Chapter 1 - Modelling and simulation#
Video overview
This short video previews the four sections of Chapter 1.
Before you can build an agent-based model, you need to know what a model actually is, how a good one is built, and how to tell a trustworthy model from a misleading one. This chapter answers those questions, giving you the shared vocabulary and the disciplined workflow that every later chapter relies on.
Learning objectives
By the end of this chapter, you will be able to:
Explain why modelling and simulation are needed, and why simulation is useful for complex systems that are hard to solve with equations (Section 1.1).
Distinguish the core elements of a model (entities, relationships, assumptions) and the main types of model (conceptual, mathematical, computational) (Section 1.1).
Use the modelling cycle to build, verify, validate, and use a simulation model, and explain why you may need to go back to earlier steps (Section 1.2).
Judge model quality using good practices such as transparency, the right level of detail, and reproducibility (documented assumptions, parameters, seeds, and enough replications), and spot when these are missing in a model description (Section 1.3).
Choose and justify a suitable modelling tool for a problem, including the trade-offs in suitability, flexibility, and reproducibility (Section 1.4).
This chapter lays the groundwork for everything that follows, and its four sections build on one another in a deliberate order: first what a model is (1.1), then how the modelling cycle turns a question into a working simulation (1.2), then how well that model performs and what makes it good (1.3), and finally with what tool you should build it (1.4). Work through them in sequence.
The four cards below link to each section:
What a model is, the purposes models serve, the elements they share, and when modelling is the right tool.
The stages that turn a real-world problem into a working simulation, from conceptual model to validation and use.
What makes a model good: the right level of detail, avoiding common pitfalls, and reproducibility.
How to choose a suitable platform, comparing ABM tools with other paradigms against the needs of the problem.
Begin with Section 1.1, What is modelling?.