Watch these short videos as leading academics present at this year’s inaugural Social Science Foo Camp discuss the opportunities and challenges presented by big data and the move to more computational methods.
Computational Social Science boils down to Social Scientists using data processing and data science computation tools (think R, Python etc) to analyze data about people and relationships.
In June, I attended the second iteration of the Summer Institute for Computational Social Science (SICSS), an intensive two-week program held at Duke that was intended to bring together researchers from across the social science and data science disciplines to learn and discuss topics in computational social science (CSS). Each day, the organizers Chris Bail and Matt Salganik taught mini-lectures on different CSS topics, we split into groups to work on activities together, and a speaker came in to present their research.
Today, there is a new window of opportunity to adopt agent computing as a mainstream analytic tool in economics. Here, I discuss four major aspects in which this technology can improve economic policymaking: causality and detail, scalability and response, unobservability and counterfactuals, and separating design from implementation. In addition, I highlight the crucial role that policy agencies and research funders have in this endeavor by supporting a new generation of computationally-enabled social scientists.
This project is a competition for researchers to build tools to help automate the discovery of data sets in the social sciences. The competition comes with prizes of $2,000 to each of the finalists, with up to $20,000 to be awarded to the winning team. Find out more and apply today.
Agent computing is a simulation tool that has been successfully adopted in many fields where policy interventions are critical. Economics, however, has failed in doing so. Today, there are new opportunities for bringing agent computing into economic policy. In this post, I discuss why this technology has not been adopted for economic policy and point out new opportunities to do it.
Ahead of this year’s APSA general meeting, we attended the Politics and Computational Social Science (PaCSS) pre-conference, hosted at Northeastern University. The event brought together political scientists working with large-scale data sets and emerging computational methods.
News media serves as a window into the society its readership represents. A newspaper’s description of a social group both demonstrates and constructs perceptions of that group within its audience. Understanding long-term trends or spatial differences in the representation of minority groups in news media can contribute to ongoing theoretical debates about the role and perception of minority groups in society.
Teaching and learning resources from the 2018 Summer Institute for Computational Social Science have been made free to access online, allowing more people to explore in depth the field of computational social science.