Nino Antulov-Fantulin: Katalogdaten im Herbstsemester 2020

NameHerr Dr. Nino Antulov-Fantulin
LehrgebietComputer-gestüzte Sozialwissenschaften
Adresse
Computational Social Science
ETH Zürich, STD F 4
Stampfenbachstrasse 48
8092 Zürich
SWITZERLAND
Telefon+41 44 632 61 57
E-Mailnino.antulov@gess.ethz.ch
DepartementGeistes-, Sozial- und Staatswissenschaften
BeziehungPrivatdozent

NummerTitelECTSUmfangDozierende
851-0101-86LComplex Social Systems: Modeling Agents, Learning, and Games Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 100.

Prerequisites: Basic programming skills, elementary probability and statistics.
3 KP2SN. Antulov-Fantulin, D. Helbing
KurzbeschreibungThis 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.
LernzielThe 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.
InhaltStudents 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.
SkriptThe lecture slides will be presented on the course web page after each lecture.
LiteraturAgent-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

Further literature will be recommended in the lectures.
Voraussetzungen / BesonderesThe 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.
860-0011-00LComplex Social Systems: Modeling Agents, Learning, and Games - With Coding Projec Information Belegung eingeschränkt - Details anzeigen
Only for Science, Technology, and Policy MSc.

Prerequisites: Good mathematical skills, basic programming skills, elementary probability and statistics.
6 KP2S + 2AN. Antulov-Fantulin, D. Helbing
KurzbeschreibungThis 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.
LernzielThe 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.
InhaltStudents 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.
SkriptAgent-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

Further literature will be recommended in the lectures.
LiteraturLiterature, in particular regarding computer models in the (computational) social sciences, will be provided in the course.
Voraussetzungen / BesonderesThe 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.