Dirk Helbing: Catalogue data in Autumn Semester 2020

Name	Prof. Dr. Dirk Helbing
Field	Computational 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-mail	dirk.helbing@gess.ethz.ch
Department	Humanities, Social and Political Sciences
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
851-0101-86L	Complex Social Systems: Modeling Agents, Learning, and Games Number of participants limited to 100. Prerequisites: Basic programming skills, elementary probability and statistics.	3 credits	2S	N. Antulov-Fantulin, D. Helbing
Abstract	This 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.
Learning objective	The 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.
Content	Students 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 notes	The lecture slides will be presented on the course web page 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 Further literature will be recommended in the lectures.
Prerequisites / Notice	The 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.
851-0252-04L	Behavioral Studies Colloquium	0 credits	2K	D. Helbing, U. Brandes, C. Hölscher, M. Kapur, C. Stadtfeld, E. Stern
Abstract	This 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.
Learning objective	Participants are informed about recent and ongoing research in the field. Presenting doctoral students obtain feedback on their dissertation research plan.
Content	The 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 / Notice	Doctoral students in D-GESS can obtain 2 credits for presenting their dissertation research plan.
851-0467-00L	From Traffic Modeling to Smart Cities and Digital Democracies Number of participants limited to 30.	3 credits	2S	D. Helbing, S. Mahajan
Abstract	This 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.
Learning objective	To 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.
Content	This 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.
Literature	Martin 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.
851-0585-41L	Computational Social Science Number of participants limited to 30.	3 credits	2S	D. Helbing, F. Fanitabasi
Abstract	The 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.
Learning objective	Participants 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.
Literature	Computational 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.
860-0011-00L	Complex Social Systems: Modeling Agents, Learning, and Games - With Coding Projec Only for Science, Technology, and Policy MSc. Prerequisites: Good mathematical skills, basic programming skills, elementary probability and statistics.	6 credits	2S + 2A	N. Antulov-Fantulin, D. Helbing
Abstract	This 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.
Learning objective	The 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.
Content	Students 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 notes	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 Further literature will be recommended in the lectures.
Literature	Literature, in particular regarding computer models in the (computational) social sciences, will be provided in the course.
Prerequisites / Notice	The 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.