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

SemesterAutumn Semester 2020
LecturersN. Antulov-Fantulin, D. Helbing
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 100.

Prerequisites: Basic programming skills, elementary probability and statistics.


851-0101-86 SComplex Social Systems: Modeling Agents, Learning, and Games Special students and auditors need a special permission from the lecturers.2 hrs
Mon16:15-18:00HG E 7 »
N. Antulov-Fantulin, D. Helbing

Catalogue data

AbstractThis course introduces mathematical and computational models to study techno-socio-economic 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 communicating their results through a seminar thesis and a short oral presentation.
ObjectiveThe students are expected to know a programming language and environment (Python, Java or Matlab) as a tool to solve various scientific problems. The use of a high-level programming environment makes it possible to quickly find numerical solutions to a wide range of scientific problems. Students will learn to take advantage of a rich set of tools to present their results numerically and graphically.

The students should be able to implement simulation models and document their skills through a seminar thesis and finally give a short oral presentation.
ContentStudents are expected to implement themselves models of various social processes and systems, including agent-based models, complex networks models, decision making, group dynamics, human crowds, or game-theoretical models.

Part of this course will consist of supervised programming exercises. Credit points are finally earned for the implementation of a mathematical or empirical model from the complexity science literature and the documentation in a seminar thesis.
Lecture notesThe lecture slides will be presented on the course web page after each lecture.
LiteratureAgent-Based Modeling

Social Self-Organization

Traffic and related self-driven many-particle systems
Reviews of Modern Physics 73, 1067

An Analytical Theory of Traffic Flow (collection of papers)

Pedestrian, Crowd, and Evacuation Dynamics

The hidden geometry of complex, network-driven contagion phenomena (relevant for modeling pandemic spread)

Further literature will be recommended in the lectures.
Prerequisites / NoticeThe number of participants is limited to the size of the available computer teaching room. The source code related to the seminar thesis should be well enough documented.

Good programming skills and a good understanding of probability & statistics and calculus are expected.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersN. Antulov-Fantulin, D. Helbing
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

Main linkhttp://www.vvz.ethz.ch/Vorlesungsverzeichnis/lerneinheit.view?semkez=2020W&ansicht=KATALOGDATEN&lern
Only public learning materials are listed.


No information on groups available.


General : Special students and auditors need a special permission from the lecturers
Places100 at the most
Waiting listuntil 19.10.2020

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