Data Analytics and Computation 690B - Agent-BasedMod/SocComplexRsrch

This course introduces students to computational approaches for studying complex social systems, combining foundations from complexity science with agent-based modeling (ABM) as a tool for theory construction, exploration, and policy-relevant reasoning. The course begins by developing core concepts of complexity - nonlinearity, feedback, emergence, attractors, and sensitivity to initial conditions - and uses these ideas to motivate why many social and policy phenomena cannot be adequately understood through linear, equilibrium-based, or purely variable-centered models. Topics may include social diffusion, threshold behavior, contagion, collective action, enforcement, network influence, delay, and accumulation. Through hands-on labs using NetLogo and/or BehaviorSpace, students explore how design choices, scale, and interaction structure shape system behavior. The course is designed for students in the social sciences, public policy, and related fields. No prior programming experience is required.