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851-0101-86L 3 Credits DS , DR , MSC D-GESS
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Complex Social Systems: Modeling Agents, Learning, and Games

Lecturers & Examiners: Prof. Dr. Dirk Helbing, Dr. Dino Carpentras, Dr. Damian Dailisan
Prerequisites: Basic programming skills, elementary probability and statistics.
VVZ CR 2.8

Last Updated: 2026-06-01 11:30:55

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

Learning Materials (Links)

General Information

Language
English
Levels
DS , DR , MSC
Frequency
Yearly recurring

Examination

Type
graded semester performance

Registration & Places

Limited places (Special selection)

Course Components

Type Title Time & Place Hours
seminar Complex Social Systems: Modeling Agents, Learning, and Games
  • Mon 16:15-18:00 (ML E 12)
2 h weekly

Offered In