Introduction to applied Social Simulation with ABM 3500-FAKANG-SSA-SCC
Social sciences often research one-shot events or processes. We
can’t repeat the exact same election, try out an alternative digital
transformation, nor check how different legislation would affect the
well-being of employees during the same period.
One of possible ways of addressing this problem is social simulation
and agent-based modeling. An agent-based model is a
computational representation of some aspect of social reality.
Inside it we place agents representing real-life social actors. Here
we use social mechanisms, which explain social phenomena as a
result of inner workings of ‘social cogs and wheels’. Behavior of
agents is determined by social mechanisms identified in real-world
studies as sensible explanations of operations of these actors. This
allows us to use ABM to portray and experiment with variants of
real events and processes. We perform in-depth studies of aspects
of social reality that are empirically inaccessible, ponder how
probable (or improbable) the current state of the world is, or try to
form predictions to better guide decision-making.
This course teaches two types of skills: an in-depth understanding
of social simulation toolkit and an ability to implement social
research problems into an agent-based model.
Understanding comes from a theoretical and methodological sense.
It is necessary to differentiate between theories, data and methods
that can and cannot be fed into an ABM. This will be taught with real
case-studies of ABM implementations.
Second part develops a technical skillset. We will devote a special
block of the course to this end. After completion students will be
able to code a simple ABM model in NetLogo. Other
implementations (e.g. in Python) might also be included.
Last aspect of the course is devoted to critical thinking. Students will
be fluent in reading model documentation and understanding
model design. They will be able to assess the usefulness of different
models in solving real-world problems. We will focus on several
implementations into matters of public, economic and
organizational decision-making. Specific topics will be aligned with
group’s interest.
Rodzaj przedmiotu
Tryb prowadzenia
Koordynatorzy przedmiotu
Efekty kształcenia
K_W05 Has in-depth knowledge about the types of social ties and
mechanisms supporting collective governance
K_W07 Has in-depth knowledge of selected methods and techniques of
social research, their limitations, specificity and areas of application
K_W10 Has in-depth knowledge about major international and domestic
sociological research pertaining to selected areas of social reality or subdomains of sociology
K_U03 Can independently form and verify judgments about the causes
of selected social phenomena
K_U05 Can plan and carry out a social study using advanced quantitative
and qualitative methods and techniques of social research
K_U06 Can use a selected computer program for data analysis, including
its advanced functions
K_U07 Can form an in-depth evaluation of the measures undertaken for
the purpose of solving a social problem, based on knowledge and analytical
skills acquired
K_U10 Can prepare a presentation of a selected problem or study in
Polish and in a foreign language
K_K04 Can argue a thesis using scientific evidence
Kryteria oceniania
1. Presentation of report with a codebase (80%) due by last
meeting judged on the basis of: sensibility of model
conceptualization (30%), code quality (20%), quality of
argumentation and presentation (30%). Specific topics of
presentation and the amount of code input will be agreed
individually.
2. Re-sit: oral exam consisting of 5 questions (16% each = 80%).
3. Participation in discussions (20%).
4. Total: (1/2 + 3) = 100%.
5. Two permissible absences.
Grades:
51% - 60% - 3
61% - 70% - 3.5
71% - 80% - 4
81% - 90% - 4.5
91% - 100% - 5
Literatura
Reading examples:
1. Railsback, S.F. & Grimm, V. (2019). Agent-Based and Individual-Based Modeling. A Practical Introduction. Princeton University Press.
2. Lindenberg, S. (2013). Social rationality, self-regulation, and well-being: The regulatory significance of needs, goals, and the self. In Wittek, R., Snijders, T. A., & Nee, V. (Eds.) The handbook of rational choice social research (pp. 72-112).
3. Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., ... & Gilbert, N. (2020). Computational models that matter during a global pandemic outbreak: A call to action. JASSS-The Journal of Artificial Societies and Social Simulation, 23(2), 10.
4. Ligmann-Zielinska, A., Siebers, P. O., Magliocca, N., Parker, D. C., Grimm, V., Du, J., ... & Ye, X. (2020). ‘One size does not fit all’: A roadmap of purpose-driven mixed-method pathways for sensitivity analysis of agent-based models. Journal of Artificial Societies and Social Simulation, 23(1).
5. Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAngelis, D. L., ... & Ayllón, D. (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).
6. Jager, W. (2017). Enhancing the realism of simulation (EROS): On implementing and developing psychological theory in social simulation. Journal of Artificial Societies and Social Simulation, 20(3).
7. Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of social influence. Towards the next frontiers. Journal of Artificial Societies and Social Simulation, 20(4).
8. Hegselmann, R. (2017). Thomas C. Schelling and James M. Sakoda: The intellectual, technical, and social history of a model. Journal of Artificial Societies and Social Simulation, 20(3).
9. Moss, S., & Edmonds, B. (2005). Sociology and simulation: Statistical and qualitative cross-validation. American journal of sociology, 110(4), 1095-1131.
10. Dignum, F., Dignum, V., Davidsson, P., Ghorbani, A., van der Hurk, M., Jensen, M., ... & Verhagen, H. (2020). Analysing the combined health, social and economic impacts of the corovanvirus pandemic using agent-based social simulation. Minds and Machines, 30, 177-194.
11. Stevenson, M., Thompson, J., de Sá, T. H., Ewing, R., Mohan, D., McClure, R., ... & Woodcock, J. (2016). Land use, transport, and population health: estimating the health benefits of compact cities. The lancet, 388(10062), 2925-2935.
12. Stadtfeld, C., Takács, K., & Vörös, A. (2020). The emergence and stability of groups in social networks. Social Networks, 60, 129-145.
13. Mehryar, S., Sliuzas, R., Schwarz, N., Sharifi, A., & Van Maarseveen, M. (2019). From individual Fuzzy Cognitive Maps to Agent Based Models: Modeling multi-factorial and multi-stakeholder decision-making for water scarcity. Journal of environmental management, 250, 109482.
14. Model libraries (e.g. NetLogo catalogue, COMSES, OSF, GitHub, other relevant sources).
Więcej informacji
Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb: