EES 4760/5760: Agent- and Individual-Based Computational Modeling

Applications in natural, social, and behavioral sciences and engineering. Designing, programming, and documenting models. Using models for experiments. Examples from environmental science, ecology, economics, urban planning, and medicine. Familiarity with basic statistics and proficiency in algebra are expected..

Instructor   Jonathan Gilligan
Classroom Stevenson 2200
Class Times Tu/Th 11:00–12:15

Office Hours Mon. 10:00–11:00, Thurs. 2:40–3:30
SC 5735 (7th floor, Stevenson Bldg 5)


Agent-based and individual-based modeling has become a powerful tool for research in many fields, including anthropology, civil engineering, computer science, ecology, economics, epidemiology, marketing, medicine, political science, public policy, robotics, sociology, transportation, and urban planning.

Agent-based modeling is used to study how individual agents (which can represent people, animals, plants, cars, robots, or packets of information in a communications network) making simple decisions can produce complex and unexpected collective behavior through their interactions. Agent-based models have been used to investigate racial segregation in American cities, traffic jams, adaptation to global warming, disease outbreaks, inflammatory response to wound infections, ecosystem dynamics, impacts of changing land-use on tropical rain forests, political instability, and market penetration of home solar-energy systems.

This course will provide an introduction to agent and individual-based modeling. You will learn how to design, program, and document agent-based computational models using the free open-source \NetLogo\ environment. You will use these models scientifically to perform computational experiments and interpret the results.

You do not need to have any prior knowledge of computer programming, but I do expect that you are familiar with basic statistics and algebra. We will use the \NetLogo\ environment for writing and running agent-based models. \NetLogo\ is a widely used system that is both powerful and easy to learn, so you can quickly start to program your own models.


We will use Steven F. Railsback and Volker Grimm, Agent-Based and Individual-Based Modeling: A Practical Introduction (Princeton, 2011) as the primary text for this course. The book has a companion web site, where you can find a list of errata and download supporting materials, including design documents, data files, and starter code for models we will work on during the term.

I have also posted the errata and the supporting materials on this site, and link to the relevant supporting files from the notes on the relevant reading and homework assignments pages.