Introduction - Systems Modelling (UML)

Systems modelling is a process of creating simplified representations or models of complex systems in order to understand their behaviour, analyse their dynamics, and make predictions or decisions based on those models. It involves the use of various mathematical and conceptual tools to describe and simulate the interactions and relationships among different components or elements within a system.

Systems modelling can be applied to a wide range of disciplines, including engineering, computer science, economics, ecology, and social sciences. It helps in studying and analysing systems that are too complex or costly to study directly, providing insights into system behaviour, potential improvements, and potential risks.

Here are some key aspects and techniques commonly used in systems modelling:

  1. Conceptualisation: Understanding and defining the boundaries, components, and interactions of the system being modelled. This involves identifying the relevant variables, parameters, and constraints.

  2. Mathematical Modelling: Developing mathematical equations or algorithms to represent the behaviour of the system. These models can be deterministic (based on precise relationships) or probabilistic (incorporating uncertainty).

  3. Simulation: Implementing the mathematical model in a computational environment to observe and analyse the behaviour of the system over time. Simulation allows for experimentation, testing different scenarios, and predicting system responses.

  4. Optimisation: Using the models to identify optimal or near-optimal solutions based on specific criteria. Optimisation techniques help in finding the best set of inputs or actions to achieve desired outcomes.

  5. Validation and Verification: Checking the accuracy and reliability of the models by comparing their predictions or results with real-world data or observations. This step helps ensure that the models adequately represent the actual system.

  6. Sensitivity Analysis: Assessing the impact of changes in model inputs or parameters on the system outputs. Sensitivity analysis helps in understanding the relative importance of different factors and their influence on the overall system behaviour.

Systems modelling provides a structured approach to studying complex systems, enabling analysis, prediction, and decision-making based on a simplified representation of reality. It helps in gaining insights into system behaviour, evaluating different strategies or interventions, and improving the understanding of complex phenomena.