Dynamic Complexity and Decision Experiments

The course provides theory of dynamic complexity and other influence factors in dynamic decision making. It introduces methods for the design, implementation, and analysis of laboratory experiments. Previous experiments in the field are replicated. Simulator construction, goal and task structure, experimental design, optimisation to establish benchmarks, statistics for hypothesis testing and internal and external validity of experiments are covered. The students design and carry out their own pilot laboratory experiments with focus on (1) purpose, (2) hypotheses, (3) design, and (4) analysis. The experiments can address both behavioural theories and learning interventions. The grading considers documentation of the vital design steps, analysis, and presentation.

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.

On completion of the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student

• knows how to design, program, carry out, analyse, and report results from laboratory experiments of dynamic systems.
• knows about methods to construct benchmarks.
• knows the most important theories of bounded rationality and in particular theories of misperceptions and learning difficulties in dynamic systems.

Skills

The student

• applies their knowledge and develops their skills by designing, carrying out and reporting on an experiment.
• is able to test theories of bounded rationality and in particular theories of misperceptions and learning difficulties in dynamic systems.

General competence

The student

• distinguishes different purposes of experiments such as testing theories of decision-making and investigating effectiveness of learning environments.
• understands and is able to judge the quality of research based on both laboratory and field experiments.