GESIS Spring Seminar 2022: Simulation Techniques in the Social Sciences
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GESIS Spring Seminar 2022:
Simulation Techniques in the Social Sciences
Syllabus for week 1:
“Agent-based Modelling in the Social Sciences"
Lecturers: Prof. Dr. Andreas Flache Carlos de Matos Fernandes (M.Sc.) Tanzhe Tang (M.Sc.)
Email: a.flache@rug.nl c.a.de.matos.fernandes@rug.nl t.tang@rug.nl
Date: 07-11 March 2022
Time: 09:30-12:30 lectures. Lunch break 12:30-14:00. Hands-on tutorial sessions 14:00-17:00. Time for individual
consultation 17:00-17:45 or on individual appointment. All online sessions will have regular short breaks.
Time zone: CEST/CEDT
Venue: Online via Zoom
About the Lecturers:
Andreas Flache is professor of sociology at the University of Groningen in the Netherlands. His research addresses
intergroup relations in a diverse society, opinion dynamics, cooperation problems, social networks, and social
learning. Andreas uses agent based computational and game theoretical modeling, laboratory experiments, and
network research. He leads externally financed research projects in these areas. Andreas published in social science
journals like American Journal of Sociology, Journal of Conflict Resolution, Organization Science, as well as in
multidisciplinary journals like the Proceedings of the National Academy of Sciences or Science, and specialized
journals like Sociology of Education, Rationality and Society, or Journal of Mathematical Sociology. He holds a
master’s degree in Computer Science and a PhD in Social Sciences.
For more information see: https://flache.gmw.rug.nl/
Google scholar: https://scholar.google.nl/citations?user=dt9jPJ0AAAAJ&hl=en
Carlos de Matos Fernandes is a PhD student at the department of Sociology and the Interuniversity Center for Social
Science Theory and Methodology (ICS) at the University of Groningen in the Netherlands. His PhD project revolves
around cooperation and how individual-level features, such as social value orientations, and personality traits, as
well as social mechanisms, such as meritocratic matching, social learning, and selection, jointly affect cooperation
levels both on the individual and collective level. Carlos studies these processes by means of agent-based modeling
and social network analysis.
For more information see: https://www.rug.nl/staff/c.a.de.matos.fernandes/
Google scholar: https://scholar.google.nl/citations?hl=en&pli=1&user=AvGDOBAAAAAJ
Tanzhe Tang is a Postdoctoral researcher at the Faculty of Behavioural and Social Sciences at the University of
Groningen in the Netherlands. His research interest is opinion dynamics, agent-based modelling, and complex
systems. In particular, he works on the relations between opinion and behaviour and how this is related to opinion
polarization. Recent work has been published in, among others, the Journal of Mathematical Sociology and the
Journal of Artificial Societies and Social Simulation.
Course Description:
Agent-based computational modeling (ABCM, or often just ABM) increasingly attracts social scientists as a tool for
unravelling the complex dynamics which often underlie puzzling social phenomena such as segregation, cultural
diversity, opinion polarization, or collective action. ABCM is an approach for theory elaboration that combines
analytic precision, ability to capture complex micro-macro interactions in a computational model, and flexibility to
accommodate empirically realistic assumptions. This course gives an introduction to ABCM for social scientists,
1focusing on its use for theory building and on best practices for systematic experimentation and analysis of models.
In tutorials accompanying the lectures, participants get a “hands-on” introduction to software tools specifically
designed for ABCM of complex (social) dynamics. Participants learn to build ABCM from scratch for a range of core
domains in the social sciences, including segregation, cooperation, cultural diversity, and opinion polarization and
how to systematically experiment with these models for gaining a deep understanding of them. Throughout the
lectures, “classical” and more recent ABCM will be introduced and explained, while participants learn to work,
experiment, and extend these models in tutorial sessions. Participants will specifically work with two software tools:
NetLogo, a widely used software and defSim, a new Python-based tool for modelling social influence dynamics.
