# Simulation and Modelling

## 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.

## Uses of Simulation and Modelling

- 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.

## Types of Simulations

**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.

## Modelling Techniques

- 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.

## Advantages and Limitations

- 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.

## Role in Computing

- 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.