Research Workshop „Agent-based Modelling of Political Polarization”
Date: 4.- 6. May 2022
Instructor: Dr. Patrick Mellacher
- Basics of Agent-based Modelling
- Types of opinion dynamics models
- Step-by-Step creation of opinion dynamics models in NetLogo
- Quantitative analysis of simulation models in R
Political polarization is arguably one of today’s major challenges, as it impedes public policy solutions to other main challenges such as climate change. Agent-based models (ABMs) have proven to be particularly useful to study polarization and have proposed several explanations for the emergence and persistence of polarization. After a short introduction to basic concepts of political polarization and agent-based modelling, participants of this workshop will learn how to develop own agent-based models of opinion formation in the programming language NetLogo and to analyze them quantitatively using the programming language R. The methods taught in this workshop are highly flexible and can serve as the basis to investigate other research questions and topics in economics and social science.
No previous knowledge in NetLogo or R is required, but basic programming knowledge will certainly help to follow the workshop. Participants should install NetLogo and RStudio prior to the start of the workshop.
This course is open for advanced masters students and PhD candidates. If you would like to participate, please send a short email describing your motivation, together with your CV, and with a statement of whether you would like to participate virtually or in person, to xanthi.tsoukli(at)uni-bamberg.de before 22.04.2022. You will be notified if you are allowed to participate in the workshop by 25.04.2022.
The course will be held in a hybrid-mode. Upon acceptance, participants must make their own travel arrangements for their stay in Bamberg, if the wish to participate in the workshop in person. No financial support is available.
About the Instructor:
Dr. Patrick Mellacher is a postdoctoral researcher at the Graz Schumpeter Centre of the University of Graz. His research agenda is located at the intersection between economics, political science and epidemiology, and focuses on studying diffusion processes and their consequences with the help of agent-based models (ABMs). Among others, he developed ABMs to study i) the interrelation between technological change, market concentration and inequality, ii) the interplay between inequality, misinformation and political decision making, iii) economic-epidemiological trade-offs of virus containment policies, and iv) the implications of viral evolution in the context of the Covid-19 crisis. He currently works for a project on Agent-based Economic Epidemiology funded by the Austrian Science Fund FWF which builds on his COVID-Town model, for which he also received the EAEPE Herbert Simon Prize 2021. He aims to connect his research to relevant real-life problems and is the spokesperson of the cluster “Social Policy and Distribution” at the University of Graz research network “Heterogeneity and Cohesion”.
Wednesday, May 4th
11:00-12:30 – Introduction to Agent-based Modelling.
Polarization: Theory and Empirics
12:30-14:30 – Lunch break
14:30-16:00 – Step-by-step creation of simple opinion formation models with NetLogo I
Thursday, May 5th
9:00-10:30 – Step-by-step creation of simple opinion formation models with NetLogo II
10:30-11:00 – Coffee break
11:00-12:30 – Modelling sources of opinion polarization and fragmentation I: Bounded confidence and forceful agents
12:30-14:30 – Lunch break
14:30-16:00 - Modelling sources of opinion polarization and fragmentation II: Negative influence and homophilic social interactions
18:30 – Dinner
Friday, May 6th
9:30-11:00 – Quantitative analysis of Agent-based Models with R and ggplot2
11:00-11:30 – Coffee break, Q&A
- Edmonds, B., Le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., ... & Squazzoni, F. (2019). Different Modelling Purposes. Journal of Artificial Societies and Social Simulation, 22(3), 1-6.
- Esteban, J. M., & Ray, D. (1994). On the Measurement of Polarization. Econometrica 62(4), 819-851.
- Fiorina, M. P., & Abrams, S. J. (2008). Political polarization in the American public. Annual Review of Political Science, 11, 563-588.
- Gräbner, C. (2018). How to relate models to reality? An epistemological framework for the validation and verification of computational models. Journal of Artificial Societies and Social Simulation, 21(3).
- Acemoglu, D., Ozdaglar, A., & ParandehGheibi, A. (2010). Spread of (mis) information in social networks. Games and Economic Behavior, 70(2), 194-227.
- Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5(3).
- Mark, N. P. (2003). Culture and competition: Homophily and distancing explanations for cultural niches. American Sociological Review, 68(3), 319-345.
- Mäs, M., & Flache, A. (2013). Differentiation without distancing. Explaining bi-polarization of opinions without negative influence. PloS one, 8(11), e74516.
- Mellacher, P. (2021). Opinion Dynamics with Conflicting Interests. GSC Discussion Paper No. 28