Simulation and Modelling – GCSE Computer Science Edexcel Revision

Introduction to Simulation and Modelling

Simulation is a method used to imitate a real-world system or process over time.
Modelling is the creation of an abstract representation of a system, often in the form of mathematical equations or computational models.
Simulation modelling is a combination of these two methods, where a model is used to conduct a simulation.
This can be useful to predict outcomes and discover new insights.

These techniques have applications in a wide range of areas, such as physics, engineering, economics, medicine, and computer science.
They can be used to simulate real-world scenarios, such as the spread of a disease or the behaviour of a financial market.
This can help to analyse and understand complex systems, and to test hypothetical scenarios without the risks associated with real-world experimentation.

Deterministic simulations provide the same output each time they are run, assuming no changes have been made to the model or initial conditions.
Probabilistic simulations incorporate elements of chance or randomness into the model, producing different results each time the simulation is run.
Static simulations model systems at a specific point in time, while dynamic simulations represent systems that change over time.
Discrete simulations model systems that change at distinct points in time, and continuous simulations represent systems that are changing continuously.

Models can be represented in various forms such as mathematical equations, diagrams or computer programs.
Agent-based modelling involves the creation of individual entities, called agents, each operating independently according to pre-defined rules.
System dynamics modelling is often used for modelling complex systems, using feedback loops and time delays to represent the behaviour of the system over time.

By using simulation and modelling, you can gain a better understanding of systems and test scenarios without the risks and costs associated with real-world experimentation.
However, the validity of the simulation is dependent on how well the model represents the real system.
Models are often a simplification of reality, and may not include all factors, leading to the emergence of unexpected behaviours.
The quality of inputs, or data used in the model, also influences the reliability of the simulation results.

In computing, simulation and modelling can be used for a variety of purposes, such as forecasting trends, optimising systems, testing software, and more.
For example, traffic simulation can help in planning road networks, or the spread of a computer virus can be modelled to develop better security systems.
It’s an essential tool that brings together computer science with other fields to solve complex problems and make informed decisions.