Modelling System Dynamics

6 EC

Semester 1, period 2

5294MOSD6Y

Owner Master Information Studies
Coordinator dr. Valeria Krzhizhanovskaya
Part of Master Information Studies, track Information Systems, year 1Master Information Studies, track Data Science, year 1

Course manual 2019/2020

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;
  • formulate models of complex economic, social or technological systems;
  • identify the structure of the 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;
  • implement the models in a system dynamics software and analyse the process dynamics;
  • formulate ordinary differential equations (ODEs) behind the system dynamics model, analyse and solve ODEs analytically and numerically;
  • explain and analyse how discretisation and numerical algorithms affect the accuracy of simulation results;
  • calibrate model parameters and validate the model against experimental data;
  • perform model sensitivity analysis;
  • explain how system dynamics modelling can be used in decision making and business optimization;
  • discuss the strategies for controlling complex dynamical systems.

Teaching methods

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

Learning activities

  • Weekly lectures on the theoretical and practical aspects of System Dynamics (4 hours per week); 
  • Weekly seminars linking the topics of the week to the practical implementations and studies (4 hours per week); 
  • Self-study and independent work on a project (12 hours per week). 

Students will work on the themes addressed in this course individually and in groups. In the first weeks students build up their practical skills individually; and in the last weeks students work in small teams, studying a complex system and integrating the results in a group project.

Learning from each other and benefiting from the wide variety of backgrounds and experiences is stimulating the learning process. During the seminars and working group sessions, you will receive feedback on your individual work from your teaching assistants and from fellow students, and you will give feedback to their work.

Attendance

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

Assessment

Item and weight Details

Final grade

The assessment is based on 3 reports:

  • Assignment 1 (30%)
  • Assignment 2 (30%)
  • Team project (40%)

Since the three assessments 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 deadline for the partial grade. 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 seminars. 

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

A detailed course structure, materials and deadlines are provided via Canvas.

Timetable

The schedule for this course is published on DataNose.

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. Valeria Krzhizhanovskaya