Join us for a lively and thought-provoking session from the University of Chicago's Center for Spatial Data Science. This lecture dives into the fascinating concept of emergence—how simple interactions at the micro level can give rise to complex patterns and behaviors at the macro level. The instructor sets the stage for upcoming experiments, blending theoretical grounding with practical setup. Along the way, you'll hear candid moments, student interactions, and glimpses of the behind-the-scenes dynamics of a collaborative learning environment. Whether you're new to complex systems or brushing up on foundational ideas, this session will inspire new ways of thinking about order, organization, and unexpected outcomes in social systems.

Here, I show how my dissertation work can strengthen spatial analyses of growth and inequality. The central theory of "Cooperation and Inequality in Stochastic Models of Growht" holds here, but I discuss its implications for spatial inequality. Intended audience: Economists, social scientists.

Intended audience: Academic researchers interested in network science, inequality, and stochastic processes.

Intended audience: Statistical physicists, biophysicists, and machine learning researchers.