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: www.people.fas.harvard.edu/~airoldi/iis-design_analysis_experiments_networks.html
