Experimental Methods in Social Systems

Level of Study

Master

Teaching semester

Irregular

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.

Learning Outcomes

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.

Make judgements

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.

Communicate

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

None

Recommended Previous Knowledge

GEO-SD302, GEO-SD 201, or corresponding background in modelling

Access to the Course

Open. Open also for Bachelor students, Erasmus Mundus students, and visiting students.

Teaching and learning methods

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

None

Forms of Assessment

Term paper and presentation graded (100%)

Grading Scale

Grading A-F

Assessment Semester

Spring

Course Evaluation

All courses are evaluated according to UiB's system for quality assurance of education.