Dirk Helbing: Catalogue data in Autumn Semester 2023 |
| 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 |
| dirk.helbing@gess.ethz.ch | |
| Department | Humanities, Social and Political Sciences |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0101-86L | Complex Social Systems: Modeling Agents, Learning, and Games   Prerequisites: Basic programming skills, elementary probability and statistics. | 3 credits | 2S | D. N. Dailisan, D. Carpentras, D. Helbing | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces mathematical and computational models to study techno-socioeconomic 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 to communicate their results through a project report and a short oral presentation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | See your own field of study in a wider context (“Science in Perspective”), e.g. see the psychological, social, economic, environmental, historical, ethical,or philosophical connections and implications. Learn to think critically and out of the box. Question what you believe you know for sure. Get to know surprising, counterintuitive properties of complex (non-linearly interacting, networked, multi-component) systems. Learn about collaboration. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | By the end of the course, the students should be able to better understand the literature on complex social systems, develop their own models for studying specific phenomena and report results according to the standards of the relevant scientific literature by presenting their results both numerically and graphically. At the end of the course, the students will deliver a report, computer code and a short oral presentation. To collect credit points, students will have to actively contribute and give a circa 30 minutes presentation in the course on a subject agreed with the lecturers, after which the presentation will be discussed. The presentation will be graded. Students are expected to implement themselves models of techno-socio-economic processes and systems, particularly agent-based models, complex networks models, decision making, group dynamics, human crowds, or game-theoretical models. Credit points are finally earned for the implementation of a mathematical or empirical model from the complexity science literature, its presentation, and documentation by a project report. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The lecture slides will be presented on the course Moodle 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. Students need to present a new subject, for which they have not earned any credit points before. Good scientific practices, in particular citation and quotation rules, must be properly complied with. Chatham House rules apply to this course. Materials may not be shared without previous written permission. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0252-04L | Behavioral Studies Colloquium | 1 credit | 2K | E. Cross, U. Brandes, D. Helbing, C. Hölscher, M. Kapur, C. Stadtfeld, E. Stern, V. Zimmermann | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This colloquium offers an opportunity to discuss recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. The colloquium 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 different branches of the behavioral sciences. Presenting doctoral students obtain feedback on their dissertation research plan. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This colloquium offers an opportunity to discuss 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 covers a broad range of areas, including theoretical as well as empirical research in social psychology, 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. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | https://sbs.ethz.ch/education.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 851-0467-00L | From Traffic Modeling to Smart Cities and Digital Democracies | 3 credits | 2S | D. Helbing, R. K. Dubey | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar will present speakers who discuss the challenges and opportunities arising for our cities and societies with the digital revolution. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | To collect credit points, students will have to actively contribute and give an individual presentation for around 30 minutes in the seminar on a subject agreed with the lecturer, after which the presentation will be discussed (could be 20 or 40 min, depending on available time). The presentation will be graded. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This seminar will present speakers who discuss the challenges and opportunities arising 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 | Dirk Helbing An Analytical Theory of Traffic Flow (collection of papers) Michael Batty, Kay Axhausen et al. Smart cities of the future Books by Michael Batty: How social influence can undermine the wisdom of crowd effect Evidence for a collective intelligence factor in the performance of human groups Optimal incentives for collective intelligence Collective Intelligence: Creating a Prosperous World at Peace Big Mind: How Collective Intelligence Can Change Our World Programming Collective Intelligence Urban architecture as connective-collective intelligence. Which spaces of interaction? Build digital democracy How to make democracy work in the digital age Digital Democracy: How to make it work? Proof of witness presence: Blockchain consensus for augmented democracy in smart cities Iterative Learning Control for Multi-agent Systems Coordination Decentralized Collective Learning for Self-managed Sharing Economies | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students need to present a new subject, for which they have not earned any credit points before. Good scientific practices, in particular citation and quotation rules, must be properly complied with. Chatham House rules apply to this course. Materials may not be shared without previous written permission. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 851-0585-41L | Computational Social Science | 3 credits | 2S | D. Helbing, E. Stockinger, J. C.‑Y. Yang | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 | Ball: Why Society Is A Complex Matter • Helbing: Social Self-Organization • Helbing: Managing Complexity • Colander/Kupers: Complexity and the Art of Public Policy • Mitchell: Complexity • Buckley: Society – A Complex Adaptive System • Castellani/Hafferty: Sociology and Complexity Science • Mikhailov/Calenbuhr: From Cells to Society • Mainzer: Thinking in Complexity • Sawyer: Social Emergence • Books published by the Santa Fe Institute 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Students need to present a new subject, for which they have not earned any credit points before. Good scientific practices, in particular citation and quotation rules, must be properly complied with. Chatham House rules apply to this course. Materials may not be shared without previous written permission. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 860-0011-00L | Complex Social Systems: Modeling Agents, Learning, and Games - With Coding Project Prerequisites: Good mathematical skills, basic programming skills, elementary probability and statistics. | 6 credits | 2S + 2A | D. N. Dailisan, D. Helbing, D. Carpentras | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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. 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. Students need to present a new subject, for which they have not earned any credit points before. Good scientific practices, in particular citation and quotation rules, must be properly complied with. Chatham House rules apply to this course. Materials may not be shared without previous written permission. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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