Dirk Helbing: Katalogdaten im Herbstsemester 2018

NameHerr Prof. Dr. Dirk Helbing
LehrgebietComputational Social Science
Adresse
Computational Social Science
ETH Zürich, STD F 3
Stampfenbachstrasse 48
8092 Zürich
SWITZERLAND
Telefon+41 44 632 88 80
Fax+41 44 632 17 67
E-Maildirk.helbing@gess.ethz.ch
DepartementGeistes-, Sozial- und Staatswissenschaften
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
851-0252-04LBehavioral Studies Colloquium2 KP2KU. Brandes, V. Amati, H.‑D. Daniel, D. Helbing, C. Hölscher, M. Kapur, R. Schubert, C. Stadtfeld, E. Stern
KurzbeschreibungThis 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.
LernzielStudents 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.
InhaltThis 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.
851-0585-04LLecture with Computer Exercises: Modeling and Simulating Social Systems in MATLAB (or Python)
Particularly suitable for students of D-MAVT, D-INFK, D-ITET, D-MTEC, D-PHYS.
3 KP2SD. Helbing, L. Aguilar Melgar, N. Antulov-Fantulin
KurzbeschreibungThis course introduces mathematical and computational models to study social systems and the process of scientific research.

Students develop a significant project, implementing a model and communicating their results through a seminar thesis and a short oral presentation.
LernzielThe students should learn how to use a high level programming environment (MATLAB or Python) 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 learnt to take advantage of a rich set of tools to present their results numerically and graphically.

After the students have learned the basic structure of the programming language, they should be able to implement social simulation models and document their skills through a seminar thesis and finally give a short oral presentation.
InhaltThis course introduces first the basic functionalities and features of the high level programming environments (MATLAB and Python), such as the simple operations with matrices and vectors, differential equations, statistical tools, the graphical representation of data in various forms, and video animations of spatio-temporal data. With this knowledge, students are expected to implement themselves models of various social processes and systems, including agent-based models, e.g. models of interactive 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 model from the sociological literature and the documentation in a seminar thesis.
SkriptThe lecture slides will be presented on the course web page after each lecture.
LiteraturLiterature, in particular regarding computer models in the 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 for further use by others and must be handed over to the Chair of Computational Social Science (COSS) for further free and unrestricted use.
851-0585-41LComputational Social Science Belegung eingeschränkt - Details anzeigen
Number of participants limited to 50
3 KP2SD. Helbing, T. Guo
KurzbeschreibungThe 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.
LernzielParticipants 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.
851-0585-48LControversies in Game Theory: Collectives and Individuals
Number of participants limited to 80
3 KP2VD. Helbing, H. Nax, H. Rauhut
KurzbeschreibungThe 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.
LernzielStudents 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.
Voraussetzungen / BesonderesThis course is thought be for students in the 5th semester or above with quantitative skills and interests in modeling and computer simulations.
851-0585-49LSelf-Organizing Multi-Agent Systems
Number of participants limited to 100.
3 KP2VD. Helbing, E. Pournaras
KurzbeschreibungThis course introduces modeling and simulation techniques for multi-agent systems in the era of Internet of Things. Topics such as collective intelligence and decentralized combinatorial optimization are covered. Students will prototype autonomous self-organizing agents to tackle techno-socio-economic challenges in application domains of smart cities and beyond.
LernzielThe learning objectives of this course is to teach how to model, design and build self-organizing (multi-)agent systems in distributed techno-socio-economic systems such as smart grids, smart cities, pedestrian flows, traffic systems, and others. Students will be especially prepared to apply such systems in the era of Internet of Things, Big Data and distributed sharing economies. For this reason, students will experiment will real-world data as well as simulation and prototyping software with which they will examine and measure emergent phenomena such as traffic jams or power cascading failures. Τhe course stretches from simple, reactive agents to more sophisticated, decision-making or cognitive agents. The ultimate goal is to construct mechanims based on state of the art distributed optimization and machine learning techniques to improve collective and system-wide objectives related to reliability, resilience, sustainability, fairness and others.
860-0011-00LModeling and Simulating Social Systems in MATLAB (or Python) - With Coding Project Belegung eingeschränkt - Details anzeigen
Only for Science, Technology, and Policy MSc and MAS.
6 KP2S + 2AN. Antulov-Fantulin, D. Helbing, L. Aguilar Melgar
KurzbeschreibungThis course introduces mathematical and computational models to study social systems and the process of scientific research.

