SLOs | Intro

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:

Core 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.

Conceptual¶

• 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.

Technical¶

• Get to know Netlogo’s interface and visualize some classic models.

• Install Netlogo and run your first simulation from the Library of models.

Critical Thinking¶

• 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.

Communication¶

• 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.

📚 Readings and Extra Materials¶

🔒 Required Readings¶

The required readings for this module are available by 📖 clicking in this link. You have to be logged in with your Calvin account to access them.

1. 📖 Durkheim: The Rules of Sociological Method and Selected Texts on Sociology and Its Method (Excerpt)

2. 📖 “Objectivity” in social science and social policy. Excerpt from the Methodology of the Social Sciences

🔓 Recommended Readings¶

1. 📖 Smaldino (2023). Modeling social behavior: Mathematical and agent-based models of social dynamics and cultural evolution. Chapter 1.

2. 📖 Netlogo Programming Guide (found in the same folder as the required readings above).

📽️ Inspirational Videos¶

• 🎥 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)

🛠️ Extra¶

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