How do those building and using models decide whether a model should be trusted? While my thinking has evolved through modelling to predict the impacts of land use on losses of nutrients to the environment – such models are central to land use policy development – this under-discussed question applies to any model.
In principle, model development is a straightforward series of steps:
- Specification: what will be included in the model is determined conceptually and/or quantitatively by peers, experts and/or stakeholders and the underlying equations are decided
- Coding: the concepts and equations are translated into computer code and the code is tested using appropriate software development processes
- Parameterisation: here the values that go into the equations are determined by a variety of methods
- Testing: the model is compared against data using any of a wide range of metrics, the comparisons are examined and the fitness of the model for the intended purpose or scope is decided. Bennett and colleagues (2013) give an excellent position on the variety of statistical approaches that can be used for this purpose.
In reality, of course, these steps do not take place in an orderly progression, there are many loops backward, some of the parameterisation and testing occurs in parallel to the coding and the first step is often re-visited many times.
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