How are variables selected for a system model?

Variables for a system model are selected based on their relevance, measurability, and impact on the system's behaviour and performance.

In more detail, the process of selecting variables for a system model is a crucial step in system analysis and design. It involves identifying the key elements that significantly influence the system's behaviour and performance. These elements, or variables, can be anything from input data, system parameters, to output results.

The first criterion for selecting variables is their relevance to the system. This means that the variables should have a direct or indirect influence on the system's behaviour or performance. For example, in a weather forecasting system, variables like temperature, humidity, wind speed, and atmospheric pressure are relevant because they directly affect the weather conditions.

The second criterion is the measurability of the variables. It is important to select variables that can be quantified or measured in some way. This is because the system model often requires numerical data to perform calculations and generate results. For example, in a financial system, variables like income, expenditure, and savings are measurable and can be expressed in monetary terms.

The third criterion is the impact of the variables on the system. This refers to the degree to which changes in the variables affect the system's behaviour or performance. Variables with a high impact are often prioritised over those with a low impact. For example, in a traffic management system, variables like vehicle speed and traffic volume have a high impact on traffic flow and congestion, and are therefore important to include in the system model.

In addition to these criteria, the selection of variables may also be influenced by other factors such as the availability of data, the complexity of the system, and the objectives of the system modelling. For instance, if the aim is to develop a simple model for educational purposes, it may be appropriate to select only a few key variables. On the other hand, if the aim is to develop a detailed and accurate model for professional use, it may be necessary to include a larger number of variables.