Welcome to the Introduction to Agent-Based Modeling & Social Theory module! This foundational module introduces the intersection of computer science and social science, exploring how computational methods can help us understand human behavior and social phenomena.
This module provides the theoretical and practical foundations for the course. We’ll explore what models are, why they’re useful in social science research, and how computational approaches can reveal insights about complex social systems. We will also introduce agent-based modeling as a key method for simulating social phenomena.
Module Duration: 2 weeks
Student Learning Objectives (SLOs)¶
By the end of this module, students will be able to accomplish the following SLOs:
Introduce students to observational research methods for gathering empirical evidence and to theories that provide analytical frameworks for such evidence.
Provide students with a sense of the nature and limits of scientific knowledge and the kinds of ethical questions that surround scientific research and its dissemination.
Study how ideas from mathematical sciences have reflected and shaped other ways of thinking and knowing.
Define what constitutes a model in social science research
Explain the relationship between social computing and traditional social science methods
Reflect on the idea of emergence introduced by Durkheim and how it sets the stage for ABM.
Identify strengths and limitations of computational modeling approaches
Understand the role of abstraction in model building
Comprehend the relevance of using Agent-based models for simulating social phenomena
Get to know Netlogo’s interface and visualize some classic models
Install Netlogo and run your first simulation from the Library of models
Evaluate when computational modeling is appropriate for social questions
Assess the validity and reliability of model assumptions
Connect abstract models to real-world social phenomena
Critique modeling choices and their implications
Articulate the purpose and value of social modeling
Initiate discussions on interpreting model results to both technical and non-technical audiences
Discuss ethical considerations in social modeling research
Engage with interdisciplinary perspectives on social phenomena
📋 Weekly Breakdown¶
Week 1: Tuesday, September 2
Heckman Library 406C
Session A:
Summary: What is ABM? Live demo in NetLogo (Starling’s murmuration, Wolf-Sheep Predation, and fire model).
Session B:
Summary: Course orientation. Syllabus overview, grading, expectations.
Introduction to project & SRGs.Slides: Course Orientation
Week 1: Thursday, September 4
Heckman Library 406C
Session A:
Summary: Emergence of the social sciences; classical theories; fundamental debates.
Sociology distinct from psychology: focus on emergence and social facts (Durkheim).Slides: Emergence of the social sciences
Session B:
Summary: Models and methods: Where does ABM fit? Connect emergence to ABM: micro → macro link as ABM’s strength.
Discussion: “What kinds of social problems make sense only at the level of emergence?”Slides: ABM Fundamentals
Week 2: Tuesday, September 9
Heckman Library 406C
Session A (SRG):
Summary: Discussion of readings (Durkheim & Weber).
SRG goal: Contrast Durkheim’s irreducible social facts with Weber’s abstraction/ideal types.
Session B:
Summary: Workshop — What makes a good model?
Groups evaluate “bad” and “good” toy models.
Criteria: simplicity, clarity, generativity, transparency, theory link.Slides: Models and Methods
Week 2: Thursday, September 11
Heckman Library 406C
Session A (Lab): NetLogo basics—interface, parameters, behaviors.
Session B (Lab): Micro-exercise: Adjust one parameter in a simple model, note results.
📝 Assignments & Due Dates (Weeks 1–2)¶
Due: 9/16 before class | Points: 20 points
Prompt (1-2 pages):
Pick one of the toy models shown in our classes over these two weeks (except the Fire model), and perform a single parameter adjustment.
Setup: Identify which starter model you used (e.g., Wolf-Sheep, Traffic, Fire).
Parameters & Code: Adjust one parameter; note what you changed. If you tried editing code, briefly explain what.
Results: Describe what happened compared to the default. Include screenshot(s).
Interpretation: What does this show about how simple rules create different outcomes?
Write your Lab Memo. You can download the template in here.
Submit your Lab Memo in PDF format through Moodle.
Due: 9/16 before class | Points: 30 points
Prompt (≥1000 words):
How do Durkheim and Weber differ in their approaches to building knowledge?
Where do you see common ground?
How might ABMs fit into ongoing discussions about subjectivity, objectivity, and building valid social knowledge?
📚 Readings and Extra Materials¶
Halls, W. D., & Lukes, S. (1982). Durkheim:The Rules of Sociological Method and Selected Texts on Sociology and Its Method. Excerpt.
Weber, M. (1949). " Objectivity" in social science and social policy. The methodology of the social sciences, 49-112. Excerpt.
Smaldino, P. (2023). Modeling social behavior: Mathematical and agent-based models of social dynamics and cultural evolution. Chapter 1.
🎥 The Power of Models (4 min)
🎥 Top 3 aspects people get wrong about Agent Based Modeling (9 min)
🎥 When is a system complex? (3 min)
🎥 Emergence – How Stupid Things Become Smart Together (7 min)
Additional Resources
Interactive Demos:
🖥️ NetLogo Web Models - Run models in your browser
🖥️ Complexity Explorer - Free courses on complexity science
🖥️ Agent-Based Models in Social Science - Gallery of examples
Tools and Software:
💻 NetLogo: Download and Documentation
Real-World Applications¶
Social Computing in Action
Current Applications:
Epidemiological modeling for public health policy
Urban planning and smart city initiatives
Social media analysis and digital humanities
Economic forecasting and market simulation
Discussion Questions:
How do computational models differ from traditional social science methods?
What are the ethical considerations when modeling human behavior?
How can we validate models of social phenomena?
What role should domain expertise play in computational social science?
Case Studies:
COVID-19 pandemic modeling and policy responses
Social media influence on political polarization
Urban segregation and housing policy analysis