The first milestone in developing an IBM is a first version of the model that, without too much fine-tuning of parameters, largely behaves like the real system, for example, a population which largely has the right size and spatial distribution. The question then is, how can we find the best representation of a certain process, for example, habitat selection, mate choice, or life history? The answer is to follow the scientific method of strong inference and formulate alternative models, or theories, of the process and then to check how good the entire IBM, including one of the alternative theories, is capable of reproducing a set of observed patterns. In this way, model development becomes less ad hoc, more rigorous, and it becomes easier to communicate why a certain model formulation has been chosen.
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