From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers via e-mail.

David N. Bresch: Catalogue data in Spring Semester 2020

Name Prof. Dr. David N. Bresch
FieldWeather and Climate Risks
Address
Institut für Umweltentscheidungen
ETH Zürich, CHN K 73.2
Universitätstrasse 16
8092 Zürich
SWITZERLAND
Telephone+41 44 632 77 87
E-maildbresch@ethz.ch
URLhttps://www.usys.ethz.ch/en/people/profile.david-bresch.html
DepartmentEnvironmental Systems Science
RelationshipFull Professor

NumberTitleECTSHoursLecturers
363-1114-00LIntroduction to Risk Modelling and Management3 credits2VB. J. Bergmann, D. N. Bresch, J. Teichmann
AbstractThis course provides an introduction to various aspects of modelling, dealing and managing risk across different industries, contexts and applications. Classes will alternate between risk professionals from industry and government and academics coming from different disciplines.
ObjectiveStudents get familiar with the building blocks of risk modelling: uncertainty, vulnerability, resilience, decision-making under uncertainty. The course looks at different approaches to modelling and dealing as well as mitigating different kind of risks in different industries and get to understand the relation to the decision-making process in business and the value chain of a company. Cases range from enterprise risk management, natural catastrophes, climate risk, energy market risk, risk engineering, financial risks, operational risk, cyber risk and more. An additional emphasis will be on the data-driven approach to smart algorithms applied to risk modelling and management. After taking this course, students should be able to demonstrate that they can identify and formulate a risk analysis problem with quantitative methods in a particular field.
ContentThe course covers the following areas:
1. Fundamentals of Risk Modelling: Probability, Uncertainty, Vulnerability, Decision-Making under Uncertainty
2. Fundamentals of Risk Management and Enterprise Risk Management
3. Risk Modelling and Management across Different areas with invited Speakers
The list of Speakers can be found here: https://riskcenter.ethz.ch/education/lectures/introduction-to-risk-modelling-and-management--.html
Lecture notesLecture notes and slides will be provided via moodle
364-1058-00LRisk Center Seminar Series0 credits2SA. Bommier, D. Basin, D. N. Bresch, L.‑E. Cederman, P. Cheridito, H. Gersbach, G. Sansavini, F. Schweitzer, D. Sornette, B. Stojadinovic, B. Sudret, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen
AbstractThis course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling and governing complex socio-economic systems, and managing risks and crises. Students and other guests are welcome.
ObjectiveParticipants 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 novel mathematical models and approaches for open problems, to analyze them with computers or other means, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level.
ContentThis course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the seminar. Students and other guests are welcome.
Lecture notesThere is no script, but the sessions will be recorded and be made available. Transparencies of the presentations may be put on the course webpage.
LiteratureLiterature will be provided by the speakers in their respective presentations.
Prerequisites / NoticeParticipants should have relatively good scientific, in particular mathematical skills and some experience of how scientific work is performed.
651-4095-01LColloquium Atmosphere and Climate 1 Information Restricted registration - show details 1 credit1KC. Schär, H. Wernli, D. N. Bresch, D. Domeisen, N. Gruber, H. Joos, R. Knutti, U. Lohmann, T. Peter, S. I. Seneviratne, K. Steffen, M. Wild
AbstractThe colloquium is a series of scientific talks by prominent invited speakers assembling interested students and researchers from around Zürich. Students take part of the scientific discussions.
Objective-get insight into ongoing research in different fields related to atmospheric and climate science
ContentThe colloquium is a series of scientific talks by prominent invited speakers assembling interested students and researchers from around Zürich. Students take part of the scientific discussions.
Prerequisites / NoticeTo acquire credit points for this colloquium, please confirm your attendance of 8 colloquia per semester by using the form which is provided at the course webpage.
651-4095-02LColloquium Atmosphere and Climate 2 Information Restricted registration - show details 1 credit1KC. Schär, H. Wernli, D. N. Bresch, D. Domeisen, N. Gruber, H. Joos, R. Knutti, U. Lohmann, T. Peter, S. I. Seneviratne, K. Steffen, M. Wild
AbstractThe colloquium is a series of scientific talks by prominent invited speakers assembling interested students and researchers from around Zürich. Students take part of the scientific discussions.
Objective-get insight into ongoing research in different fields related to atmospheric and climate sciences
Prerequisites / NoticeTo acquire credit points for this colloquium, please confirm your attendance of 8 colloquia per semester by using the form which is provided at the course webpage.
651-4095-03LColloquium Atmosphere and Climate 3 Information Restricted registration - show details 1 credit1KC. Schär, H. Wernli, D. N. Bresch, D. Domeisen, N. Gruber, H. Joos, R. Knutti, U. Lohmann, T. Peter, S. I. Seneviratne, K. Steffen, M. Wild
AbstractThe colloquium is a series of scientific talks by prominent invited speakers assembling interested students and researchers from around Zürich. Students take part of the scientific discussions.
Objective-get insight into ongoing research in different fields related to atmospheric and climate sciences
Prerequisites / NoticeTo acquire credit points for this colloquium, please confirm your attendance of 8 colloquia per semester by using the form which is provided at the course webpage.
701-0650-00LRisk Analysis and Management3 credits2GA. Patt, D. N. Bresch, J. Wohland
AbstractThis course introduced students to principles of quantitative risk analysis, across a wide variety of environmental areas including weather and climate, natural hazards, and toxic substances. It also introduces them to established practices of risk management, including regulatory approaches, insurance, and contingency planning.
Objective- Competence in applying methods of quantitative risk analysis.
- Understanding of common approaches towards risk management.
- Understanding of the importance of risk and uncertainty in decision- and policy-making.
- Ability to communicate risk information clearly and effectively.
ContentStatistics for risk analysis; Monte Carlo simulation; toxicology and epidemiology; exposure assessment; fault tree analysis; risk in decision-making; risk perception and communication; loss spreading and insurance; mitigating natural hazard losses; risk and climate change policy.
Prerequisites / Noticenone
701-1252-00LClimate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation Restricted registration - show details 3 credits2V + 1UD. N. Bresch, R. Knutti
AbstractThe course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation.
ObjectiveStudents will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (in Python), hence basic understanding of scientific programming forms a prerequisite of the course.
ContentThe first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society.
The course provides an introduction to the following themes:
1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events
2) methods to optimize and constrain model parameters using observations
3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer)
4) basics of economic evaluation, economic decision making in the presence of climate risks and pre-emptive risk management to optimally allocate resources
Lecture notesPowerpoint slides will be made available.
LiteratureMany papers for in-depth study will be referred to during the lecture.
Prerequisites / NoticeHands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence good understanding of scientific programming forms a prerequisite of the course, in Python (teaching language, object oriented) or similar. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required.

