Level of Study
Objectives and Content
This course teaches the basics of the System Dynamics method. System Dynamics helps explain how change takes place, why people misunderstand change, and why so many policies fail to solve problems. The method builds on a systems perspective where things influence each other and where knowledge from different fields of study may be needed. Students learn to recognize typical problem behaviours of dynamic systems, exemplified by global warming, over-utilization of natural resources, unemployment, epidemics, price fluctuations, all problems of importance for the sustainable development goals. Students learn to formulate hypotheses for why problems develop, and they learn to represent their hypotheses in simulation models and use the models to test their hypotheses. For models that give likely explanations of problem developments, students learn to formulate and test alternative policies in the very same models. At a more general level, the course gives training in applying the scientific method to socio-economic problems, it provides a common language for interdisciplinary research, and it gives training in project formulations and reporting.
Express knowledge and understanding
Students gain knowledge about the System Dynamics method and its relation to standard science, operations research, and public and private management. They also get to know the basics of dynamics systems (systems consisting of instantaneous and accumulating cause and effect relationships, feedback loops, nonlinearities, and delays) and the use of causal loop diagrams, stock and flow diagrams, graph functions, and equations to represent and illustrate cause-and-effect relationships. And, they obtain knowledge about different ways to analyze and understand development over time (graphical integration, structure diagrams, and simulation) as well as about misperceptions and simplified rules of thumb that people use to manage complex dynamic systems.
Apply knowledge and understanding
To practice the new knowledge is very important and the course offers several types of training. The reading material is presented in a MOOC where students are challenged while reading, in quizzes, and in interactive learning environments. Class sessions make use of the flipped classroom format where students answer questions, discuss with each other and engage with the instructor. There are four mandatory assignments, with guidance by the student assistant before the deadlines and debriefings after the deadlines. Students also learn to apply analogies to understand several important social challenges, where the most familiar situations serve as analogies for less transparent problems.
Students develop systems thinking skills and an intuitive understanding of the scientific method. This changes the perspective the students have on how problems develop and on how problems can be reduced. It also makes the students more deeply interested in analysis, and a high fraction of the master students end up in academia, in consulting, or in large organizations that set aside time for formal analysis.
The diagramming techniques that the students learn can be seen as tools for effective communication at an intermediate level between imprecise narratives and complex mathematical equations. Students learn a short recipe, P'HAPI, for project design and for effective reporting from projects. Students practice their skills both in classroom discussions and in assignments.
Develop learning skills
After finishing the course, students typically have a new and different view on how dynamic social systems work. This encourages them to ask new questions such as: what are the important stocks and feedback loops, is behaviour created endogenously or is it caused by external influences, do data represent causal relationships or correlations, will the system counteract proposed policies etc. Once these questions are asked, they motivate learning.
Required Previous Knowledge
None. Prospective students should check that they understand what a mathematical function is and how it can be exposed in a graph. Students do not need knowledge of complex mathematical methods of analysis.
Credit Reduction due to Course Overlap
Access to the Course
The course is open to all students at the graduate and the undergraduate level at the University of Bergen.
Teaching and learning methods
The course material is readily available online in a MOOC. The MOOC contains reading material, videos, quizzes, interactive learning environments, downloadable models and additional reading material. The course is of the flipped classroom type where students prepare before classes, and where classes are used to answer and discuss questions related to the pre-class preparations. This is a social process where students interact intensely with each other and the instructor, and where this activity leads to deep learning. There are 36 class hours (four two-hour classes per week) and 32 hours of lab assistance (two four-hour labs per week) over a 4-week period from mid-August to mid-September.
Compulsory Assignments and Attendance
4 written assignments must be accepted to sit for the exam
Update fall 2020; no written assignments
Forms of Assessment
4-hour exam (100% of grade). The exam involves building models in Stella and analysing the model behaviour.
Fall 2020: The grading is based on points obtained on the quizzes in the course (25%) and on the final exam where you build and analyze models (75%).
An ECTS grade is provided to the student at the end of the course according to the A-F scale.
For assessment spring 2021: Students can only hand in the final part of the exam (build and analyze models) in the spring semester. The quiz must be completed fall 2020 and will be included in the final grade.
All courses are evaluated according to UiB's system for quality assurance of education.
Type of assessment: Alternative assessments
- Withdrawal deadline
Exam part: Take home exam
- Submission deadline
- 18.09.2020, 14:00
- Examination system
- Digital exam
Exam part: Quizzes