Updated: Jun 9
System dynamics (SD) according to the System Dynamics Society is a computer aided approach to policy analysis and design. It applies to dynamic problems arising in complex social, managerial, or ecological systems – literally any dynamic systems characterized by interdependence, mutual interaction, information feedback, and circular causality.
The term "System Dynamics" was coined by Jay Forester at MIT in 1961. The aim was to explore dynamic responses to changes made either within or outside of a system to explain the past and predict the future. This makes System Dynamics useful for better understanding and improving sociology-technical problems in the domain of quality, safety, environmental, and regulatory programs and systems.
When trying to understand systems we often start by taking a snapshot of the situation which creates a static and linear causality representation of reality. This is perhaps, a first order approximation which may provide useful initial insights. However, to more fully understand the past and predict the future a dynamic model is needed that represents the interdependence of system components. This is where causal loops are used.
Causal loop diagrams (CLDs) were introduced by Jay Forester (1961) and developed further since. The purpose of a CLD's is to map out the structure and influences to system behavior.
In theory, there are two kinds of causal loops: reinforcing or balancing. Negative reinforcing causal loops are called vicious cycles and have unfavourable outcomes. Positive reinforcing causal loops ware called virtuous cycles and have favourable results. Balancing loops keep the system at equilibrium.
At a high-level managed quality, safety, environmental, and regulatory systems are designed to maintain consistency. The audit / fix cycle forms a negative feedback loop that uses corrective actions to adjust the system output back within control limits. This forms a balancing causal loop.
However, the effect of these adjustments can destabilize a system when capabilities to restore equilibrium are inadequate.
This is amplified when a system must achieve new levels of performance outside of its current capabilities. It is here that SD becomes an important tool to help policy makers better improve outcomes of their compliance programs. SD can help to evaluate policy changes made as part of performance-based obligations to ensure that underlying systems have the capabilities, capacity, and competencies to achieve and sustain new levels of performance.
This assists the function of the program level of a managed system to:
Introduce change by means of continuous improvement without destabilizing the underlying system
Adjust system capabilities to meet increasing performance demand
Evaluate and adjust outcomes to optimize overall system effectiveness