2. Agent-based Modeling
Winter 2026 (January 3 – March 24)
This course teaches students how to simulate human behavior and social learning in order to predict the possible outcomes of interventions aimed at spreading sustainable practices. Agent-based modeling (ABM) is a powerful computational technique that lets us explore how individual-level decisions interact with social networks and ecological contexts to produce collective outcomes.
Enrollment is free. Subscribe on Substack to get weekly lectures and updates.
The educational materials, the socmod R package, and any research we produce will always be openly available.
Course Overview
In this course, we use agent-based models to explore how sustainable behaviors diffuse through modern, complex social systems. Agents represent individuals, each with specified psychological traits, social contexts, and behavioral tendencies, drawn from psychology, sociology, evolutionary anthropology, and network science.
Students learn to design, build, and analyze ABMs, while strengthening their understanding of the behavioral and social sciences underpinning them.
Learning Objectives
By the end of the course, you will:
- Construct agent-based models of social learning and sustainability using the R programming language
- Simulate diffusion processes under varying ecological, social, and uncertainty conditions
- Apply network science concepts to understand how structure shapes collective dynamics
- Use high-performance computing techniques to run large-scale simulations
- Analyze large, complex datasets generated by ABMs
- Practice open science workflows using Git and GitHub
Weekly Topics
| Week | Date Range | Topic | Description |
|---|---|---|---|
| 1 | Jan 3 – Jan 9 | Introduction to ABM | Motivation, applications, setup |
| 2 | Jan 10 – Jan 16 | Agents and Rules | Simple behavioral rules |
| 3 | Jan 17 – Jan 23 | Networks and Diffusion | The structure of social interaction |
| 4 | Jan 24 – Jan 30 | Stochasticity | Variability across runs |
| 5 | Jan 31 – Feb 6 | Psychology in ABMs | Traits and biases |
| 6 | Feb 7 – Feb 13 | High-Performance Computing | Parallel runs, reproducibility |
| 7 | Feb 14 – Feb 20 | Data Analysis | Summarizing and visualizing outputs |
| 8 | Feb 21 – Feb 27 | Case Study | Policy scenarios |
| 9 | Mar 3 – Mar 9 | Interactive Dashboards | ABMs with Shiny |
| 10 | Mar 10 – Mar 16 | Student Presentations | Share findings |
| 11 | Mar 17 – Mar 24 | Final Projects | Student-designed ABMs |
Format and Access
- Weekly lectures released via Substack
- Hands-on exercises in R
- Interactive discussions: rolling enrollment lets new learners join anytime
Tools and Resources
- R + Shiny for modeling and interactive dashboards
- socmod R library: modeling social learning and sustainability
- Git & GitHub for version control and collaboration
- High-performance computing resources for large-scale simulations
Placement in the Curriculum
This is the second course in the Social Science for Sustainability curriculum, next being taught Winter 2026.
Preceding courses:
- Autumn 2025 → Introduction to Social Science for Sustainability
Subsequent courses:
- Spring 2026 → Opinion Change: Models and Measurement
- Summer 2026 → TBD