Computational Social Sciences

What we are doing

Working to integrate social theory and computational methods to address fundamental questions in the social sciences. We encourage a transdisciplinary approach that tightly couples big data sets and small data sets, computational methods such as machine learning and agent-based modeling, and robust theories, models, and frameworks from the social sciences. 

Examples include:

• Developing and applying innovative statistical tests to assess the decline-of-war thesis in international politics [link: https://www.amazon.com/Only-Dead-Persistence-War-Modern/dp/0190849533] 

• Using network methods to understand the dynamic origins of human cooperation [link: DOI: 10.1073/pnas.1715357115] 

• Developing model-based statistics for detecting early warning signals of collapse of European Neolithic societies [link: https://doi.org/10.1073/pnas.1602504113]  

Why we are doing it

Over the past 100 years or more, social scientist in political science, sociology, anthropology, psychology, history, and related fields have developed robust heuristic theoretical models and frameworks to explain the breadth of human behavior, culture, and social organization. Yet many of these approaches, while promising significant benefits to society at large, remain contested due to lack of reproducibility, sparse or unique datasets, and inadequate formal models. Computational methods have the potential to solve many of these problems, but to achieve this goal requires adaptation to the unique demands of social science research.   

Why at Ohio State

TDAI offers an integrative physical, computational, and organizational infrastructure to facilitate such work as well as a large and engaged community of faculty. 

Learn More

Interested researchers can learn about upcoming events and engage in ongoing discussion by joining our listserv, following us on Twitter, and contacting us directly for more information. Undergraduate and graduate students are also encouraged to get involved.