Keywords:
Agent-based computational modelling, social complexity, NetLogo, segregation, polarization, cooperation
Course Prerequisites:
▪ Interest in and affinity with formal (computational, mathematical) modelling of social processes
▪ Master, research-master, PhD-student or postdoctoral level in a social science discipline or related disciplines
▪ No specific prior programming programing experience required
▪ Affinity with formalizing social-scientific theories is desirable
Target Group:
Participants will find the course useful if:
▪ Master, research-master or PhD-student level in a social science discipline or related disciplines.
▪ Affinity with formalizing social-scientific theories
Course and Learning Objectives:
By the end of the course participants will:
▪ Understand the use of ABCM for analysing complex social processes
▪ Understand the concept of social complexity and its relation to ABCM
▪ Know and understand the substantive problems, theories and classical as well as recent related computational
models in a number of core areas of social scientific research (e.g., segregation, diversity, polarization,
cooperation)
▪ Be able to build ABCM models tackling these phenomena from scratch, using NetLogo or defSim (a tool based
on Python)
▪ Be able to apply a set of good practices for developing a deep understanding of complex social dynamics
through systematic experimentation with ABCM.
▪ Be able to reflect upon the strength and pitfalls of ABCM as a tool for theory development in the social sciences.
Organisational Structure of the Course:
Course structure: there will be about three hours of classroom instruction and about three hours of hands-on tutorials
and exercises per day. The lectures will introduce and explain the theory and methods in ABCM research, substantive
problem areas in the social science literature and the specific ABCM and their contribution to scientific insight per
domain. In the tutorials, participants are expected to work individually or in small groups on building from scratch,
applying and extending ABCM for specific research questions discussed in the lectures. They will work based on
template models and instructional material provided by the lecturers. Tutors experienced with the software tools
(NetLogo, defSim) and sample models will provide hands-on guidance and supervision.
Depending on interest of the students, some time can be made available for consultation on students’ own ABCM
projects.
2Software requirements:
Participants will need to use their own computers for purposes of conducting exercises. The following programs will be
used, one of which has to be installed before the workshop:
NetLogo, most recent version, freely available at https://ccl.northwestern.edu/netlogo/download.shtml.
Participants should install NetLogo on their machines prior to the workshop.
defSim (open source, based on Python, can be used with google colab, colab.research.google.com/). See further
https://defsim.github.io/defSim/. No pre-installation needed.
Recommended Literature to look at in advance:
No specific recommendations. Participants are encouraged to read the required readings of day 1 in advance.
Day-to-day schedule and literature:
Day 1: General introduction: Agent-based modelling and social complexity
▪ Agent-based computational modelling in the social sciences
▪ Theoretical foundations: micro-macro perspective, analytical sociology and social complexity
▪ Examples of complex social phenomena: e.g. segregation, cultural dynamics, polarization
▪ Tutorial session (afternoon): Participants will be introduced to NetLogo, practice with coding in the NetLogo
environment, and already build, analyze, and extend an ABCM of segregation from scratch.
Literature:
Flache, A., de Matos Fernandes, C. 2021. "Agent-based computational models". Pp 453–473 in: Manzo, G. (ed.)
Research Handbook on Analytical Sociology. Research Handbooks in Sociology series. Edward Elgar Publishing. DOI:
https://doi.org/10.4337/9781789906851.00033 [with supplementary sample NetLogo models]. Open Access.
Macy, M. W. and Flache, A. (2009) ‘Social dynamics from the bottom up: Agent-based models of social interaction’,
in Hedström, P. and Bearman, P. (eds) The Oxford Handbook of Analytical Sociology. Oxford, England: Oxford
University Press, pp. 245–268. doi: 10.1093/oxfordhb/9780199215362.013.11.
Suggested reading:
DellaPosta, D., Y. Shi, M. Macy. 2015. Why do Liberals drink lattes? American Journal of Sociology 120(5): 1473–1511.
Macy, M.W. & R. Willer. 2002. From Factors to Actors: Computational Sociology and Agent-Based Modeling. Annual
Review of Sociology 2002 28:1, 143-166.
