Many systems can be modeled as being composed of agents interacting with one another and their environment. Agent based modeling (ABM) can be used to explain phenomena in the biological and social sciences that are driven by multi-agent interactions, ranging from evolution, to epidemic spread, to flocking, to cooperation, to racial segregation in neighborhoods. Agent based modeling allows us to explore how simple rules governing agent behavior can lead to remarkably complex emergent phenomena. In this course students will use Python to explore and modify well-studied agent based models of complex systems, as well as formulate models of their own.

Course Requirements:

There will be 8 homework assignments. In each assignment, students will be tasked with constructing or modifying an agent based model based on material introduced in lecture or reading. The subjects of the models may include evolution, animal and plant behavior, epidemic spread, social networks, and human interaction. Students will comment on the effects of varying parameters on outcomes of the models.

Intended Audience:

Undergrads interested in a general and flexible tool applicable in many different subjects ranging from the natural to the social sciences, e.g. how to model the spread of disease over human contact networks in epidemiology, and how to model predator-prey dynamics in ecology.