Suchergebnis: Katalogdaten im Herbstsemester 2022
Management, Technologie und Ökonomie Master  Willkommen und Einführung ins MSc ETH MTEC Montag, 19.09.2022, 14.00 - 15.15 h, HG E 1.1 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Systems Design and Risks | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 363-1167-00L | Data Science for Social Challenges  | W | 3 KP | 3G | R. Roller, L. Brandenberger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Many of today's social challenges cannot be adequately grasped simply by observing human behavior. To make these challenges visible and address their causes, we can use advanced statistics to disentangle complex interdependencies between the driving factors. In this course, we build up methodological skills and places a strong focus on interpretation and reflection of results. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | A successful participant of this course will be able to - interpret the results of data analysis with regard to the methodological choices and the operationalization of theoretical concepts - assess potential flaws in research designs that can lead to flawed interpretations of results - apply a wide variety of statistical models (e.g., regressions, difference-in-difference, network models) to different data sources - and name the difference between statistical models and the advantages (or drawbacks) they hold for different data types - name the limitations of observational data analysis, especially with regard to causality - explain the importance of sensitivity and robustness checks for statistical analyses In summary, a successful participant is able to assess quantitative social science research with regard to its research design, the model choice as well as the interpretation drawn from the estimates and make suggestions for improvements. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Data Science for Social Challenges offers a practical approach to the quantitative analysis of human behavior and social interactions. While the course `Social Data Science' focuses on data retrieval and processing, this course focuses on data analysis and interpretation of results. The course is organized in three blocks of increasing data complexity. The first block tackles linear data analyses, where a dependent variable is modeled based on a set of independent and control variables. The second block tackles causal inference, where experimental settings are approximated with observational data to allow for causal interpretation of results. The third block tackles data sources where observations are not independent of each other and therefore defy most statistical models. Here, we examine how people interact with each other and how these interactions affect the people involved in turn. The course covers various application of quantitative social sciences: - measuring biases in societies - analyzing behavior changes (due to internal or external events) - studying deviant behavior and peer effects - exploring coordination between people The course makes the link to sociological theories and shows how they can be used to derive testable hypotheses. A strong focus is laid upon the operationalization of different concepts, such as finding an appropriate measure of deviant behavior or the level of animosity that exists between people at a given time. These measures are tested using appropriate statistical models. Here, the focus is put upon the interpretation (e.g., coefficient sizes and power) as well as the presentation of results (e.g., through marginal effects). Lastly, the course fosters critical thinking by discussing sensitivity and robustness tests. As such, the course offers insights into quantitative research design by following a hands-on approach to the study of societal challenges through social data science. The course includes a lecture, student-led presentations and an accompanying exercise class. In the exercise class students get the opportunity to run through the whole data analysis process. Starting with data inspection, students operationalize theoretical concepts and test them on various statistical models. Strong focus is put on sensitivity checks, where the effect of changes to the model (i.e., adding another control variable) is assessed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Interested students can peruse: Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Hamburg: SAGE Publications. Baur, N., & Blasius, J. (Eds.). (2019). Handbuch Methoden der empirischen Sozialforschung. Wiesbaden: Springer VS. Angrist, J. D., & Pischke, J. S. (2008). Mostly Harmless Econometrics. Princeton: Princeton University Press. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The statistical analyses in the course exercises are performed in R. Students should be interested in learning R skills to run sophisticated quantitative analyses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 363-1162-00L | Resilience in the New Age of Risk | W | 3 KP | 2V | H. Schernberg, C. Hölscher, J. Jörin, G. Sansavini | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | With the global increase in interconnectivity, the potential for disruption is everywhere. Modern organisations who build resilience in all systems will respond intelligently to emergent disruptions. This course explores the concept of resilience and its application to socio-technical systems: The resilience of infrastructure systems and how individuals and social groups interact in and with them. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | After taking this course, you will be able to: - Discuss the concept of resilience and related frameworks and concepts, and explain their relevance in different contexts (organizations, infrastructure, social groups…). - Use and discuss key resilience metrics and use them to analyze infrastructure systems. - Discuss the role of organizational resilience and describe methods to improve it. - Describe how resilience is applied in practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Our increasingly complex and connected systems face continuously emerging disruptions. Resilience constitutes a fundamental departure from the philosophy of risk-management. With resilience, stakeholders adopt risk mitigation strategies aligned to the theories of complex systems. It is, however, difficult to learn about resilience, since it applies to an extremely large array of systems and contexts. Moreover, the topic of resilience is surprisingly absent from most university curricula. This course fills a gap and walks you through a mode of thinking that is bound to shape the way risks and disasters are dealt with in our increasingly connected society. Hence, tomorrow's risk managers will and shall also be "resilience managers". This course breaks down the concept of complex systems and their resilience. It introduces some of the different flavors of resilience and provides tools for building it in various socially relevant areas (social resilience, engineered systems resilience, organizational resilience...). The course is divided in 4 parts. - Part 1: Foundations of Resilience (2 hours) - Part 2: Resilience Analysis: Infrastructure Systems (12 hours) - Part 3: Organizational resilience and sensemaking (6 hours) - Part 4: Resilience in Practice (4 hours) Part 1 introduces the concept of resilience, and the framework in which it is applied. The distinction between resilience and risk management is highlighted, as well as how these approaches complement each other. The founding concepts of resilience are explained and illustrated: vulnerability, disruption, absorption, recovery, adaptation, etc. Part 2 walks you through the analysis of the resilience of infrastructure systems. It introduces the useful metrics of resilience. It provides examples of building resilience into complex systems, by increasing the robustness and recoverability of systems, and reducing vulnerabilities. Finally, students will explore the optimization of infrastructure systems. Part 3. Every system subject to potential disruptions is managed by a human organization. Sensemaking describes how humans frame the problem. It is a process whereby organizational actors attach meaning to external events to resolve the uncertainty surrounding them. Investing in mindfulness improves personal and organizational resilience and success. Finally, the management of organizational resilience is discussed. Part 4 will provide examples of the use of resilience by practitioners, with guest speakers from the public and private sector. This course is aimed at MSc and MAS students, from MTEC and other departments. Ideally, students have a quantitative background and some knowledge of risk management. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | The Science and Practice of Resilience, Book by Benjamin D. Trump and Igor Linkov | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The course is hybrid (in-person or remote). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kompetenzen |
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| 363-1017-00L | Risk and Insurance Economics | W | 3 KP | 2G | H. Schernberg | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course covers the economics of risk and insurance, in particular the following topics will be discussed: 2) individual decision making under risk 3) models of insurance demand, risk sharing, insurance supply 4) information issues in insurance markets 5) advanced topics in microeconomics and behavioral economics 5) the macroeconomic role of insurers and insurance regulation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course introduces students to basic microeconomic models of risk attitudes and highlight the role insurance can – or cannot – play for individuals facing risks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Everyday, we take decisions involving risks. These decisions are driven by our perception of and our appetite for risk. Insurance plays a significant role in people's risk-management strategies. In the first part of this lecture, we discuss a normative decision concept, Expected Utility theory, and compare it with empirically observed behaviour. Students then learn about the rationale for individuals to purchase insurance, and for companies to offer it. We derive the optimal level of insurance demand and discuss how it depends on our model's underlying assumptions. We then discuss the consequences of information asymmetries in insurance markets and the consequences for insurance supply. Finally, we discuss refinements in decision theory that help account for observed behaviours that don't fit with the basic models of microeconomic theory. For example, we'll explore how behavioural economics can be leveraged by the insurance industry. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Main literature: - Zweifel, P., & Eisen, R. (2012). Insurance Economics. Springer. - Handbook of the Economics of Risk and Uncertainty, Volume1; Further readings: - Dionne, G. (Ed.). (2013). Handbook of Insurance (2nd ed.). Springer. References will be given on a topic-by-topic basis during the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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