Day 2: Developing theoretical explanations of empirical phenomena with ABM: good practices
▪ Adding realism: systematic model analysis and experimentation
▪ Good practices in analysing ABM
▪ Using discrete choice models to bridge ABM and choice data
▪ Problems and pitfalls
▪ Tutorial sessions (afternoon): “injecting realism” stepwise, implementing and analysing ABCM with discrete
choice models [NetLogo]. Time permitting, students can be introduced to working with several NetLogo
models from the literature (see e.g. Sage & Flache 2021 or Manzo et al, 2018).
Literature:
Flache, A., de Matos Fernandes, C. 2021. "Agent-based computational models". Pp 453–473 in: Manzo, G. (ed.)
Research Handbook on Analytical Sociology. Research Handbooks in Sociology series. Edward Elgar Publishing. DOI:
https://doi.org/10.4337/9781789906851.00033 [with supplementary sample NetLogo models]. Open Access.
Sage, L., & Flache, A. 2021. Can ethnic tolerance curb self-reinforcing school segregation? A theoretical Agent Based
Model. Journal of Artificial Societies and Social Simulation 24 (2) 2. http://jasss.soc.surrey.ac.uk/24/2/2.html
3Suggested readings:
Bruch, E. E. and Atwell, J. (2015) ‘Agent-Based Models in Empirical Social Research’, Sociological Methods and
Research, 44(2), pp. 186–221. doi: 10.1177/0049124113506405.
Bruch, E. E. and Mare, R. D. (2006) ‘Neighborhood choice and neighborhood change’, American Journal of
Sociology, 112(3), pp. 667–709. doi: 10.1086/507856.
Clark, W. A. V and Fossett, M. (2008) ‘Understanding the social context of the Schelling segregation model’,
Proceedings of the National Academy of Sciences of the United States of America, 105(11), pp. 4109–4114. doi:
10.1073/pnas.0708155105
Manzo, G. et al. (2018) ‘Complex Contagions and the Diffusion of Innovations: Evidence from a Small-N Study’,
Journal of Archaeological Method and Theory, 25(4), pp. 1109–1154. doi: 10.1007/s10816-018-9393-z.
van de Rijt, A., Siegel, D. and Macy, M. W. (2009) ‘Commentary and Debate’, American Journal of Sociology, 114(4),
pp. 1166–1180. doi: 10.1086/588795.
Day 3: Cooperation, learning, and social control
▪ The problem of cooperation
▪ Modelling the emergence of cooperation: evolutionary models and learning models
▪ Social control and cooperation
▪ The weakness of strong ties
▪ Potentials and pitfalls of “meritocratic matching” as solution for the problem of cooperation
▪ Tutorial session: Students will learn to work with and understand NetLogo implementations of representative
models from the literature, including models of learning in PD games, cooperation in spatial contexts and
models of the emergence, effects and pitfalls of solutions to the problem of cooperation, like social control or
meritocratic matching.
Literature:
Macy, M.W., A. Flache (2002) Learning Dynamics in Social Dilemmas, Proceedings of the National Academy of
Sciences U.S.A. May 14;99(10): 7229-7236.
Flache, A., M.W. Macy, 1996. The Weakness of Strong Ties: Collective action failure in a highly cohesive group.
Journal of Mathematical Sociology 21:3-28.
Suggested readings:
Axelrod, R. (1984) The Evolution of Co-operation, London: Penguin (first published 1984), chapters 1 and 2, pp. 1-54.
Bianchi, F. and Squazzoni, F. (2015), Agent-based models in sociology. WIREs Comput Stat, 7: 284-306.
https://doi.org/10.1002/wics.1356 (A general overview paper containing many examples of models of cooperation)
de Matos Fernandes, Carlos A., Flache, Andreas, Bakker, Dieko M. and Dijkstra, Jacob (2022) 'A Bad Barrel Spoils a
Good Apple: How Uncertainty and Networks Affect Whether Matching Rules Can Foster Cooperation' Journal of
Artificial Societies and Social Simulation 25 (1) 6 . doi:
10.18564/jasss.4754
Helbing, D., Yu, W. 2009. The outbreak of cooperation among success-driven individuals under noisy conditions.
