1.3: Good practices of modelling

Following the modelling cycle is necessary, but it does not by itself make a model good. Whether the work holds up depends on a few habits that are easy to skip under time pressure and that separate rigorous modelling from careless modelling [1][2].

This section covers four of those practices:

1.3.1 · What makes a model “good”

Judging a model by its fitness for purpose, not by whether it is “true” or “complete”.

1.3.1: What makes a model “good”?

1.3.2 · How much detail?

The KISS and KIDS strategies, and how to choose between them.

1.3.2: How much detail? KISS and KIDS

1.3.3 · Common pitfalls

The recurring mistakes that catch even experienced modellers.

1.3.3: Common pitfalls

1.3.4 · Reproducibility

A practical checklist for making results others can recreate.

1.3.4: Reproducibility checklist

Before moving on, preview the working habit behind each of these four areas:

Learning Activity: The four good-practice habits

distinguish the four habits that make modelling rigorous rather than careless.

Four flip cards, one for each good-practice area of Section 1.3, from what makes a model good through to reproducibility.

Click a card to flip it and reveal the working habit behind that area, then flip all four.

These habits are not guaranteed by the modelling cycle. A model can pass through every stage of the cycle and still hide its assumptions, carry needless detail, or resist reproduction. The four subsections that follow turn each habit from an aspiration into a concrete practice.

These four practices are not arbitrary: they are distilled from established simulation methodology, principally Railsback and Grimm on parsimony and analysis [1] and Law on verification, validation, and replication [2]. One further practice, documentation, is deferred rather than covered here: writing a model up in a standard format, using the ODD and ODD+D protocols, is treated in Chapter 5, where empirical and decision-focused modelling is discussed in detail.

Key takeaways

Key takeaways

• A sound modelling cycle still depends on fitness for purpose, appropriate detail, explicit checks, and reproducible evidence.

• Good practice is a set of habits applied throughout the study, not a final check added after the model is built.

• Documentation becomes part of model quality because it lets another reader understand, assess, and rerun the work.

Begin with 1.3.1: What makes a model “good”?, which sets out what it means for a model to be “good” in the first place.

References

[1] (1,2)

Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: A practical introduction (2nd ed.). Princeton University Press.

[2] (1,2)

Law, A. M. (2015). Simulation modeling and analysis (5th ed.). McGraw-Hill Education.