Students develop a significant project, implementing a model and communicating their results through a seminar thesis and a short oral presentation.
LernzielThe students should learn how to use a high level programming environment (MATLAB or Python) 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 learnt to take advantage of a rich set of tools to present their results numerically and graphically.

After the students have learned the basic structure of the programming language, they should be able to implement social simulation models and document their skills through a seminar thesis and finally give a short oral presentation.
InhaltThis course introduces first the basic functionalities and features of the high level programming environments (MATLAB and Python), such as the simple operations with matrices and vectors, differential equations, statistical tools, the graphical representation of data in various forms, and video animations of spatio-temporal data. With this knowledge, students are expected to implement themselves models of various social processes and systems, including agent-based models, e.g. models of interactive 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 model from the sociological literature and the documentation in a seminar thesis.
SkriptThe lecture slides will be presented on the course web page after each lecture.
LiteraturLiterature, in particular regarding computer models in the 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 for further use by others and must be handed over to the Chair of Computational Social Science (COSS) for further free and unrestricted use.
860-0030-00LDigitale Nachhaltigkeit Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 45

Diese LE ersetzt die LE 851-0591-00 Digitale Nachhaltigkeit in der Wissensgesellschaft. Studierende, die die Lerneinheit 851-0591 Digitale Nachhaltigkeit belegt hatten dürfen die Lerneinheit 860-0030-00L nicht besuchen und anrechnen lassen.

