Welcome to System Dynamics for Modeling Complex Systems 2025

Description: Systems are getting increasingly complex every day and their interdependency is considerably growing. Therefore, effective modeling approaches are required to improve our understanding of their behavior and investigate the long-term consequences of our decisions. System dynamics (SD) is a powerful modeling tool to understand and analyze the dynamic behavior of complex systems over time. Using SD, the interactions and dependencies within a complex system can be captured to simulate the long-term behavior of the system and analyze the impact of different strategies and decisions. SD incorporates key concepts including positive/negative feedback loops, stocks and flows, delays, and non-linearities to predict system behavior over time using cause and effect analysis, thus providing an effective tool for policymaking. SD is used in many applications and real-world scenarios, particularly economics, healthcare, engineering, ecology, as well as business and management among others. SD is an effective tool for enhancing our system thinking and can improve our everyday decision-making by increasing our awareness about the long-term impacts of our choices.

Prerequisites: Familiarity with basic mathematics, statistics, data analysis, and software skills.

Learning objectives: To enhance system thinking and problem-solving skills.  – To equip students with the knowledge and skills to understand, model, and analyze the structure and behavior of complex systems.  – To understand and identify positive/negative feedback loops in complex systems structures.  – To investigate the long-term impact of strategic decisions on the system behavior.

Teaching methods: Lectures  – Self-study – Group work

Criteria for assessment: Active participation in the course   – Reports and exercises to be prepared in groups and delivered two weeks after closing the course.

Key literature:

Books: Business Dynamics: Systems thinking and modeling for a complex world, John D. Sterman, McGraw-Hill,2000

Papers:

1. Blumberga A, Lauka D, Barisa A, Blumberga D. Modelling the Baltic power system till 2050. Energy Conversion and management. 2016 Jan 1;107:67-75.

2. Mutingi, M., Mbohwa, C. and Kommula, V.P., 2017. System dynamics approaches to energy policy modelling and simulation. Energy Procedia, 141, pp.532-539.

3. Akhwanzada, S.A. and Tahar, R.M., 2012. Strategic forecasting of electricity demand using system dynamics approach. International Journal of Environmental Science and Development, 3(4), p.328.

4. Pereira, A.J. and Saraiva, J.T., 2013. A long term generation expansion planning model using system dynamics?Case study using data from the Portuguese/Spanish generation system. Electric Power Systems Research, 97, pp.41-50

5. Gravelsins A, Bazbauers G, Blumberga A, Blumberga D, Bolwig S, Klitkou A, Lund PD. Modelling energy production flexibility: system dynamics approach. Energy Procedia. 2018 Aug 1;147:503-9.

6. Esmaieli M, Ahmadian M. The effect of research and development incentive on wind power investment, a system dynamics approach. Renewable energy. 2018 Oct 1;126:765-73.

7. Kunsch, P.L., 2008. Simulation of micro-CHP diffusion by means of system dynamics. Energy Policy, 36(7), pp.2308-2319.

Organizer: Najmeh Bazmohammadi

Lecturers: Najmeh Bazmohammadi

ECTS: 2.0

Time: 20 – 21 October 2025

Place: Aalborg University

Zip code: 9220

City: Aalborg

Maximal number of participants: 20

Deadline: 29 September 2025

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