Introduction to applied Social Simulation with ABM 3500-FAKANG-SSABM
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
Założenia (opisowo)
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 sub-domains 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. Research project (80%) due by June 15th 2025 judged on the basis of: sensibility of implementation (30%), code quality (20%), quality of argumentation and interpretation (30%).
2. Re-sit: written exam consisting of 5 questions (18% each = 80%).
3. Participation in discussions (10%).
4. Two permissible absences (10%).
5. Total: (1/2 + 3 + 4) = 100%.
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: