System Dynamics: Understanding Feedback and Simulation Models
System Dynamics: True or False Statements
Assess the following statements regarding system dynamics:
Feedback systems control actions based on actual results (False).
False. Feedback allows control of a system, enabling corrective action based on feedback information. These values are from the immediately preceding time instant.
Level equations perform the mathematical process of integration (True).
True. Levels change their values by integrating flows. A level represents accumulation, integration, or value, depending on the field.
Calculating flow values requires values from the previous time step (False).
False. The value of a flow does not depend on previous values of levels. In a system with external influences, flows can change instantly.
Results in a system dynamics model are not sensitive to the step size (dt) used (False).
False. Step size affects results. The appropriate step size (dt) depends on the integration method.
Auxiliary variables allow disaggregation of a model to understand flow equations (True).
True. Auxiliary variables simplify complex expressions, representing steps in calculating a flow variable from level variables and information channels.
A causal loop diagram clearly shows the system’s state variables (False).
False. Causal loop diagrams represent cause-effect relationships between variables. They do not distinguish between level and rate variables, leading to potential inaccuracies in polarity characterizations.
To understand a standard variable’s behavior, we use a generic system dynamics structure (False).
False. Understanding level behavior requires identifying the real system’s flows and levels and determining the structures they create.
Usefulness, Relevance, and Validity of Simulation Models
The validity of a model is relative; no model perfectly represents the real system. A successful model improves understanding of the represented reality.
Advantages of Simulation Models:
- Explicit Assumptions: Assumptions are documented and open to revision.
- Logical Consequence Calculation: Models accurately calculate the consequences of assumptions.
- Comprehensiveness: They can interrelate many factors simultaneously.
Disadvantages of Simulation Models:
- Poor Documentation: Some models are poorly documented, making assumptions hard to examine (“black boxes”).
- Complexity: Overly complex models can erode user confidence in the assumptions’ consistency.
- Quantification Difficulty: They struggle with relationships and factors that are hard to quantify or rely on expert judgment.
Utility of Simulation Models:
The utility of models lies in simplifying reality, making it understandable.