Overview and syllabus
Introduction
Hello, and welcome to the course website for Computational Social Science for Sustainability, developed and taught by Dr. Matthew Turner, and offered Winter 2025 for Stanford Doerr School of Sustainability undergraduates and graduate students! Join the course to develop a nuanced understanding of human social behavior while learning new computational and presentation skills in the R programming language.
Course structure
We meet twice weekly, currently scheduled for Monday and Wednesday 11:30am-12:50pm, but subject to change, location pending. Each week will have twin themes, one social science phenomenon (e.g., political polarization) and one computational technique or skill (e.g., agent-based modeling).
Syllabus
Each week will feature one lecture focused on a select topic from social science for sustainability (S3).
| Week | Lecture description | Lab description | Readings |
|---|---|---|---|
| 1 | Sustainable behaviors to adapt to a changing world | “Yes, and” storytelling exercise | McNamara et al. (2020); Jones, Ready, and Pisor (2021); Pisor et al. (2022); Galesic et al. (2023); Kling et al. (2024) |
| 2 | Social learning, social influence, and uncertainty | Agent-based model of adaptation diffusion and opinion dynamics | Otto and Whitlock (2013); Acerbi, Mesoudi, and Smolla (2022), Chapter 1; Flache and Macy (2011); Matthew A. Turner and Smaldino (2018); Matthew A. Turner et al. (2023a) |
| 3 | Group structure and metapopulation theory | Adaptation diffusion in group-structured social networks | Cikara and Van Bavel (2014); Derex and Boyd (2016); Matthew A. Turner et al. (2023b) |
| 4 | Animal studies; Bayesian modeling and statistical analysis | King Markov; Model fits following Silk et al. (2005) | McElreath (2020), Chapters 9 and 11: Ch. 9 introduces Markov Chain Monte Carlo, Ch. 11 uses Silk et al. (2005) data and follows their analysis |
The ten-week course will consist of ten full lectures and ten sections focused on computing skills with some lecture, but mostly hands-on practice to make sure students learn the skills they need to complete weekly project assignments. Weekly assignments will combine software development and writing, requiring students to write a mini 2-page journal article-style papers every week. Students will motivate and explain a model they help program, analyze model outputs and explain the results, and explain the broader importance of the work. These papers will follow the IMAD structure: Introduction, Model (or Methods), Analysis, and Discussion, also known as the IMRaD structure where “Results and” replaces “Analysis”.
Weekly assignments, midterm and final exams, midterm and final projects
Initial idea, subject to change!
- Weekly assignments: There will be eight weekly assignments total, taking weeks off for midterm and final weeks to focus on projects and exams. Each will be worth 10 points.
- Midterm and final exams: There will be a midterm and final written exam possibly including written mathematics, mathematical derivations, and pseudocode solutions to logic or programming problems. The midterm exam will be worth 20 points; the final will be 30 points.
- Midterm project: 30 points
- Final project: 40 points
Weekly assignment
10 points per weekly assignment: I, 2pts; M 2pts, Code 3pts; R 3pts; D 1 pt.
Midterm exam:
Written responses, formal calculations, explaining formalisms, writing pseudocode 20 pts
Final exam:
Written responses, formal calculations, explaining formalisms, writing pseudocode 40 pts
Midterm project:
Pick one of the weekly assignments and expand upon it based upon your own interests. Same grading as weekly project, but scaled up to 20 points. Minimum 2000 words in IMAD/IMRaD format.
Final project
I will suggest several potential final projects, and students may invent their own. Same grading distribution as weekly project, but scaled up to 40 points. Minimum 3000 words in IMAD/IMRaD format.