Complex Systems: Modeling Complex Systems I

Integrative breadth-first introduction to computational methods for modeling complex systems; dynamical systems, numerical methods, cellular automata, agent-based computing, game theory, genetic algorithms, artificial neural networks, and complex networks. Semester team-based project. Pre/Co-requisites: Computer programming in any language; calculus, linear algebra recommended. Cross-listed with: CS 6020.

Graduate standing only; Cross-listed with CS 6020 A; Total combined enrollment: 45; Open to Degree and PACE students

Integrative breadth-first introduction to computational methods for modeling complex systems; dynamical systems, numerical methods, cellular automata, complex networks, game theory and agent-based models. Prerequisite: Graduate standing, computer programming in python, basic calculus, and statistics are required, linear algebra recommended.

This course combines lecture and discussion formats along with recorded supporting material. On top of occasional assignments in the first half of the course, students should expect weekly readings outside of class, with additional time for a final team project. We use the following textbook as complementary text: "Introduction to the Modeling and Analysis of Complex Systems" by Hiroki Sayama.

Grades are based on class participation, biweekly assignments over the first half of the semester, weekly quizzes on readings, and a final team project.