Data-driven models are increasingly used to simulate and make predictions about complex systems, from online shopping preferences and the performance of the stock market to the spread of disease and political unrest. But while powerful methods in machine learning and computational social science improve at predicting the future, they often lack the ability to explain why those results occur, rendering these models less helpful for shaping interventions and policy.
Social MIND, or Social Machine Intelligence for Novel Discovery, aims to reorient these models to emphasize prediction, explanation and intervention. With a $2 million grant from the Defense Advanced Research Projects Agency (DARPA) as part of its Ground Truth program, the collaboration between researchers at the University of Chicago (including Knowledge Lab and UChicagoCS) and the Massachusetts Institute of Technology will build a “model of models” that combines computational approaches and pits them against each other to reveal the underlying factors driving social systems, as well as potential points of intervention.
“What we’re trying to do in social science is develop powerful explanations,” said James Evans, co-primary investigator of Social MIND and professor of sociology at UChicago. “Machine learning models generate predictions, most of which are not elegant or beautiful explanations. We need to tune, tame and refocus machine learning on the task of identifying the best explanations, those that allow us to understand and change the world.”
Michael Franklin, Liew Family Chair of Computer Science at UChicago, is a co-PI on the grant. For more on Social MIND, read the full article at UChicago News. The project is currently seeking postdoctoral researchers.