亿用户互动的机会，在一个可以根据其他人的行为指定/更改任何个人参数的环境中。从技术上讲，在此类应用中，我们经常可以访问所谓的社交网络信息，而现有策略通常不会利用这些信息来设计和分析实验。在此类网络系统中获取和操作知识的工具是关键。特别是，设计和分析实验的算法和统计策略，利用有关感兴趣个体系统组件之间连接性的信息，并且可以大规模运行，将为解决许多重要的开放问题和政策问题提供必要的垫脚石。 这项研究开发一个综合研究和教育计划，解决三个关键问题：（1）如何消除用于获取网络人群数据的流行链接跟踪算法的偏差； (2) 如何设计和评估优化给定目标的新网络采样算法； （3）如何设计网络群体的随机实验，以估计因果效应，而不是关联效应。它将开展两个案例研究，以在实践中展示这些工具：（1）对美国和欧洲的教育和网络资本对向上流动的影响进行大规模研究； (2) 对因果策略进行实证分析，以改进分布式软件系统中延迟的根本原因分析。有关更多信息，请参阅项目网站：http://www.people.fas.harvard.edu/~艾罗迪/iis-design_analysis_experiments_networks.html