After successful completion of the course, students are able to develope computational models for social processes such as diffuction of information, formation of norms, and social dynamics. They will also be familiarized with Data ethics.
- Basic concepts and origin of Computational Social Science as a discipline.
- Sociological and computational approaches to the analysis of social networks.
- Game theory.
- Epidemics on networks.
- Spreading and adoption of social norms and culture.
- Opinion dynamics and polarization.
- Computational inequality.
- Algorithms and society.
- Agent-based modeling.
- Opinion Dynamics.
The course will consist of lectures and exercises. The students will receive an exercise sheet 1-2 weeks before each exercise and are expected to submit their solutions in advance. The final examination will consist of final group projects and written reports.
The lecture slides will be available on the Web.
Workload for Students (in hours):
Total: 75h
Lecture modality
Although the course is hybrid, most lectures will be held in-person without any online retransmission or recording.
Some lectures may be taught remotely, in which case students will be notified in advance.
- Bi-weekly home assignments: 40 %
- Final project I: 25%
- Final project II: 25%
- Lecture attendance and interaction at the class: 10%
Basic programming knowledge in python is desirable.
