Objectives and Content
The course provides theory and methods for the design, programming, and analysis of laboratory experiments. The course includes introductions to simulator construction, interactive learning environments, optimisation to establish benchmarks, and to statistics for hypothesis testing. The students should design and carry out their own pilot laboratory experiments with focus on (1) purpose, (2) hypotheses, (3) design, and (4) analysis. The experiments could address both behavioural theories and learning interventions. The grading considers documentation of the vital design steps, analysis, and presentation.
Teaching Methods and Extent of Organized Teaching
Lectures and term paper advisory sessions
Early March- Mid April (lectures first 3 weeks)
9 hours (lectures), 6 hours (assistance on experiments)
3 weeks (lectures), 3 weeks (term papers)
42 hours plus work on own
Compulsory Assignments and Attendance
Semester of Instruction
Level of Study
Access to the Course
Open. Open also for Bachelor students, Erasmus Mundus students, and visiting students.
Express knowledge and understanding
Students know how to design, program, carry out, analyse, and report results from laboratory experiments of dynamic systems. They know about methods to construct benchmarks. Students know how to test theories of bounded rationality and in particular theories of misperceptions and learning difficulties in dynamic systems.
Apply knowledge and understanding
Students apply their knowledge and develop their skills by designing, carrying out and reporting on an experiment.
Students distinguish different purposes of experiments such as testing theories of decision-making and investigating effectiveness of learning environments. They understand and are able to judge the quality of research based on both laboratory and field experiments.
Students know the standard presentation form for articles based on experiments. They apply this knowledge when writing a short article about and presenting orally the experiment they have carried out.
Develop learning skills
The course serves as a complement to earlier courses in System Dynamics. Through the design and analysis of experiments students get a deeper insight into decision-making. This makes them better modellers. They also develop their inclination to ask questions about and their ability to distinguish significant and insignificant assumptions in models. Students become better at asking critical questions about models and they reduce their bias towards seeking confirming evidence.
Required Previous Knowledge
Recommended Previous Knowledge
GEO-SD302, GEO-SD 201, or corresponding background in modelling
Forms of Assessment
Term paper and presentation graded (100%)
GEO-SD306 will be evaluated minimum every third time it´s taught.
Department of Geography