Dirk Helbing: Catalogue data in Spring Semester 2021

Name Prof. Dr. Dirk Helbing
FieldComputational Social Science
Computational Social Science
ETH Zürich, STD F 3
Stampfenbachstrasse 48
8092 Zürich
Telephone+41 44 632 88 80
Fax+41 44 632 17 67
DepartmentHumanities, Social and Political Sciences
RelationshipFull Professor

851-0252-04LBehavioral Studies Colloquium Information 0 credits2KD. Helbing, U. Brandes, C. Hölscher, M. Kapur, C. Stadtfeld, E. Stern
AbstractThis colloquium offers an opportunity for students to discuss their ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It also offers an opportunity for students from other disciplines to discuss their research ideas in relation to behavioral science. The colloquium also features invited research talks.
ObjectiveStudents know and can apply autonomously up-to-date investigation methods and techniques in the behavioral sciences. They achieve the ability to develop their own ideas in the field and to communicate their ideas in oral presentations and in written papers. The credits will be obtained by a written report of approximately 10 pages.
ContentThis colloquium offers an opportunity for students to discuss their ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It also offers an opportunity for students from other disciplines to discuss their ideas in so far as they have some relation to behavioral science. The possible research areas are wide and may include theoretical as well as empirical approaches 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. Ideally the students (from Bachelor, Master, Ph.D. and Post-Doc programs) have started to start work on their thesis or on any other term paper.
Course credit can be obtained either based on a talk in the colloquium plus a written essay, or by writing an essay about a topic related to one of the other talks in the course. Students interested in giving a talk should contact the course organizers (Ziegler, Kapur) before the first session of the semester. Priority will be given to advanced / doctoral students for oral presentations. The course credits will be obtained by a written report of approximately 10 pages. The colloquium also serves as a venue for invited talks by researchers from other universities and institutions related to behavioral and social sciences.
LiteratureWill be provided on request.
Prerequisites / NoticeDoctoral students in D-GESS can obtain 2 credit points for presenting their dissertation research plan.
851-0585-38LData Science in Techno-Socio-Economic Systems Restricted registration - show details
Number of participants limited to 80

This course is thought be for students in the 5th semester or above with quantitative skills and interests in modeling and computer simulations.

Particularly suitable for students of D-INFK, D-ITET, D-MAVT, D-MTEC, D-PHYS
3 credits2VD. Helbing, N. Antulov-Fantulin, V. Vasiliauskaite
AbstractThis course introduces how techno-socio-economic systems in our complex society can be better understood with techniques and tools of data science. Students shall learn how the fundamentals of data science are used to give insights into the research of complexity science, computational social science, economics, finance, and others.
ObjectiveThe goal of this course is to qualify students with knowledge on data science to better understand techno-socio-economic systems in our complex societies. This course aims to make students capable of applying the most appropriate and effective techniques of data science under different application scenarios. The course aims to engage students in exciting state-of-the-art scientific tools, methods and techniques of data science.
In particular, lectures will be divided into research talks and tutorials. The course shall increase the awareness level of students of the importance of interdisciplinary research. Finally, students have the opportunity to develop their own data science skills based on a data challenge task, they have to solve, deliver and present at the end of the course.
ContentWill be provided on a separate course webpage.
Lecture notesSlides will be provided.
LiteratureGrus, Joel. "Data Science from Scratch: First Principles with Python". O'Reilly Media, 2019.

"A high-bias, low-variance introduction to machine learning for physicists"

Applications to Techno-Socio-Economic Systems:

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

"A network framework of cultural history"

"Science of science"

"Generalized network dismantling"

Further literature will be recommended in the lectures.
Prerequisites / NoticeGood programming skills and a good understanding of probability & statistics and calculus are expected.
851-0585-48LControversies in Game Theory Restricted registration - show details
Number of participants limited to 100.
3 credits2VD. Helbing, H. Nax, H. Rauhut
AbstractThe mini-course 'Controversies in Game Theory' consists of 5 course units that provide an in-depth introduction to issues in game theory motivated by real-world issues related to the tensions that may result from interactions in groups, where individual and collective interests may conflict. The course integrates theory from various disciplines.
ObjectiveStudents are encouraged to think about human interactions, and in particular in the context of game theory, in a way that is traditionally not covered in introductory game theory courses. The aim of the course is to teach students the complex conditional interdependencies in group interactions.
ContentThe course will pay special attention to the dichotomy of cooperative vs non-cooperative game theory through the lense of the pioneering work by John von Neumann (who—which is not very well known--was an undergraduate student at ETH Zurich). We will review the main solution concepts from both fields, work with applications relying on those, and look at the “Nash program” which is a famous attempt to bridge the two.
Lecture notesSlides will be provided.
LiteratureJohn v Neumann and Oskar Morgenstern. 1944. Theory of Games and Economic Behavior. (https://en.wikipedia.org/wiki/Theory_of_Games_and_Economic_Behavior)

Diekmann, Andreas: Spieltheorie. Rowohlt 2009.

