In previous study you explored the implications of a range of negative and positive feedback processes that operate in concert to either undermine or sustain various interlinked social, economic and ecological subsystems. However, these implications are drawn from your personal ‘mental’ models, and previous resources have highlighted how these are severely limited by our information processing and storage capacities. Computer-based predictions of the behaviour of complex systems are now at the centre of many decision-making processes. Intuition is an important and under-rated way of thinking about complexity, but it can be misleading when trying to understand or predict the dynamic behaviours of complex systems. So, while developing verbal and/or visual models (such as the diagrams developed in earlier activities) to predict complex system behaviour can communicate useful insights, we can’t rely solely on them. Many systems practitioners see the identification of system purpose, boundaries, the clarification of qualitative relationships between system components, and the isolation of feedback loops, as only the first steps in a comprehensive systems approach. Comprehensive investigations can therefore include the development of dynamic mathematical models of the system under scrutiny.
In this course, you will explore several system dynamics models to understand the behaviour of dynamic, non-linear complex systems as outlined in Readings 5.1 and 5.2. The initial activities within this section will build on your understanding of the concepts presented in the previous section’s readings, particularly positive and negative feedback. The phenomena described in Readings 5.1 and 5.2 emerge out of the behaviour of positive and negative feedback interactions. The complex behaviour of many systems may seem initially impossible to interpret rationally. Yet, it is often possible to gain insights into these systems by isolating a limited number of feedback relationships which combine to create seemingly unpredictable behaviours over time.
The final resource, Reading 5.3, makes a case for the use of computer-based models when exploring complex systems.