As we have seen in the previous chapters, the dependability of a given model is a rather relative measure. At best it depends on intersubjective agreements, at worst it must be based on entirely personal estimations if normative criteria are not at hand. At the same time modelling and the quality of the imaging process leading to the model are influenced by the kernel of the mapping — this is all the information getting lost when the real system under consideration is mapped into the model. Any statement about the dependability of a model is probabilistic and thus affected by uncertainties. To some degree the loss of information due to mapping as well as the stochastic process of the evaluation of a model are founded on the evidence available to the modeller and to the relevant scientific community. We thus have to grant a certain tolerance to the performance of a model when we compare it with its counter — image (cf. Subsect. 2.4.3), and we will even try to characterize it by degrees itself.
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Dr. Tibor Müller
Dr. Harmund Müller
- Springer Berlin Heidelberg
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