The digital transformation of business and society has made it increasingly simple to collect large datasets about almost every part of our lives. While just a decade ago the datasets used in social science, business and economics were (mostly) large or deep but not both, we can now study detailed individual-level data on a societal scale.

This is not just a passing fad. We believe that the data revolution and modern tools from artificial intelligence, data science, and machine learning can substantially influence social science and economics research. Modern approaches in data science provide unprecedented value in the study of important phenomena and can help build new and better theory, which is instrumental in solving pressing societal problems.

The Computational Economics and Social Sciences chapter of the Data Science Center strategically selects domains of interest, acquires deep domain knowledge in these areas, and then studies data from the selected domains.

Selected Research Projects

• Collaboration and delegation between humans and artificial intelligence
• Power TAC - Competitive Benchmarking: An Approach to Address Wicked Problems with Big Data and Analytics
• Structural Breaks and Time Variation in High-Dimensional Dependencies