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In other words, the model may produce nice results for some parameter combinations but reporting this is not enough; modelling papers need to report the circumstances under which the model works and the circumstances under which it stops making sense - where it breaks down. In other words, when it comes to the comparison of relevant model outputs with data, finding one model that generates data consistent with what is observed does not mean that it is the unique causal model. So, in order to comprehensively explore the space of possible outcomes, then, the model needs to be run a large number of times to fully capture the effects of the model parameters as well as stochasticity (another good reason for efficient model code; see Rule 6). A good and often cited example of this kind of model is Ptolemy's model of planetary motion [[7]], which described the movements of the planets, and could be used to predict the behaviour of the solar system. [Extracted from the article]