Proceedings of the National Academy of Sciences Mar 2009, 106 (10) 3680-3685; DOI: 10.1073/pnas.0811503106
Macy, M.W. 1993. Backward-Looking Social Control. American Sociological Review 58. 819-836.
Heckathorn, D.D. 1996. The Dynamics and Dilemmas of Collective Action. American Sociological Review 61: 250-277.
Zschache, J. (2018) ‘Melioration learning in iterated public goods games: The impact of exploratory noise’, The
Journal of Mathematical Sociology, 42(1), pp. 1–16. doi: 10.1080/0022250X.2017.1396983.
Day 4: Modelling social influence: consensus, diversity, and polarization
▪ The problem of explaining opinion diversity and polarization
▪ ABCM of the emergence of opinion diversity
▪ ABCM identifying conditions and mechanisms of polarization
▪ Spatial segregation and opinion polarization: two sides of the same coin?
4▪ Tutorial session: building from scratch and modifying social influence models of cultural diversity and
polarization from the literature with defSim / Python. Learning to conduct simulation experiments with defSim
/ Python.
Literature:
Axelrod, R. 1997. “The Dissemination of Culture - A Model with Local Convergence and Global Polarization.” Journal
of Conflict Resolution 41(2):203–26.
Flache, Andreas, Mäs, Michael, Feliciani, Thomas, Chattoe-Brown, Edmund, Deffuant, Guillaume, Huet, Sylvie and
Lorenz, Jan (2017). “Models of Social Influence: Towards the Next Frontiers”. Journal of Artificial Societies and Social
Simulation 20 (4) 2. (http://jasss.soc.surrey.ac.uk/20/4/2.html)
Hegselmann, R. and U. Krause. 2002. “Opinion Dynamics and Bounded Confidence: Models, Analysis and
Simulation.” Journal of Artificial Societies and Social Simulation 5(3). Retrieved
(http://jasss.soc.surrey.ac.uk/5/3/2.html).
Suggested readings:
Centola, D., J. C. González-Avella, V. M. Eguíluz, and M. San Miguel. 2007. “Homophily, Cultural Drift, and the Co-
Evolution of Cultural Groups.” Journal of Conflict Resolution 51(6):905–29. Retrieved
(http://journals.sagepub.com/doi/10.1177/0022002707307632).
Flache, A. (2018) ‘About Renegades and outgroup haters: Modeling the link between social influence and intergroup
attitudes’, Advances in Complex Systems, 21(6&7), p. 1850017. doi: 10.1142/S0219525918500170.
Flache, A. and M. W. Macy. 2011a. “Local Convergence and Global Diversity: From Interpersonal to Social Influence.”
Journal of Conflict Resolution 55(6):970–95. Retrieved October 10, 2014
(http://jcr.sagepub.com/cgi/doi/10.1177/0022002711414371).
Flache, A. and M. W. Macy. 2011. “Small Worlds and Cultural Polarization.” The Journal of Mathematical Sociology
35(1–3):146–76. (http://www.tandfonline.com/doi/abs/10.1080/0022250X.2010.532261).
Mäs, M. and A. Flache. 2013. “Differentiation without Distancing. Explaining Opinion Bi-Polarization without Assuming
Negative Influence .” Plos One 8(11). (https://doi.org/10.1371/journal.pone.0074516)
Tang, T., & Chorus, C. G. (2019). Learning opinions by observing actions: simulation of opinion dynamics using an
action-opinion inference model. Journal of Artificial Societies and Social Simulation, 22(3)
Day 5: Social influence dynamics in online settings
▪ Do social media promote polarization … or not? And if so, under which conditions?
▪ Modelling effects of “globalizing interaction” on polarization and consensus
▪ Effects of the mode of communication: 1:1 vs many:1 interaction (offline vs. online communication)
▪ Modelling the effects of social bots on polarization: the strength of weak bots
▪ Linking models to data: online experiments, digital trace data
▪ Concluding discussion: benefits and challenges of ABM as tool for advancing social science theory building.
