Resources

 

Classes at Stanford

Complex Systems tools and techniques have been applied to a wide range of fields, including physics, chemistry, biology, geology, social systems, philosophy, engineering, and many others. Unfortunately, classes to learn these powerful tools are hard to come by, especially at Stanford. Students interested in learning more are encouraged to take the courses taught by the Stanford Complexity Group, which are typically 1 unit classes taught in the winter quarter. The class list below gives a description of a few other classes at Stanford that might meet a student’s Complex Systems curiosity!

Computational Modeling and Analytics in Social Science

EDUC 390X – CS 424M
Prof. Paulo Blikstein
This hands-on modeling course is a great way to get acquainted with agent based models. Blikstein covers a wide range of topics including emergence, complex systems, network science, and machine learning while teaching students how to use a number of programming tools such as NetLogo, Sonia, and R. This is an excellent course to become familiar with social science modeling while getting first hand experience with modeling tools. Unfortunately, the course is not taught every year. Email Dr. Blikstein for more information on the next class. The Syllabus is available here.

Introduction to Computational Social Science

MS&E 331
Prof. Sharad Goel
This course is an excellent introduction into computational social science. Topics covered include data visualization, mathematical modeling, and data processing skills. This course assumes basic coding skills and provides a large amount of hands on experience with data.
The class website is here.

Ants: Behavior, Ecology, and Evolution

BIO 31Q
Prof. Deborah Gordon
This class is mainly focused at Sophomores, but students will learn about emergence in ant colonies. Simple rules governing the behavior of individual ants result in complex movements, structures, and behaviors when aggregated.

Social and Information Networks

CS 224W
Prof. Jure Leskovec
How do diseases spread? Who are the influencers? How can we predict friends and enemies in a social network? How information flows and mutates as it is passed through networks? Behind each of these questions there is an intricate wiring diagram, a network, that defines the interactions between the components. And we will never understand these questions unless we understand the networks behind them. The course will cover recent research on the structure and analysis of such large social and information networks and on models and algorithms that abstract their basic properties. Class will explore how to practically analyze large-scale network data and how to reason about it through models for network structure and evolution. Topics include methods for link analysis and network community detection, diffusion and information propagation on the web, virus outbreak detection in networks, and connections with work in the social sciences and economics.