Complex data sets, simplification

One of the possibilities is to study experimentally the coupled system as a whole, at a time when all the reactions concerned are taking place. On the basis of the data obtained it is possible to solve the system of differential equations (1) simultaneously and to determine numerical values of all the parameters unknown (constants). This approach can be refined in that the equations for the stoichiometrically simple reactions can be specified in view of the presumed mechanism and the elementary steps so that one obtains a very complex set of different reaction paths with many unidentifiable intermediates. A number of procedures have been suggested to solve such complicated systems. Some of them start from the assumption of steady-state rates of the individual steps and they were worked out also for stoichiometrically not simple reactions [see, e.g. (8, 9, 5a)]. A concise treatment of the properties of the systems of consecutive processes has been written by Noyes (10). The simplification of the treatment of some complex systems can be achieved by using isotopically labeled compounds (8, 11, 12, 12a, 12b). Even very complicated systems which involve non-... 

Ultimately a model is a simplification of real-world behaviour. To be useful, it must produce predictions of sufficient accuracy in a reasonable time span if it is quicker to do the experiments than to develop and run the model, then the model is worthless (except as an academic exercise). In choosing an approach for modelling a system, a compromise must be made between complexity and simplicity. A very complex model, which includes a detailed description of the many physico-chemical processes involved, may give an accurate prediction over a wide range of conditions. However, there will be many parameters to set from experimental data, and so development and validation will be time consuming. An over complex model may also have a protracted run-time. However, a very simple model may be quick to develop and run, but if the predictions are far from reality, this model is also useless. 

With a common set of data, a computer system can simultaneously produce a wide variety of specialized displays (e.g., flowcharts, tables, matrices, graphs, maps). With such a graduated series of displays, which trade off depth of explanation for simplification, almost any decision-maker can locate a display form which suits his interpretative abilities and through which an understanding and belief can be built in more or less complex forms of assessment (Fig. 7). 

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