Dirk Helbing: Catalogue data in Autumn Semester 2020

Name Prof. Dr. Dirk Helbing
FieldComputational Social Science
Address
Computational Social Science
ETH Zürich, STD F 3
Stampfenbachstrasse 48
8092 Zürich
SWITZERLAND
Telephone+41 44 632 88 80
Fax+41 44 632 17 67
E-maildirk.helbing@gess.ethz.ch
DepartmentHumanities, Social and Political Sciences
RelationshipFull Professor

NumberTitleECTSHoursLecturers
851-0101-86LComplex Social Systems: Modeling Agents, Learning, and Games Information Restricted registration - show details
Number of participants limited to 100.

Prerequisites: Basic programming skills, elementary probability and statistics.
3 credits2SN. Antulov-Fantulin, D. Helbing
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
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.
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.
851-0252-04LBehavioral Studies Colloquium Information 0 credits2KD. Helbing, U. Brandes, C. Hölscher, M. Kapur, C. Stadtfeld, E. Stern
AbstractThis colloquium is about recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan.
ObjectiveParticipants are informed about recent and ongoing research in the field. Presenting doctoral students obtain feedback on their dissertation research plan.
ContentThe covers the broadly understood field of behavioral science, including theoretical as well as empirical research in Social Psychology and Research on Higher Education, Sociology, Modeling and Simulation in Sociology, Decision Theory and Behavioral Game Theory, Economics, Research on Learning and Instruction, Cognitive Psychology and Cognitive Science.
Prerequisites / NoticeDoctoral students in D-GESS can obtain 2 credits for presenting their dissertation research plan.
851-0467-00LFrom Traffic Modeling to Smart Cities and Digital Democracies Information Restricted registration - show details
Number of participants limited to 30.
3 credits2SD. Helbing, S. Mahajan
AbstractThis seminar will present speakers who discuss the challenges and opportunities arisinig for our cities and societies with the digital revolution. Besides discussing questions of automation using Big Data, AI and other digital technologies, we will reflect on the question of how democracy could be digitally upgraded to promote innovation, sustainability, and resilience.
ObjectiveTo collect credit points, students will have to give a 30-40 minute presentation in the seminar, after which the presentation will be
discussed. The presentation will be graded.
ContentThis seminar will present speakers who discuss the challenges and opportunities arisinig for our cities and societies with the digital revolution. Besides discussing questions of automation using Big Data, AI and other digital technologies, we will also reflect on the question of how democracy could be digitally upgraded, and how citizen participation could contribute to innovation, sustainability, resilience, and quality of life. This includes questions around collective intelligence and digital platforms that support creativity, engagement, coordination and cooperation.
LiteratureMartin Treiber and Arne Kesting
Traffic Flow Dynamics: Data, Models and Simulation
https://www.amazon.com/Traffic-Flow-Dynamics-Models-Simulation-dp-3642324592/dp/3642324592/

Dirk Helbing
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

Dirk Helbing
An Analytical Theory of Traffic Flow (collection of papers)
https://www.researchgate.net/publication/261629187

Michael Batty, Kay Axhausen et al.
Smart cities of the future

Books by Michael Batty
https://link.springer.com/article/10.1140/epjst/e2012-01703-3

How social influence can undermine the wisdom of crowd effect
https://www.pnas.org/content/108/22/9020

Evidence for a collective intelligence factor in the performance of human groups
https://science.sciencemag.org/content/330/6004/686.full

Optimal incentives for collective intelligence
https://www.pnas.org/content/114/20/5077.short

Collective Intelligence: Creating a Prosperous World at Peace
https://www.amazon.com/Collective-Intelligence-Creating-Prosperous-World/dp/097156616X/

Big Mind: How Collective Intelligence Can Change Our World
https://www.amazon.com/Big-Mind-Collective-Intelligence-Change/dp/0691170797/

Programming Collective Intelligence
https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325/

Urban architecture as connective-collective intelligence. Which spaces of interaction?
https://www.mdpi.com/2071-1050/5/7/2928

Build digital democracy
https://www.nature.com/news/society-build-digital-democracy-1.18690

How to make democracy work in the digital age
http://www.huffingtonpost.com/entry/how-to-make-democracy-work-in-the-digital-age_us_57a2f488e4b0456cb7e17e0f

Digital Democracy: How to make it work?
http://futurict.blogspot.com/2020/06/digital-democracy-how-to-make-it-work.html

Proof of witness presence: Blockchain consensus for augmented democracy in smart cities
https://www.sciencedirect.com/science/article/pii/S0743731520303282

Iterative Learning Control for Multi-agent Systems Coordination
https://www.amazon.co.uk/Iterative-Learning-Control-Multi-agent-Coordination-ebook/dp/B06XJVQC41/ref=sr_1_fkmr1_1?dchild=1&keywords=coordination+Jennings+multi-agent&qid=1601973480&sr=8-1-fkmr1

Decentralized Collective Learning for Self-managed Sharing Economies
https://dl.acm.org/doi/abs/10.1145/3277668

Further literature will be recommended in the lectures.
851-0585-41LComputational Social Science Information Restricted registration - show details
Number of participants limited to 30.
3 credits2SD. Helbing, F. Fanitabasi
AbstractThe seminar aims at three-fold integration: (1) bringing modeling and computer simulation of techno-socio-economic processes and phenomena together with related empirical, experimental, and data-driven work, (2) combining perspectives of different scientific disciplines (e.g. sociology, computer science, physics, complexity science, engineering), (3) bridging between fundamental and applied work.
ObjectiveParticipants of the seminar should understand how tightly connected systems lead to networked risks, and why this can imply systems we do not understand and cannot control well, thereby causing systemic risks and extreme events.

They should also be able to explain how systemic instabilities can be understood by changing the perspective from a component-oriented to an interaction- and network-oriented view, and what fundamental implications this has for the proper design and management of complex dynamical systems.

Computational Social Science and Global Systems Science serve to better understand the emerging digital society with its close co-evolution of information and communication technology (ICT) and society. They make current theories of crises and disasters applicable to the solution of global-scale problems, taking a data-based approach that builds on a serious collaboration between the natural, engineering, and social sciences, i.e. an interdisciplinary integration of knowledge.
LiteratureComputational Social Science
https://science.sciencemag.org/content/sci/323/5915/721.full.pdf

Manifesto of Computational Social Science
https://link.springer.com/article/10.1140/epjst/e2012-01697-8

Social Self-Organisation
https://www.springer.com/gp/book/9783642240034

How simple rules determine pedestrian behaviour and crowd disasters
https://www.pnas.org/content/108/17/6884.short

Peer review and competition in the Art Exhibition Game
https://www.pnas.org/content/113/30/8414.short

Generalized network dismantling
https://www.pnas.org/content/116/14/6554.short

Computational Social Science: Obstacles and Opportunities
https://science.sciencemag.org/content/369/6507/1060?rss%253D1=

Bit by Bit: Social Research in the Digital Age
https://www.amazon.co.uk/Bit-Social-Research-Digital-Age-ebook/dp/B072MPFXX2/

Further literature will be recommended in the lectures.
860-0011-00LComplex Social Systems: Modeling Agents, Learning, and Games - With Coding Projec Information Restricted registration - show details
Only for Science, Technology, and Policy MSc.

Prerequisites: Good mathematical skills, basic programming skills, elementary probability and statistics.
6 credits2S + 2AN. Antulov-Fantulin, D. Helbing
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 notesAgent-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.
LiteratureLiterature, in particular regarding computer models in the (computational) social sciences, will be provided in the course.
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.