▪ Tutorial session: using defSim to model online social influence dynamics.
Literature:
Keijzer, M. A., Mäs, M. and Flache, A. (2018) ‘Communication in Online Social Networks Fosters Cultural Isolation’,
Complexity, 2018, p. 9502872. doi: 10.1155/2018/9502872.
Mäs, M., M. Keijzer. 2021. The complex link between filter bubbles and opinion polarization. Accepted for publication
in Data Science (671-1651).
Suggested readings:
Clemm von Hohenberg, Bernhard, Mäs, Michael & Pradelski, Bary S. R., Micro Influence and Macro Dynamics of
Opinions (May 25, 2017). Available at SSRN: https://ssrn.com/abstract=2974413
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.
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., ... Quattrociocchi, W. (2016). The spreading of
misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554–559. doi:
10.1073/pnas.1517441113
5Flache, A., M. Mäs & M. A. Keijzer. Forthcoming. Computational approaches in rigorous sociology: agent-based
computational modeling and computational social science. To appear in: Gërxhani, de Graaf, N.D. & Raub, W. (eds.).
Handbook of Sociological Science. Contributions to Rigorous Sociology. In: Research Handbooks in Sociology Series
(ed. Blossfeld, H.P.). Edward Elgar Publishing.
Keijzer, M. & M. Mäs. 2021. The strength of weak bots. Online Social Networks and Media.
Takács, K., A. Flache, and M. Mäs. 2016. “Discrepancy and Disliking Do Not Induce Negative Opinion Shifts”. PloS ONE
11(6): e0157948. (http://dx.plos.org/10.1371/journal.pone.0157948).
Additional Recommended Literature:
Modelling social network dynamics with (empirically calibrated) stochastic actor oriented models
Bianchi, F., A. Flache, F. Squazzoni. 2020. Solidarity in collaboration networks when everyone competes for the
strongest partner. A stochastic actor-based simulation model. Journal of Mathematical Sociology 44:4, 249-266,
DOI:10.1080/0022250X.2019.1704284.
Snijders, Tom A. B., and Christian E. G. Steglich. 2015. “Representing Micro–Macro Linkages by Actor-Based Dynamic
Network Models.” Sociological Methods and Research 44(2):222–71. doi: 10.1177/0049124113494573.
Stadtfeld, Christoph. 2018. “The Micro–Macro Link in Social Networks.” Emerging Trends in the Social and Behavioral
Sciences 1–15. doi: 10.1002/9781118900772.etrds0463.
Stadtfeld, Christoph, Károly Takács, and András Vörös. 2020. “The Emergence and Stability of Groups in Social
Networks.” Social Networks 60:129–45. doi: 10.1016/j.socnet.2019.10.008.
See further material on the website of Tom Snijders: https://www.stats.ox.ac.uk/~snijders/siena/.
On the history of segregation models:
Hegselmann, Rainer. 2017. “Thomas C. Schelling and James M. Sakoda: The Intellectual, Technical, and Social History
of a Model.” JASSS 20(3). doi: 10.18564/jasss.3511.
ODD protocols for ABMs (standard for reporting ABM):
Grimm, Volker, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. Deangelis, Bruce
Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S. A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, J. Gary
Polhill, Viktoriia Radchuk, Marie Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele, and Daniel Ayllón. 2020. “The
ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity,
Replication, and Structural Realism.” Journal of Artificial Societies and Social Simulation 23(2). doi:
10.18564/jasss.4259.
Selected textbooks:
Railsback, S. F. and Grimm, V. (2019) Agent-based and individual-based modeling: A practical introduction. 2nd edn.
Princeton, NJ: Princeton University Press.
Squazzoni, F. (2012) Agent-Based Computational Sociology. Chichester: Wiley & Sons. doi: 10.1002/9781119954200.
Wilensky, U. and Rand, W. (2015) An Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered
Complex Systems with NetLogo. Cambridge, MA: MIT Press.
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