Modelling System Dynamics

Course manual 2023/2024

Course content

In the Modelling System Dynamics course, students will learn how to model and analyse the dynamics of large-scale economic, social or technological systems and processes. System dynamics is grounded in the modern theory of nonlinear dynamics and control theory. Students will learn how to describe the structures of complex systems and build simulations of real-world problems. Students will discover the basic concepts of system dynamics: stocks and flows, feedback loops, control strategies, state oscillation and instability, S-shaped growth, overshoot and collapse, path dependency and other nonlinear dynamics. In the course, students will explore different problem domains, build up their skills by practising on small assignments, and finally demonstrate their knowledge and skills in a project, using a system dynamics modelling environment.  

Study materials

Literature

• Sterman, J. (2000), 'Business dynamics: Systems thinking and modeling for a complex world', Boston: Irwin/McGraw-Hill 

Practical training material

• is available on Canvas

Software

• Vensim for system dynamics simulation and Python for model calibration and sensitivity analysis 

Objectives

• After a successful completion of the course the students can: explain the added value of modelling to science and society
• describe the properties of several classes of modelling approaches and advantages of systems dynamics approach
• identify the structure of a system, describe the stocks and flows, and suggest feedback loops
• explain the features of nonlinear dynamics, such as state oscillation and instability, S-shaped growth, overshoot and collapse, path dependency, tipping points, and hysteresis
• implement the models in a system dynamics software and analyze the dynamics of the process
• formulate ordinary differential equations (ODEs) behind the system dynamics model
• analyze and solve low dimension ODEs analytically and numerically;
• explain and analyze how discretization and numerical algorithms affect the accuracy of simulation results
• formulate models of complex economic, social, or technological systems
• calibrate model parameters and validate the model against experimental data
• perform model sensitivity analysis
• explain how systems dynamics modelling can be used in decision making and business optimization
• discuss the system traps and solutions for controlling complex dynamical systems

Teaching methods

• Lecture
• Self-study
• Seminar
• Working independently on e.g. a project or thesis

Learning activities

Attendance

In TER part B of this programme no requirements regarding attendance are mentioned.

Assessment

Students are assessed based on their submitted assignment reports and a team project.

Since these assignments address different topics within the course, partial grades do not compensate each other. All partial grades should be at least 5.5 to pass the course. 

Only one of the three reports may be re-submitted in case of an insufficient grade. Re-submissions are accepted after the end of the course via Canvas. To ensure fairness, a maximum grade of 6.0 is applied to resubmitted reports. 

The consequence of not meeting a report deadline is a 1 grade point penalty per day after the report deadline. A maximum of 4 days delay is allowed, due to the grading logistics and the need to provide timely feedback. 

Inspection of assessed work

All reports are submitted via Canvas. Partial and final grades and feedback are also provided via Canvas. Students may ask for further explanations of the grades and feedback during the practical sessions. 

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Additional information

Required prior knowledge and skills:

• Programming in Python; 
• Some mathematical skills, such as basic calculus, basic statistics (e.g. distribution, mean, variance), exponential and logarithmic functions, differentiation and integration. 

Contact information

Coordinator

• dr. Debraj Roy