Frank Schweitzer: Katalogdaten im Herbstsemester 2019
|Name||Herr Prof. Dr. Frank Schweitzer|
Professur für Systemgestaltung
ETH Zürich, WEV G 211
|Telefon||+41 44 632 83 50|
|Fax||+41 44 632 18 80|
|Departement||Management, Technologie und Ökonomie|
|363-0541-00L||Systems Dynamics and Complexity||3 KP||3G||F. Schweitzer|
|Kurzbeschreibung||Finding solutions: what is complexity, problem solving cycle.|
Implementing solutions: project management, critical path method, quality control feedback loop.
Controlling solutions: Vensim software, feedback cycles, control parameters, instabilities, chaos, oscillations and cycles, supply and demand, production functions, investment and consumption
|Lernziel||A successful participant of the course is able to: |
- understand why most real problems are not simple, but require solution methods that go beyond algorithmic and mathematical approaches
- apply the problem solving cycle as a systematic approach to identify problems and their solutions
- calculate project schedules according to the critical path method
- setup and run systems dynamics models by means of the Vensim software
- identify feedback cycles and reasons for unintended systems behavior
- analyse the stability of nonlinear dynamical systems and apply this to macroeconomic dynamics
|Inhalt||Why are problems not simple? Why do some systems behave in an unintended way? How can we model and control their dynamics? The course provides answers to these questions by using a broad range of methods encompassing systems oriented management, classical systems dynamics, nonlinear dynamics and macroeconomic modeling. |
The course is structured along three main tasks:
1. Finding solutions
2. Implementing solutions
3. Controlling solutions
PART 1 introduces complexity as a system immanent property that cannot be simplified. It introduces the problem solving cycle, used in systems oriented management, as an approach to structure problems and to find solutions.
PART 2 discusses selected problems of project management when implementing solutions. Methods for identifying the critical path of subtasks in a project and for calculating the allocation of resources are provided. The role of quality control as an additional feedback loop and the consequences of small changes are discussed.
PART 3, by far the largest part of the course, provides more insight into the dynamics of existing systems. Examples come from biology (population dynamics), management (inventory modeling, technology adoption, production systems) and economics (supply and demand, investment and consumption). For systems dynamics models, the software program VENSIM is used to evaluate the dynamics. For economic models analytical approaches, also used in nonlinear dynamics and control theory, are applied. These together provide a systematic understanding of the role of feedback loops and instabilities in the dynamics of systems. Emphasis is on oscillating phenomena, such as business cycles and other life cycles.
Weekly self-study tasks are used to apply the concepts introduced in the lectures and to come to grips with the software program VENSIM.
Another objective of the self-study tasks is to practice efficient communication of such concepts.
These are provided as home work and two of these will be graded (see "Prerequisites").
|Skript||The lecture slides are provided as handouts - including notes and literature sources - to registered students only. All material is to be found on the Moodle platform. More details during the first lecture|
|Voraussetzungen / Besonderes||The end-of-semester examination will account for 70% of the grade and may be conducted on computers.|
The self-study tasks contribute to the compulsory continuous performance assessment (obligatorisches Leistungselement) and account for 30% to the final grade.
The Leistungselement contains several modules: one obligatory self-study tasks (self-assessment, pass/fail), one group activity (one out of 3 group exercises, 15% of grade), and one individual submission (one out of 6 individual exercises, 15% of grade).
Students will also be required to submit peer feedback about self-study solutions of other students (4 feedback submissions in total).
The 30% Leistungselement is conditional on the pass/fail self-assessment exercise and the four feedback submissions.
|364-1058-00L||Risk Center Seminar Series||0 KP||2S||B. Stojadinovic, D. Basin, A. Bommier, D. N. Bresch, L.‑E. Cederman, P. Cheridito, H. Gersbach, H. R. Heinimann, M. Larsson, G. Sansavini, F. Schweitzer, D. Sornette, B. Sudret, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen|
|Kurzbeschreibung||This 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. Students and other guests are welcome.|
|Lernziel||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 novel mathematical models for open problems, to analyze them with computers, 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.|
|Inhalt||This 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 colloquium. Students and other guests are welcome.|
|Skript||There is no script, but a short protocol of the sessions will be sent to all participants who have participated in a particular session. Transparencies of the presentations may be put on the course webpage.|
|Literatur||Literature will be provided by the speakers in their respective presentations.|
|Voraussetzungen / Besonderes||Participants should have relatively good mathematical skills and some experience of how scientific work is performed.|