Overview

Is everything predictable given enough data and powerful algorithms? This seminar explores that question through social science and computer science research in over a dozen domains including life trajectories of individuals, wars, weather, disease outbreaks, social media, and image classification. We aim to identify fundamental limits, learn about common pitfalls, and explore policy implications. Coursework is a mix of reading and empirical work tailored to students’ backgrounds. The course, which is for graduate students in both computer science and the social sciences, is designed to facilitate publishable student research.

Learning objectives

• Students will be able to describe theories of predictability and unpredictability in different scientific domains.
• Students will be able to compare and correctly apply commonly used measures of predictive performance.
• Students will be able to evaluate the appropriateness of prediction as a scientific or policy goal.
• Students will be able to make predictions about the future of prediction.
• Students will be able to create new research that helps understand the limits of predictability.