SLOs | Polarization

Welcome to the Polarization Models module! This module explores how groups form opposing viewpoints, how moderate positions erode, and how social identities become divided. We’ll examine the mechanisms that drive political, social, and ideological polarization using computational modeling approaches.

Polarization models help us understand one of the most pressing challenges of contemporary society: how and why groups become increasingly divided in their beliefs, values, and behaviors. Through agent-based modeling, we’ll explore opinion dynamics, the role of social networks, media influence, and institutional factors that contribute to societal polarization and potential interventions.

Module Duration: 2 weeks

👩🏾‍🎓 Student Learning Objectives (SLOs)¶

By the end of this module, students will be able to accomplish the following SLOs:

Core¶

• Articulate limiting assumptions or limitations to the conclusions that can be drawn from the use of these methods and identify appropriate and inappropriate uses of such methods, as informed by a Reformed, Christian perspective.

• 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 (revisited in the context of polarization, ethics, and justice).

Conceptual¶

• Define different types of polarization (ideological, affective, social sorting)

• Explain mechanisms that drive opinion polarization and group formation

• Analyze the role of social networks, media, and algorithms in polarization

• Understand the relationship between polarization and democratic governance

Technical Skills¶

• Implement opinion dynamics models (voter model, Hegselmann-Krause, etc.)

• Model the effects of network structure on opinion formation

• Simulate media influence and algorithmic filtering on belief systems

• Analyze polarization metrics and measurement approaches

Critical Thinking¶

• Evaluate competing explanations for political and social polarization

• Assess the effectiveness of interventions designed to reduce polarization

• Critique the assumptions and limitations of polarization models

• Connect polarization theory to contemporary political and social challenges

Communication¶

• Present complex polarization dynamics to diverse audiences

• Discuss the implications of polarization research for democratic society

• Articulate the tension between diversity and social cohesion

• Engage constructively with politically sensitive topics and research

📚 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. 📖 Iyengar, S., Lelkes, Y., Levendusky, M., Malhotra, N., & Westwood, S. J. (2019). The Origins and Consequences of Affective Polarization in the United States. Annual Review of Political Science, 22(1), 129–146.

2. 📖 Baldassarri, D., & Gelman, A. (2008). Partisans without Constraint: Political Polarization and Trends in American Public Opinion. American Journal of Sociology, 114(2), 408–446. 10.1086/590649

3. 📖 Chueca Del Cerro, C. (2024). The power of social networks and social media’s filter bubble in shaping polarisation: an agent-based model. Applied Network Science, 9(1).

4. 📖 Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146–1151.

5. 📖 Dandekar, P., Goel, A., & Lee, D. T. (2013). Biased assimilation, homophily, and the dynamics of polarization. Proceedings of the National Academy of Sciences, 110(15), 5791–5796.

🔓 Optional Readings¶

1. 📖 Smaldino (2023). Modeling social behavior: Mathematical and agent-based models of social dynamics and cultural evolution. Chapter 9.

2. 📖 Petter Törnberg (2022). How digital media drive affective polarization through partisan sorting. PNAS.

3. 📖 Article: Science Corner: Who is My Neighbor?

🎥 Videos¶

1. Political Polarisation

2. The Rise of Political Polarization -- Goldman Stories: Henry Brady

3. Liberal vs. Conservative: A Neuroscientific Analysis with Gail Saltz | Big Think

🔗 Online Resources¶

1. Stanford Large Network Dataset Collection

2. Network Corpus

3. Colorado Index of Complex Networks

4. Gephi Blog

5. Kaggle - EDA: Gephi Network Analysis of YouTubers’ Interactions

🛠️ Software Tools¶

1. Gephi - Open-source network visualization and analysis software.