Besonders geeignet für Studierende D-INFK, D-ITET, D-MATL, D-MAVT, D-MTECT, D-USYS
3 KP2VM. M. Dapp, D. Helbing
KurzbeschreibungWie beeinflussen verschiedene Interessen die Methoden der Produktion, Verteilung und Nutzung digitaler Ressourcen? Den gängigen Ansätzen mit starker Betonung Geistigen Eigentums werden offene Ansätze, zum Beispiel Open Source/Content/Access, gegenübergestellt. Der Fokus liegt auf den Auswirkungen dieser Ansätze und »digitaler Nachhaltigkeit« als möglicher Vision für die Gesellschaft.
LernzielIm Zentrum des Diskurses steht der Umgang mit digitalen Gütern und Geistigem Eigentum in unserer Gesellschaft. Digitalisierung und Internet ermöglichen einen Umgang mit Wissen, der in direktem Gegensatz zum traditionellen Verständnis von "Geistigem Eigentum" und den darauf fussenden Industrien steht. Ausgehend von ökonomischen und rechtlichen Grundlagen werden proprietäre und offene/«freie» Modelle einander gegenüber gestellt. Nachhaltige Entwicklung wird als Konzept auf digitale Güter übertragen, so dass die besondere Natur digitaler «Dinge» berücksichtigt wird.
Die Studierenden können anschliessend (hoffentlich)
- die besondere Natur digitaler Güter im Gegensatz zu physischen abgrenzen
- die Grundkonzepte von Urheberrecht und Patentrecht kritisch erläutern
- das Grundprinzip von Blockchains als jüngste offene Entwicklung erklären
- politisch-rechtliche und ökonomische Unterschiede proprietärer und offener Ansätze bei der Produktion und Nutzung digitaler Güter erklären
- an einem Beispiel erklären, was digitale Nachhaltigkeit bedeutet und worin die Relevanz des Konzepts für Wissensgesellschaften liegt
- Ansätze der Freien/Open Source Software auf andere digitale Güter übertragen (z.B. Open Content, Open Access)
InhaltTechnische Realität: In Minuten können wir perfekte Kopien hochwertigen digitalen Wissens oder Kultur (als Text, Audio, Video, Grafik oder Software) über den gesamten Globus verteilen. Und dies zu verschwindend geringen Kosten. «Digitalisierung plus Internet» ermöglichen erstmals in der Geschichte der Menschheit den (theoretisch) freien Zugang und Austausch von Wissen weltweit zu minimalen Kosten. Eine immense Chance für die Weiterentwicklung der Gesellschaften in Nord und Süd. «Cool, so what's the problem?»
Das Problem ist, dass diese Realität das heutige Geschäftsmodell der Wissens- und Kulturindustrien (vom Music Label und Hollywood über den Verlag bis zum Software-Hersteller) in seinen Grundfesten bedroht. Es sind mächtige kommerzielle Interessen im Spiel, denn die Bedeutung von «Wissen» als viertem Produktionsfaktor wird im 21. Jahrhundert weiter stark zunehmen. Dementsprechend hart ist das Vorgehen gegen «Raubkopierer», «Softwarepiraten» und «File-Sharer». Eine Kernfrage ist das Konzept des Eigentums an digitalem Wissen. Herangezogen wird ein Jahrhunderte altes Konzept von «Geistigem Eigentum», das der digitalen Realität nicht Rechnung trägt und teilweise zu absurden Situationen führt. Das ursprüngliche Ziel - die Weiterentwicklung der Gesellschaft durch eine möglichst grosse Verbreitung von Wissen - droht vergessen zu gehen.
Der Umgang mit dem PC entwickelt sich zur neuen Kulturtechnik des 21. Jahrhunderts. Neu daran ist, dass diese Kulturtechnik im Gegensatz zu «Lesen, Schreiben und Rechnen» nicht autonom existiert, sondern auf eine Soft- und Hardware-Infrastruktur angewiesen ist. Diese Bindung erzeugt eine Abhängigkeit vom Anbieter der Infrastruktur, der technisch «Spielregeln» festlegen kann, die dem Benutzer Freiheiten nehmen oder sie begrenzen können. Selbst der Fortgeschrittene kann diese (häufig verdeckt) implementierten Spielregeln technisch nur schwer erkennen und deren gesellschaftliche Bedeutung kaum bewerten. Doch gerade diese unsichtbaren Konsequenzen gilt es zu begreifen und zu hinterfragen, denn sie kontrollieren Zugriff, Verteilung und Nutzung des digitalen Wissens.
Vergleichbar mit der Öko-Bewegung in den 60/70er Jahren, existiert eine wachsende politische Bewegung für «Freie Software», dessen populärstes Symbol «GNU/Linux» ist. Sie kämpft dafür, dass Softwarecode als zentrales Kulturgut nicht als Privateigentum behandelt wird, sondern frei von Privatinteressen allen zur Verfügung steht. Mit dem Erfolg dieser Bewegung sind weitere Initiativen entstanden, die die Konzepte der Freien Software auf andere Wissensbereiche (z.B. akademisches Wissen, Musik) übertragen...
Als Vorgeschmack sei das Essay «ETH Zurich - A Pioneer in Digital Sustainability!» empfohlen. Es kann auf www.essays2030.ethz.ch heruntergeladen werden.
SkriptDie Folien und weitere Unterlagen (beides i.d.R. englischsprachig) werden wöchentlich online verfügbar sein.
LiteraturInhalte der folgenden Bücher (als freie PDFs online erhältlich) werden behandelt:
1 Volker Grassmuck, Freie Software - Zwischen Privat- und Gemeineigentum, Bundeszentrale für Politische Bildung, 2. Aufl. Bonn 2004.
2 François Lévêque & Yann Ménière, The Economics of Patents and Copyright, Berkeley Electronic Press, 2004.
3 Yochai Benkler, The Wealth of Networks, Yale University Press. New Haven 2006.
http://www.benkler.org/wealth_of_networks

Zur Vertiefung empfohlen:
1 (allgemein) Chris DiBona et al., Open Sources Voices from the Open Source Revolution, O'Reilly, 1999.
2 (Politologie) Steven Weber, The Success of Open Source, Harvard UP, 2004.
3 (Recht) James Boyle, Shamans, Software, & Spleens - Law and The Construction of the Information Society, Harvard UP, 1996.
4 (Recht) Lawrence Lessig, Code and Other Laws of Cyberspace, Basic Books, New York 1999.
Voraussetzungen / BesonderesAus organisatorischen und didaktischen Gründen (hoher Grad an Interaktion und Gruppenarbeit zu aktuellen Themen als Kreditbedingung) ist die Zahl auf 45 Teilnehmende limitiert. Natürlich sind alle Interessierte eingeladen, die LV auch ohne Semesterleistung zu besuchen.