Dirk Helbing: Catalogue data in Spring Semester 2018

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
851-0252-04L	Behavioral Studies Colloquium Number of participants limited to 50.	2 credits	2K	M. Kapur, H.‑D. Daniel, D. Helbing, C. Hölscher, R. Schubert, C. Stadtfeld, E. Stern, E. Ziegler
Abstract	This 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.
Learning objective	Students 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.
Content	This 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.
851-0585-38L	Data Science in Techno-Socio-Economic Systems 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 credits	2V	E. Pournaras, T. Guo, D. Helbing
Abstract	This course introduces how techno-socio-economic systems in our nowadays digital society can be better understood with techniques and tools of data science. Students shall learn the fundamentals of data science, machine learning, but also advanced distributed real-time data analytics in the Planetary Nervous System. Students shall deliver and present a seminar thesis at the end of the course.
Learning objective	The goal of this course is to qualify students with knowledge on data science as a way to understand complex techno-socio-economic systems in our nowadays digital 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 and collaboration platforms such as the Planetary Nervous System. The course shall increase the awareness level of students about the challenges and open issues of data science in socio-technical domains such as privacy. Finally students have the opportunity to develop their writing, presentation and collaboration skills based on a seminar thesis they have to deliver and present at the end of the course
851-0588-00L	Introduction to Game Theory Number of participants limited to 400 Particularly suitable for students of D-INFK, D-MATH	3 credits	2V	H. Nax, D. Helbing, B. Pradelski
Abstract	This course introduces the foundations of game theory with a focus on its basic mathematical principles. It treats models of social interaction, conflict and cooperation, the origin of cooperation, and concepts of strategic decision making behavior. Examples, applications, theory, and the contrast between theory and empirical results are particularly emphasized.
Learning objective	Learn the fundamentals, models, and logic of thinking about game theory. Learn basic mathematical principles. Apply formal game theory models to strategic interaction situations and critically assess game theory's capabilities through a wide array of applications and experimental results.
Content	Game theory provides a unified mathematical language to study interactions amongst different types of individuals (e.g. humans, firms, nations, animals, etc.). It is often used to analyze situations involving conflict and/or cooperation. The course introduces the basic concepts of both non-cooperative and cooperative game theory (players, strategies, coalitions, rules of games, utilities, etc.) and explains the most prominent game-theoretic solution concepts (Nash equilibrium, sub-game perfection, Core, Shapley Value, etc.). We will also discuss standard extensions (repeated games, incomplete information, evolutionary game theory, signal games, etc.). In each part of the course, we focus on examples and on selected applications of the theory in different areas. These include analyses of cooperation, social interaction, of institutions and norms, social dilemmas and reciprocity as well as applications on strategic behavior in politics and between countries and companies, the impact of reciprocity, in the labor market, and some applications from biology. Game theory is also applied to control-theoretic problems of transport planning and computer science. As we present theory and applications, we will also discuss how experimental and other empirical studies have shown that human behavior in the real world often does not meet the strict requirements of rationality from "standard theory", leading us to models of "behavioural" and "experimental" game theory. By the end of the course, students should be able to apply game-theoretic in diverse areas of analysis including > controlling turbines in a wind park, > nations negotiating international agreements, > firms competing in markets, > humans sharing a common resource, etc.
Lecture notes	See literature below. In addition we will provide additional literature readings and publish the lecture slides directly after each lecture.
Literature	K Binmore, Fun and games, a text on game theory, 1994, Great Source Education SR Chakravarty, M Mitra and P Sarkar, A Course on Cooperative Game Theory, 2015, Cambridge University Press A Diekmann, Spieltheorie: Einführung, Beispiele, Experimente, 2009, Rowolth MJ Osborne, An Introduction to Game Theory, 2004, Oxford University Press New York J Nash, Non-Cooperative Games, 1951, Annals of Mathematics JW Weibull, Evolutionary game theory, 1997, MIT Press HP Young, Strategic Learning and Its Limits, 2004, Oxford University Press
851-0591-01L	BIOTS - Blockchain And the Internet of Things Number of participants limited to 250 Particularly suitable for students of D-INFK, D-MTEC, D-ITET, D-MAVT,D-PHYS	3 credits	4G	M. M. Dapp, D. Helbing, S. Klauser
Abstract	Blockchain and Internet of Things technologies hold the promise to transform our societies and economies. While IoT devices allow us to measure all kinds of activity by humans and machines, the blockchain allows us to securely time-stamp and value this data and even give it a price to trade it on (new) markets. We explore this potential with a specific focus on sustainable development.
Learning objective	The course provides opportunities to gain fundamental understanding of promising new technologies as well as develop creative decentralized solutions for societal challenges using these technologies. Participants will learn the fundamentals of blockchain technology, its mechanisms, design parameters and potential for decentralized solutions. Those with software development skills will then further explore the blockchain to develop hands-on decentralized applications and smart contracts. Non-coding participants will further explore how these technologies could be used to design new economic systems. These new cryptoeconomic systems should give citizens multiple incentives to increase cooperation, health, recycling, or education and other positive externalities and to decrease emissions, waste, noise, or stress and other negative externalities. During the hackathon, participants will work in mixed teams on concrete challenges addressing some of the pressing global challenges our societies face, like climate change, financial instability, energy, or mass migration, etc. The aim is to develop decentralized approaches towards a sustainable, sharing circular economy using blockchain and IoT technologies. Teams will produce a short report (about 10 pages), demonstrate their hackathon prototype based on blockchain technology (Ethereum platform) and present to a interdisciplinary jury on the last day. Throughout the course, participants will hone their critical thinking abilities by leaving their own discipline and discussing best approaches to solve global complex challenges in an international, multi-disciplinary setting with invited subject matter experts and peers from all around the world. We encourage students with no programming experience, who are interested in the potential of blockchain and IoT to address global challenges, to apply as well!
860-0022-00L	Complexity and Global Systems Science Prerequisites: solid mathematical skills. Particularly suitable for students of D-ITET, D-MAVT and ISTP	3 credits	2V	D. Helbing, K. K. Kleineberg
Abstract	This 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.
Learning objective	Participants 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.
Content	This 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 cascade 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.
Prerequisites / Notice	Mathematical skills can be helpful
860-0024-00L	Digital Society: Ethical, Societal and Economic Challenges Number of participants is limited to 35	3 credits	2V	D. Helbing, M. M. Dapp
Abstract	This 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...
Learning objective	Participants shall learn to understand that any technology implies not only opportunities, but also risks, and that 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.
Literature	Will be provided on a complementary website of the course. Complementary literature should be searched and evaluated by the students themselves.
Prerequisites / Notice	To 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.