Computational Modeling of Social Systems

Topics

The course will cover the following computational approaches to the modelling of social systems:

• Agent-based modelling: segregation and cultures
• Opinion dynamics: voter models bounded confidence
• Models of network formation and their application to social networks
• Models of social dynamics on networks

Format

The course takes the form of a seminar in two phases:

1. Lectures on basics of computational modelling (9 sessions)
2. Student presentations on selected readings in computational modelling (5 sessions)

Grading

Students select a published article from a set of readings to present in the second part and to write a short review of the article. The course grade is based on the student presentation (50%), participation in discussions after each presentation (20%), and on the report (30%).

Students will have 15 for the presentation, followed by 5 to 10 minutes for questions. The report should be around 5 pages. If you want to include more details (for instance more information about the dataset or mathematical methods) you can use an appendix (up to 3 pages).

Exercises

A practical part of exercises connected to the lectures will be offered. It is highly recommended to participate in this exercise part because it will help to acquire the skills that are important for the final project. The exercises will not be graded.

Seminars

Students Seminars

Swaps are possible if both students agree. There can be up to 4 seminars per day, so you can ask me by email to be moved to a non-full date (first come first served). I will update this calendar when changes are made, so be sure to check it once in a while.

Most of the material used in this series of seminars is derived from Prof. David Garcia "Computational Modelling of Social Systems" course. You can find useful material on his website.

Seminars

Coding Sessions

• A Weighted Balance Model of Opinion Hyperpolarization. Simon Schweighofer, Frank Schweitzer, David Garcia (2020)
• From Private Attitude to Public Opinion: A Dynamic Theory of Social Impact. Andrey Nowak, Jacek Szamrej, Bibb Latané (1990)
• Biased assimilation, homophily, and the dynamics of polarization. Pranav Dandekar, Ashish Goel, and David Lee (2013)
• A simple model of global cascades on random networks. Duncan Watts (2002)
• Assessing the impact of SARS-CoV-2 prevention measures in Austrian schools using agent-based simulations and cluster tracing data. Lasser et al. (2022)
• Stress-testing the resilience of the Austrian healthcare system using agent-based simulation. Kaleta et al. (2022)
• Coupled catastrophes: sudden shifts cascade and hop among interdependent systems. Charles D. Brummitt, George Barnett, Raissa M. D'Souza (2015)
• Friend of a friend models of network growth. Watson Levens, Alex Szorkovszky, and David J. T. Sumpter (2022)
• Explosive percolation in random networks. Dimitris Achlioptas, Raissa M D'Souza, Joel Spencer (2009)
• Emergence of Communities in Weighted Networks. Jussi M. Kumpula, Jukka-Pekka Onnela, Jari Saramäki, Kimmo Kaski, and János Kertész (2007)
• The Simmel effect and babies' names. M.J.Krawczyk, A.Dydejczyk, K.Kułakowski (2014)
• The role of endogenous and exogenous mechanisms in the formation of R&D networks. Mario V. Tomasello, Nicola Perra, Claudio J. Tessone, Márton Karsai & Frank Schweitzer (2014)