Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Search result: Catalogue data in Spring Semester 2020

GESS Science in Perspective Information
Only the courses listed below will be recognized as "GESS Science in Perspective" courses.

Further below you will find courses under the category "Type B courses Reflections about subject specific methods and content" as well as the language courses.

During the Bachelor’s degree Students should acquire at least 6 ECTS and during the Master’s degree 2 ECTS.

Students who already took a course within their main study program are NOT allowed to take the course again.
Type B: Reflection About Subject-Specific Methods and Contents
Subject-specific courses: Recommended for bachelor students after their first-year examination and for all master- or doctoral students.

Students who already took a course within their main study program are NOT allowed to take the same course again.

All these courses are listed under the category “Typ A”, this means, every student can enroll in these courses.
D-MTEC
NumberTitleTypeECTSHoursLecturers
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
W3 credits3SN. Antulov-Fantulin
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.
Prerequisites / NoticeGood programming skills and a good understanding of probability & statistics and calculus are expected.
351-0578-00LIntroduction to Economic Policy
Does not take place this semester.
W2 credits2V
AbstractFirst approach to the theory of economic policy.
ObjectiveFirst approach to the theory of economic policy.
ContentWirtschaftspolitik ist die Gesamtheit aller Massnahmen von staatlichen Institutionen mit denen das Wirtschaftsgeschehen geregelt und gestaltet wird. Die Vorlesung bietet einen ersten Zugang zur Theorie der Wirtschaftspolitik.

Gliederung der Vorlesung:

1.) Wohlfahrtsökonomische Grundlagen: Wohlfahrtsfunktion, Pareto-Optimalität, Wirtschaftspolitik als Mittel-Zweck-Analyse u.a.

2.) Wirtschaftsordnungen: Geplante und ungeplante Ordnung
3.) Wettbewerb und Effizienz: Hauptsätze der Wohlfahrtsökonomik, Effizienz von Wettbewerbsmärkten
4.) Wettbewerbspolitik: Sicherstellung einer wettbewerblichen Ordnung

Gründe für Marktversagen:
5.) Externe Effekte
6.) Öffentliche Güter
7.) Natürliche Monopole
8.) Informationsasymmetrien
9.) Anpassungskosten
10.) Irrationalität

11.) Wirtschaftspolitik und Politische Ökonomie

Die Vorlesung beinhaltet Anwendungsbeispiele und Exkurse, um eine Verbindung zwischen Theorie und Praxis der Wirtschaftspolitik herzustellen. Z. B. Verteilungseffekte von wirtschaftspolitischen Massnahmen, Kartellpolitik am Ölmarkt, Internalisierung externer Effekte durch Emissionshandel, moralisches Risiko am Finanzmarkt, Nudging, zeitinkonsistente Präferenzen im Bereich der Gesundheitspolitik
Lecture notesNein.
363-0532-00LEconomics of Sustainable DevelopmentW3 credits2VL. Bretschger
AbstractConcepts and indicators of sustainable development, paradigms of weak and strong sustainability;
neoclassical and endogenous growth models;
economic growth in the presence of exhaustible and renewable resources; pollution, environmental policy and growth;
role of substitution and technological progress;
Environmental Kuznets Curve; sustainability policy.
ObjectiveThe aim is to develop an understanding of the implications of sustainable development for the long-run development of economies. It is to be shown to which extent the potential for growth to be sustainable depends on substitution possibilities, technological change and environmental policy.
After successful completion of this course, students are able to
1. understand the causes of long-term economic development
2. analyse the influence of natural resources and pollution on the development of social welfare
3. to appropriately classify the role of politics in the pursuit of sustainability goals.
ContentThe lecture introduces different concepts and paradigms of sustainable development. Building on this foundation and following a general introduction to the modelling of economic growth, conditions for growth to be sustainable in the presence of pollution and scarce natural resources are derived. Special attention is devoted to the scope for substitution and role of technological progress in overcoming resource scarcities. Implications of environmental externalities are regarded with respect to the design of environmental policies.
Concepts and indicators of sustainable development, paradigms of weak and strong sustainability, sustainability optimism vs. pessimism;
introduction to neoclassical and endogenous growth models;
pollution, environmental policy and growth;
role of substitution possibilities and technological progress;
Environmental Kuznets Curve: concept, theory and empirical results;
economic growth in the presence of exhaustible and renewable resources, Hartwick rule, resource saving technological change.
Lecture notesWill be provided successively in the course of the semester.
LiteratureBretschger, F. (1999), Growth Theory and Sustainable Development, Cheltenham: Edward Elgar.

