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Complex Social Systems: Modeling Agents, Learning, and Games
Last Updated: 2026-06-03 00:07:38
Abstract
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.
Objective
See your own field of study in a wider context (“Science in Perspective”), e.g. see the psychological, social, economic, environmental, historical, ethical,or philosophical connections and implications. Learn to think critically and out of the box. Question what you believe you know for sure. Get to know surprising, counterintuitive properties of complex (non-linearly interacting, networked, multi-component) systems. Learn about collaboration.
Content
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. To collect credit points, students will have to actively contribute and give a circa 30 minutes presentation in the course on a subject agreed with the lecturers, after which the presentation will be discussed. The presentation will be graded. 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.
Resources
Lecture Notes
The lecture slides will be presented on the course Moodle after each lecture.
Literature
Agent-Based Modeling https://link.springer.com/chapter/10.1007/978-3-642-24004-1_2 Social Self-Organization https://www.springer.com/gp/book/9783642240034 Traffic and related self-driven many-particle systems Reviews of Modern Physics 73, 1067 https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.73.1067 An Analytical Theory of Traffic Flow (collection of papers) https://www.researchgate.net/publication/261629187 Pedestrian, Crowd, and Evacuation Dynamics https://www.research-collection.ethz.ch/handle/20.500.11850/45424 The hidden geometry of complex, network-driven contagion phenomena (relevant for modeling pandemic spread) https://science.sciencemag.org/content/342/6164/1337
General Information
- Language
- English
- Levels
- DS , DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar | Complex Social Systems: Modeling Agents, Learning, and Games | No time listed | 2 h weekly |
Offered In
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Science in Perspective (In “Science in Perspective”-courses students learn to reflect on ETH’s STEM subjects from the perspective of humanities, political and social sciences. Only the courses listed below will be recognized as "Science in Perspective" courses.)
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Type A: Enhancement of Reflection Competence (SiP courses are recommended for bachelor students after their first-year examination and for all master- or doctoral students. All SiP courses are listed in Type A. Courses listed under Type B are only recommendations for enrollment for specific departments.)
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Type B: Reflection About Subject-Specific Methods and Contents (Subject-specific courses. Particularly relevant for students interested in those subjects. All these courses are also listed under the category “Typ A”, and every student can enroll in these courses.)
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Doctorate Humanities, Social and Political Sciences (More Information at: )
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