Examination: graded tutorials during the semester (benotete Semesterleistung)
744-0101-00LModule 1: Systems Thinking Restricted registration - show details
Only for CAS in Collaborative Decision Making Under Uncertainty.
1 credit1GB. B. Pearce, D. N. Bresch, M. Stauffacher
AbstractThis is the first of nine modules for the Certificate of Advanced Studies in Collaborative Decision Making Under Uncertainty. Each module is designed to focus on a particular methodology and a specific theme related to climate risk and sustainable development. Each module is titled after the methodological focus of each module. The focus of this module is systems thinking.
ObjectiveThe focus of this first module is to introduce participants to a fundamental way of tackling complexity and analyzing the world using systems thinking.

The learning objectives are to:
- Understand and apply systems thinking to strategic decision making scenarios.
- Analyze decision making processes within participants’ institutional settings.
- Identify common core concepts that connects challenges experienced by each participant’s decision making situation.
ContentCollect decision making challenges within the working context of each participant by applying concepts from systems thinking. The methods that are likely to be used are storytelling, rich picture creation from soft systems methodology, concept mapping, qualitative systems modeling, and peer feedback.
LiteratureIncludes selected readings from:

Midgley, G. (2000). Systemic intervention. In Systemic Intervention (pp. 113-133). Springer, Boston, MA.
Senge, P. M. (2006). The Fifth Discipline (2nd ed.). New York: Random House.
Vester, F. (1988). The biocybernetic approach as a basis for planning our environment. Systems Practice, 1(4), 399–413. http://doi.org/10.1007/BF01066582

Additional readings and exercises will be announced in class.
Prerequisites / NoticeAll 9 modules must be completed to obtain the Certificate of Advanced Studies.
744-0102-00LModule 2: Macrocognition and Elicitation of Expert Knowledge Restricted registration - show details
Only for CAS in Collaborative Decision Making Under Uncertainty.
1 credit1GB. B. Pearce, D. N. Bresch
AbstractThis is the second of nine modules for the Certificate of Advanced Studies in Collaborative Decision Making Under Uncertainty. The focus of this module is on the elicitation and use of expert knowledge for collaborative processes.
ObjectiveThe focus of this second module is to introduce participants to the concepts of macrocognition and ways in which expert knowledge can be elicited for collaborative problem solving.