Bretschger, L. (2004), Wachstumstheorie, Oldenbourg, 3. Auflage, München.

Bretschger, L. (2018), Greening Economy, Graying Society, CER-ETH Press, ETH Zurich.

Perman, R., Y. Ma, J. McGilvray and M. Common (2011), Natural Resource and Environmental Economics, Longman , 4th ed., Essex.

Neumayer, E. (2003), Weak and Strong Sustainability, 2nd ed., Cheltenham: Edward Elgar.
363-1039-00LIntroduction to NegotiationW3 credits2GM. Ambühl
AbstractThe course combines different lecture formats to provide students with both the theoretical background and the practical appreciation of negotiation. A core element of the course is an introduction to the concept of negotiation engineering.
ObjectiveStudents learn to understand and to identify different negotiation situations, analyze specific cases, and discuss respective negotiation approaches based on important negotiation methods (i.a. Game Theory, Harvard Method).
ContentThe course combines different lecture formats to provide students with both the theoretical background and the practical appreciation of negotiation. A core element is an introduction to the concept of negotiation engineering. The course covers a brief overview of different negotiation approaches, different categories of negotiations, selected negotiation models, as well as in-depth discussions of real-world case studies on international negotiations involving Switzerland. Students learn to deconstruct specific negotiation situations, to differentiate key aspects and to develop and apply a suitable negotiation approach based on important negotiation methods.
LiteratureThe list of relevant references will be distributed in the beginning of the course.
363-0564-00LEntrepreneurial RisksW3 credits2GD. Sornette
AbstractDimensions of risks with emphasis on entrepreneurial, financial and social risks.

What young entrepreneurs need to know from start-up creation to investment in innovation

Perspectives on the future of innovation and how to better invent and create

How to innovate and scale up and work with China

Dynamical risk management and learning from the failure of others
ObjectiveWe live a in complex world with many nonlinear
negative and positive feedbacks. Entrepreneurship is one of
the leading human activity based on innovation to create
new wealth and new social developments. This course will
analyze the risks (upside and downside) associated with
entrepreneurship and more generally human activity
in the firms, in social networks and in society.
The goal is to present what we believe are the key concepts
and the quantitative tools to understand and manage risks.
An emphasis will be on large and extreme risks, known
to control many systems, and which require novel ways
of thinking and of managing. We will examine the questions
of (i) how much one can manage and control these risks,
(ii) how these actions may feedback positively or negatively
and (iii) how to foster human cooperation for the creation
of wealth and social well-being.

The exam will be in the format of multiple choice questions.
ContentPART I: INTRODUCTION

Lecture 1 (19/02): Risks (and opportunities) in the economic, entrepreneurial and social spheres
(D. Sornette)


PART II: START-UPS AND INVESTMENT IN INNOVATION

Lecture 2 (26/02): Setting the landscape on entrepreneurship and private investment
(P. Cauwels)

Lecture 3 (04/03 and 11/03): Corporate finance
(P. Cauwels)

Lecture 4 (18/03): Legal, governance and management
(P. Cauwels)

Lecture 5 (25/03): Investors in the innovation economy
(P. Cauwels)


PART III: HOW TO PREDICT THE FUTURE

Lecture 6 (01/04): Historical perspective
(P. Cauwels)

Lecture 7 (08/04): The logistic equation of growth and saturation
(D. Sornette)

Lecture 8 (22/04): Future perspective
(P. Cauwels)

Lecture 9 (29/04): The fair reward problem, the illusion of success and how to solve it
(P. Cauwels)


PART IV: HOW TO WORK WITH CHINA
“if China succeeds, the world succeeds; if China fails, the world fails” (D. Sornette).

