Complex Social Systems: Modeling Agents, Learning, and Games

851-0101-86 S Complex Social Systems: Modeling Agents, Learning, and Games

860-0011-00 A Complex Social Systems: Modeling Agents, Learning, and Games - With Coding Project

Autumn Semester 2024

Dozierende: D. N. Dailisan, D. Carpentras, D. Helbing
Place: ML H 44
Time: Mo 16:15-18:00

Course Description:
This course introduces mathematical and computational models to study techno-socioeconomic systems and the process of scientific research. Students develop a significant project to tackle techno-socio-economic challenges in application domains of complex systems. They are expected to implement a model and to communicate their results through a project report and a short oral presentation.

Learning Goals:
By the end of the course, the students should be able to better understand the literature on complex social systems, develop their own models for studying specific phenomena and report results according to the standards of the relevant scientific literature by presenting their results both numerically and graphically.

At the end of the course, the students will deliver a report, computer code and a short oral presentation.

Students are expected to implement themselves models of techno-socio-economic processes and systems, particularly agent-based models, complex networks models, decision making, group dynamics, human crowds, or game-theoretical models.

Credit points are finally earned for the implementation of a mathematical or empirical model from the complexity science literature, its presentation, and documentation by a project report.

Student Projects:
Design and implement agent-based models such as discussed in the lectures and compare their performance in an adaptation, learning, prediction or control task.

Suggested report structure:
1. Introduction: research question clearly stated
2. Related Work: students briefly discuss related work and state-of-the-art approaches
3. Model(s): explanation and relation to the phenomenon under investigation
4. Performance evaluation metrics: chosen by the students and motivated appropriately
5. Summary, discussion, conclusions and outlook (possible future work)

Please approach us in case of any questions.

Learning goal: The students should engage with the methods taught, critically select and implement the underlying models and demonstrate a good overview of the agentbased lecture content, but also familiarize themselves with current literature. The project scope is limited to specific application areas to allow for fair and competent evaluation.