Dixit, Avinash K., and Susan Skeath. Games of Strategy. WW Norton & Company, 2015.

Ken Binmore (1992): Fun and Games. Lexington: Heath.

Camerer, Colin (2003): Behavioral Game Theory. Experiments in Strategic Interaction. Princeton: Princeton University Press.

Game Theory Evolving

Evolutionary Game Theory

Evolutionary Game Theory in Natural, Social and Virtual Worlds

Evolutionary Dynamics and Extensive Form Games

Evolutionary Games and Population Dynamics

Quantitative Sociodynamics

Synergistic Selection: How Cooperation Has Shaped Evolution and the Rise of Humankind

Survival of the Nicest

Evolutionary Games with Sociophysics

Statistical Physics and Computational Methods for Computational Game Theory

Games of life

Further literature will be recommended in the lectures.
Prerequisites / NoticeThis course is thought be for students in the 5th semester or above with quantitative skills and interests in modeling and computer simulation.
860-0022-00LComplexity and Global Systems Science Restricted registration - show details
Number of participants limited to 50.

Prerequisites: solid mathematical skills.

Particularly suitable for students of D-ITET, D-MAVT and ISTP
3 credits2SD. Helbing, S. Mahajan
AbstractThis course discusses complex techno-socio-economic systems, their counter-intuitive behaviors, and how their theoretical understanding empowers us to solve some long-standing problems that are currently bothering the world.
ObjectiveParticipants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop models for open problems, to analyze them, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to think scientifically about complex dynamical systems.
ContentThis course starts with a discussion of the typical and often counter-intuitive features of complex dynamical systems such as self-organization, emergence, (sudden) phase transitions at "tipping points", multi-stability, systemic instability, deterministic chaos, and turbulence. It then discusses phenomena in networked systems such as feedback, side and cascading effects, and the problem of radical uncertainty. The course progresses by demonstrating the relevance of these properties for understanding societal and, at times, global-scale problems such as traffic jams, crowd disasters, breakdowns of cooperation, crime, conflict, social unrests, political revolutions, bubbles and crashes in financial markets, epidemic spreading, and/or "tragedies of the commons" such as environmental exploitation, overfishing, or climate change. Based on this understanding, the course points to possible ways of mitigating techno-socio-economic-environmental problems, and what data science may contribute to their solution.
Lecture notes"Social Self-Organization
Agent-Based Simulations and Experiments to Study Emergent Social Behavior"
Helbing, Dirk
ISBN 978-3-642-24004-1
LiteraturePhilip Ball
Why Society Is A Complex Matter

Globally networked risks and how to respond
Nature: https://www.nature.com/articles/nature12047

Global Systems Science and Policy

Managing Complexity: Insights, Concepts, Applications

Further links:





Further literature will be recommended in the lectures.
Prerequisites / NoticeMathematical skills can be helpful
860-0024-00LDigital Society: Ethical, Societal and Economic Challenges Restricted registration - show details
Number of participants is limited to 30.
3 credits2VD. Helbing, C. I. Hausladen
AbstractThis seminar will address ethical challenges coming along with new digital technologies such as cloud computing, Big Data, artificial
intelligence, cognitive computing, quantum computing, robots, drones, Internet of Things, virtual reality, blockchain technology, and more...
ObjectiveParticipants shall learn to understand that any technology implies not only opportunities, but also risks. It is important to understand these well in order to minimize the risks and maximize the benefits. In some cases, it is highly non-trivial to identify and avoid undesired side effects of technologies. The seminar will sharpen the attention how to design technologies for values,
also called value-sensitive design or ethically aligned design.
ContentWill be provided on a complementary website of the course.
Lecture notesWill be provided on a complementary website of the course.
LiteratureEthically Aligned Design
Version 1: https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ead_v1.pdf
Version 2: https://standards.ieee.org/content/dam/ieee-standards/standards/web/documents/other/ead_v2.pdf

Value-Sensitive Design

Handbook of Ethics, Values and Technological Design

Thinking Ahead

Towards Digital Enlightenment

Künstliche Intelligenz und Maschinisierung des Menschen

Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy (J Taplin)

How Humans Judge Machines

Further literature will be recommended in the lectures.
Prerequisites / NoticeTo earn credit points, students will have to read the relevant literature on one of the above technologies and give a
presentation about the ethical implications. Both, potential problems and possible solutions shall be carefully discussed.