Lecture 10 (06/05): The macro status in China and the potential opportunity and risks for the world
(K. Wu)

Lecture 11 (13/05): The collision of the two opposite mindsets: Innovation and Entrepreneurship in China and Switzerland
(K. Wu)


PART V: ESSENTIALS ON DYNAMICAL RISK MANAGEMENT

Lecture 12 (20/05): Principles of Risk Management for entrepreneurship
(D. Sornette)

Lecture 13 (27/05): The biology of risks and war principles applied to management
(D. Sornette)
Lecture notesThe lecture notes will be distributed a the beginning of
each lecture.
LiteratureI will use elements taken from my books

-D. Sornette
Critical Phenomena in Natural Sciences,
Chaos, Fractals, Self-organization and Disorder: Concepts and Tools,
2nd ed. (Springer Series in Synergetics, Heidelberg, 2004)

-Y. Malevergne and D. Sornette
Extreme Financial Risks (From Dependence to Risk Management)
(Springer, Heidelberg, 2006).

-D. Sornette,
Why Stock Markets Crash
(Critical Events in Complex Financial Systems),
(Princeton University Press, 2003)

as well as from a variety of other sources, which will be
indicated to the students during each lecture.
Prerequisites / Notice-A deep curiosity and interest in asking questions and in attempting to
understand and manage the complexity of the corporate, financial
and social world

-quantitative skills in mathematical analysis and algebra
for the modeling part.
751-1500-00LDevelopment EconomicsW3 credits2VI. Günther, K. Harttgen
AbstractIntroduction into basic theoretical and empirical aspects of economic development. Prescriptive theory of economic policy for poverty reduction.
ObjectiveThe goal of this lecture is to introduce students to basic development economics and related economic and developmental contexts.
ContentThe course begins with a theoretical and empirical introduction to the concepts of poverty reduction and issues of combating socioeconomic inequality. Based on this, important external and internal drivers of economic development and poverty reduction are discussed as well as economic and development policies to overcome global poverty. In particular, the following topics are discussed:

- measurement of development, poverty and inequality,
- growth theories
- trade and development
- education, health, population and development
- states and institutions
- fiscal,monetary- and exchange rate policies
Lecture notesNone.
LiteratureGünther, Harttgen und Michaelowa (2020): Einführung in die Entwicklungsökonomik.
Prerequisites / NoticeVoraussetzungen:
Grundlagenkenntisse der Mikro- und Makroökonomie.

Besonderes:
Die Veranstaltung besteht aus einem Vorlesungsteil, aus eigener Literatur- und Recherchearbeit sowie der Bearbeitung von Aufgabenblättern.

Die Vorlesung basiert auf: Günther, Harttgen und Michaelowa (2019): Einführung in die Entwicklungsökonomik. Einzelne Kapitel müssen jeweils vor den Veranstaltungen gelesen werden. In den Veranstaltungen wird das Gelesene diskutiert und angewendet. Auch werden offene Fragen der Kapitel und Übungen besprochen.
851-0732-03LIntellectual Property: An Introduction Information Restricted registration - show details
Number of participants limited to 180

Particularly suitable for students of D-ARCH, D-BIOL, D-CHAB, D-INFK, D-ITET, D-MAVT, D- MATL, D-MTEC.
W2 credits2VS. Bechtold, M. W. Schonger
AbstractThe course introduces students to the basics of the intellectual property system and of innovation policy. Areas covered include patent, copyright, trademark, design, know-how protection, open source, and technology transfer. The course looks at Swiss, European, U.S. and international law and uses examples from a broad range of technologies. Insights can be used in academia, industry or start-ups.
ObjectiveIntellectual property issues become more and more important in our society. In order to prepare students for their future challenges in research, industry or start-ups, this course introduces them to the foundations of the intellectual property system. The course covers patent, copyright, trademark, design, know-how protection, open source, and technology transfer law. It explains links to contract, antitrust, Internet, privacy and communications law where appropriate. While the introduction to these areas of the law is designed at a general level, examples and case studies come from various jurisdictions, including Switzerland, the European Union, the United States, and international law.

