Event Timeslots (1)
Tuesday
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In recent decades, computer simulations have transformed the landscape of contemporary social sciences. From models of city formation to simulations of opinion polarization, these tools allow scientists to explore various social phenomena in dynamic, multi-factorial environments. As simulations become more detailed--adding variables such as gender, race, population size, geographical conditions, and income level--they present different challenges to traditional methods of explanation and causal inference.
This seminar invites students to engage critically with these modern methods. Can complex computer models explain, or merely predict? Do they help us understand social causation, or simply generate plausible scenarios? What kinds of knowledge are simulated? And what philosophical assumptions underlie these methods?
We start with some prominent simulation models (e.g., models of social segregation and forest fire dynamics). Then, we move on to themes in philosophy of science, such as causation, explanation, emergence, and group knowledge. Alongside an accessible textbook on complex social models, students will further engage with readings in philosophy of science.
The course is in English and mainly designed for bachelor students in philosophy, with no prior background in computer simulations or philosophy of science required. It is also open to students from other disciplines, including political science, economics, and other social sciences.
Learning Objectives
Upon successful completion of this seminar, students will be able to:
- Articulate a foundational understanding of complex adaptive systems and agent-based modeling as applied in social science and economics.
- Apply philosophical theories of causation, explanation, representation, and emergence to specific examples of computer simulation.
- Develop advanced skills in philosophical analysis, critical thinking, and present nuanced arguments on the methodology of complex models of social dynamics.