The learning objectives are to:
- Understand and practice using knowledge maps, influence diagrams, concept mapping and other methods for eliciting expert knowledge.
- Apply methods for eliciting unknown unknowns from a group.
- Identify the expertise within the group for understanding complexity.
- Relate to core challenges identified from Module 1.
LiteratureIncludes selected readings from:

Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515–526. http://doi.org/10.1037/a0016755
Klein, G. A. (1998). Sources of power: How people make decisions. Cambridge, MA: MIT press.
Klein, G. A., Calderwood, R., & Macgregor, D. (1989). Critical Decision Method for Eliciting Knowledge (Vol. 19, pp. 462–472). Presented at the IEEE Transactions on Systems, Man and Cybernetics. http://doi.org/10.1109/21.31053
Crandall, B., Klein, G. A., & Hoffmann, R. R. (2006). Working Minds. Cambridge: MIT Press.

Additional readings and exercises will be announced in class.
Prerequisites / NoticeParticipation and completion of Module 1.
All modules must be completed in order to receive the certificate.
744-0103-00LModule 3: Mental Models Theory Restricted registration - show details
Only for CAS in Collaborative Decision Making Under Uncertainty.
1 credit1GB. B. Pearce, D. N. Bresch
AbstractThis is the third of nine modules for the Certificate of Advanced Studies in Collaborative Decision Making Under Uncertainty. This focus of this module is on the use of mental models theory for collaborative processes.
ObjectiveThe focus of this third module is to introduce participants to mental model theories and related concepts that are relevant for creating conditions for effective problem framing/problem structuring.

The learning objectives are to:
- Understand the relationship between the concept of a “mental model”, shared mental models and problem .framing/problem structuring
- Exchange and integrate participants’ own mental models for problem identification.
- Experience the importance of problem framing for effective collaboration.
- Create problem definitions for group projects.
LiteratureIncludes selected readings from:

Argyris, C. (1976). Single-loop and double-loop models in research on decision making. Administrative Science Quarterly, 363–375.
Johnson-Laird, P. N. (1983). Mental Models. Cambridge, MA: Harvard University Press.
Morgan, M. G., Fischhoff, B., Bostrom, A., & Atman, C. J. (2002). Risk communication: A mental models approach. Cambridge, UK: Cambridge University Press.
Schön, D. A. (1984). The Reflective Practitioner: How Professionals Think In Action. New York: Basic Books.

Additional reading and exercises will be announced in class.
Prerequisites / NoticeCompletion of Modules 1 and 2.
All modules must be completed in order to receive the certificate.
744-0107-00LModule 7: Prototypes Analysis and Systems Thinking Review Restricted registration - show details
Only for CAS in Collaborative Decision Making Under Uncertainty.
1 credit1GB. B. Pearce, D. N. Bresch
AbstractThis is the seventh of nine modules for the Certificate of Advanced Studies in Collaborative Decision Making Under Uncertainty. The focus of this module is to refine strategy prototypes through an exploration of potential unintended consequences and unknown unknowns.
ObjectiveThe focus of this seventh module is to analyze first-level prototypes within a systems thinking framework. Based on the outputs, participants refine their prototypes.

The learning objectives are to:
- Apply systems thinking by integrating solutions with systems analysis to consider possible unintended consequences of decision making
- Review and integrate inputs from previous modules to help with further selection of prototypes and implementation.
Prerequisites / NoticeCompletion of Modules 1-6.
All modules must be completed in order to receive the certificate.
744-0109-00LModule 9: Final Presentation/Output Rounds Restricted registration - show details
Only for CAS in Collaborative Decision Making Under Uncertainty.
1 credit1GB. B. Pearce, D. N. Bresch, M. Stauffacher
AbstractThis is the last of nine modules for the Certificate of Advanced Studies in Collaborative Decision Making Under Uncertainty. The final versions of the strategy prototypes will be presented to the public and discussed during this session.
ObjectiveThe focus of this ninth module is to present, discuss, and reflect on the prototypes developed over the course of the CAS.

The presentations will be open to the public on Friday.

The learning objectives are to:
- Practice communicating ideas of how to engage with uncertainty in strategic decision making.
- Reflect and draw insights from the experience of working in collaboration with others.
- Establish next steps for improved decision making in the future on an individual and organizational level.
Prerequisites / NoticeCompletion of Modules 1-8
All modules must be completed in order to receive the certificate.
744-0500-00LGroup Work between Modules Restricted registration - show details 1 credit11AB. B. Pearce, D. N. Bresch, M. Stauffacher
AbstractBetween each of the nine modules that are a part of the CAS CDM, participants are expected to work on their group projects together. This amount of time should amount to 150 hours in total. Groups will be able to determine the topic and direction of this work during the in-class sessions
ObjectiveThe learning objectives are to:
- Test concepts and methods discussed in-class in the real world.
- Identify differences between theory and practice in the application of these methods.
- Create and test viability of prototypes developed in-class.
Prerequisites / NoticeAccompanies Modules 1-9 as an obligatory element of coursework.