In addition, the course introduces students to the fundamentals of innovation policy. After exposing students to the economics of intellectual property protection, the course asks questions such as: Why do states grant property rights in inventions? Has the protection of intellectual property gone too far? How do advances in biotechnology and the Internet affect the intellectual property system? What is the relationship between open source, open access and intellectual property? What alternatives to intellectual property protection exist?

Knowing how the intellectual property system works and what kind of protection is available is useful for all students who are interested in working in academia, industry or in starting their own company. Exposing students to the advantages and disadvantages of the intellectual property system enables them to participate in the current policy discussions on intellectual property, innovation and technology law. The course will include practical examples and case studies as well as guest speakers from industry and private practice.
851-0252-10LProject in Behavioural Finance Restricted registration - show details
Number of participants limited to 40

Particularly suitable for students of D-MTEC
W3 credits2SS. Andraszewicz, C. Hölscher, D. Kaszás
AbstractThis interactive practical course provides and overview of the key topics in behavioral finance. Along studying information about investor's behavior, decision-making, cognitive, biological and personality markers of risk taking and measuring risk appetite, students train critical thinking, argumentation and presentation. The learning process is based on interactive discussions and presentations.
ObjectiveThis course provides an overview of the key topics in behavioural finance and gives the opportunity for a first hands-on experience in designing, analysing and presenting a behavioural study. In the first half of the semester, students present papers from different topics within behavioural finance, including Judgment and Decision Making, psychometrics and individual differences, and risk perception and eliciting people’s propensity to take risk, biological markers of risk taking and investment behavior and trading games. The paper presentations are informal, require no power-point presentations and are followed by a discussion with the rest of the students in the class. The goal of these presentations is three-fold: in an interactive and engaging way, to provide an overview of the topics contained in the area of behavioural finance, to teach students to extract the most relevant information from scientific papers and be able to communicate them to their peers and to enhance critical thinking during the discussion.
In the middle of the semester, the students pick a topic in which they want to conduct a small study. Some topics will be offered by the lecturers, but students are free to choose a topic of their own.
This is followed by fine-tuning their research questions given found literature, data collection and analysis. At the end of the semester students receive feedback and advice on the data analysis and present the results in a formal presentation with slides. The final assignment is a written report from their study. Active participation in the meetings is mandatory to pass the course. This course does not involve learning by heart.

Key skills after the course completion:
- Overview of topics in behavioural finance
- Communication of research output in an a formal and informal way, in an oral and written form
- Critical thinking
- Argumentation and study design
Content- Giving presentations
- How to quickly "read" a paper
- Judgment and Decision Making, Heuristics and Biases
- Biology on the trading floor
- Psychometrics and individual differences
- Eliciting people's propensity to take risks
- Experimental design in behavioural studies
- Experimental Asset Markets
Lecture notesAll learning materials will be available to students over eDoz platform.
LiteratureTversky, A., & Kahneman, D. (1992). Advance in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323

Rieskamp, J. (2008). The probabilistic nature of preferential choice. Journal of Experimental Psychology: Learning, memory and Cognition, 34(6), 1446-1465

Hertwig, R., & Herzog, S. (2009). Fast and frugal heuristics: Tools of social rationality. Social Cognition, 27(5), 661-698

Coates, J.M., Gurnell, M., & Sarnyai, Z. (2010). From molecule to market: steroid hormones and financial risk taking. Philosophical Transacations of the Royal Society B, 365, 331-343

Cueva, C., Roberts, R.E., Spencer, T., Rani, N., Tempest, M., Tobler, P.N., Herbert, J., & Rustichini (2015). Cortisol and testosterone increase financial risk taking and may destabilize markets. Nature, 5(11206), 1-16

Conlin, A., Kyröläinen, P., Kaakinen, M., Järvelin, M-R., Perttunen, J., & Svento, R. (2015). Personality traits and stock market participation. Journal of Empirical Finance, 33, 34-50

Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings in National Academy of Sciences, 110, 5802-5805

Oehler, A., Wedlich, F., Wendt, S., & Horn, M. (July 9, 2016). Available at SSRN: https://ssrn.com/abstract=2807401

Fenton-O'Creevy, M., Nicholson, N., Soane, E., & Willman, P. (2003). Trading on illusions: Unrealistic perceptions of control and trading performance. Journal of Occupational and Organizational Psychology, 76, 53-68

Frey, R., Pedroni, A., Mata, R., Rieskamp, J., & Hertwig, R. (2017). Risk preference shares the psychometric structure of major psychological traits. Science Advances, 3, 1-13

Schürmann, O., Andraszewicz, S., & Rieskamp, J. (2017). The importance of losses when eliciting risk preferences. Under review

Andraszewicz, S., Kaszas, D., Zeisberger, S., Murphy, R.O., & Hölscher, C. (2017). Simulating historical market crashes in the laboratory. Manuscript in preparation.

Allenbach, M., Kaszas, D., Andraszewicz, S., & Hölscher, C. (2017). Skin conductance response as marker or risk undertaken by investors. Manuscript in preparation.

Simic, M., Kaszas, D., Andraszewicz, S., & Hölscher, C. (2017). Incentive structure compatibility in a principal agent problem. Manuscript in preparation.

Sornette, D., Andraszewicz, S., Wu, K., Murphy, R.O., Rindlerm P., & Sanadgol, D. (2017). Overpricing persistance in experimental asset markets with intrinsic uncertainty. Under review.

Andraszewicz, S., Wu, K., & Sornette, D. (2017). Behavioural effects and market dynamics in field and laboratory experimental asset markets. Under review.
Prerequisites / NoticeGrading is based the active participation in the class and the final project. There is no exam.
851-0591-01LBETH - Blockchain for Sustainability Restricted registration - show details
Does not take place this semester.
Number of participants limited to 200

Particularly suitable for students of D-INFK, D-MTEC, D-ITET, D-MAVT,D-PHYS
W3 credits4GD. Helbing
AbstractBlockchain 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.
ObjectiveThe 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!
851-0739-01LSequencing Legal DNA: NLP for Law and Political Economy
Particularly suitable for students of D-INFK, D-ITET, D-MTEC
W3 credits2VE. Ash
AbstractThis course explores the application of natural language processing techniques to texts in law, politics, and the news media. Students will put these tools to work in a course project.
ObjectiveLaw is embedded in language. An essential task for a judge, therefore, is reading legal texts to interpret case facts and apply legal rules. Can an artificial intelligence learn to do these tasks? The recent and ongoing breakthroughs in natural language processing (NLP) hint at this possibility.

Meanwhile, a vast and growing corpus of legal documents are being digitized and put online for use by the public. No single human could hope to read all of them, yet many of these documents remain untouched by NLP techniques. This course invites students to participate in these new explorations applying NLP to the law -- that is, sequencing legal DNA.
ContentNLP technologies have the potential to assist judges in their decisions by making them more efficient and consistent. On the other hand, legal language choices -- as in legal choices more generally -- could be biased toward some groups, and automated systems could entrench those biases. We will explore, critique, and integrate the emerging set of tools for debiasing language models and think carefully about how notions of fairness should be applied in this domain.

More generally, we will explore the use of NLP for social science research, not just in the law but also in politics, the economy, and culture. In a semester paper, students (individually or in groups) will conceive and implement their own research project applying natural language tools to legal or political texts.
Prerequisites / NoticeSome programming experience in Python is required, and some experience with NLP is highly recommended.
851-0739-02LSequencing Legal DNA: NLP for Law and Political Economy (Course Project)
This is the optional course project for "Building a Robot Judge: Data Science for the Law."

Please register only if attending the lecture course or with consent of the instructor.

Some programming experience in Python is required, and some experience with text mining is highly recommended.
W2 credits2VE. Ash
AbstractThis is the companion course for extra credit for a more substantial project, for the course "Sequencing Legal DNA: NLP for Law and Political Economy".
Objective
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