Modelling Environmental Systems
At the end of the course, students will have developed the following knowledge, skills and competences:
- Know the principles and systemic approaches for modelling of environmental systems;
- Understand the fundamentals of systems thinking and system dynamics modelling;
- Know how to define a problem in dynamic terms;
- Capacity to develop dynamic hypotheses and build conceptual models for environmental problems;
- Capacity to use system dynamics software and computer-based tools for the analysis of environmental systems;
- Know seminal models for the analysis of typical behavior patterns in environmental systems;
- Be able to formulate model-based environmental policies and evaluation simulation scenarios;
- Understand the efficacy of different strategies for intervening in environmental systems;
- Capacity to analyze data and define modelling strategies for the analysis of environmental problems in a context of complexity and uncertainty;
Capacity to communicate model-based insights effectively.
Nuno Miguel Ribeiro Videira Costa
Weekly - 4
Total - Available soon
Reading list includes selections from:
Bossel, H. (2007). Systems Zoo 2. Simulation models – climate, ecosystems, resources. Books on Demand.
Ford, A. (2010). Modeling the Environment. 2nd Edition. Island Press.
Sterman, J. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill.
Meadows, D.H. (2008). Thinking in systems: a primer. Wright, D. (Ed.). Chelsea Green Publishing.
Voinov, A. (2008). Systems science and modelling for Ecological Economics. Academic Press.
Several academic journal articles.
Thinking in systems: perspective and fundamental concepts
Defining dynamic problems: behaviour-over-time graphs; behaviour modes in socio-ecological systems
Model conceptualization tools I: causal loop diagrams and system archetypes
Model conceptualization tools II: stock-and-flow diagrams, introduction to system dynamics modelling software
Dynamics of environmental systems: first-order systems (equilibrium, growth and goal-seeking dynamics), second-order systems (oscillations and overshoot dynamics), co-flows and aging chains
Model verification and validation: structure and behaviour tests, sensitivity analysis, model documentation
Policy design and evaluation: formulating policies and model-based simulation scenarios
Policy levers: places to intervene in systems
Dynamic modelling based on verbal descriptions, agent-based models and machine learning models
Communicating model insights and interface development
Programs where the course is taught: