Examples of Preterition and Abundance in Modelling

In addition to the earlier posting Stachowiak on Preterition and Abundance in Modelling here are some examples of Preterition and Abundance (also see here for all postings on Stachowiak):

Stachowiak Abundance Preterition Black WhiteIs this a black and white picture? Is this a colour image of a black/ white arrangement or a black/ white image of a coloured arrangement?  In the latter case, even if you want not to express the colour of the Original at all, you have to choose some colour for the image (here, the scale from black to white).

Stachowiak Abduction Use CaseWho triggers the Use Case? In UML the association between actor and use case is not allowed to have a direction. Thus, in order to express that an actor triggers the use case it are sometimes notated on the left hand side. So, the diagram could say that the customer triggers the use case or not. We cannot say, without any further information.

Stachowiak Maza LabyrinthWhat does the graph tell about the maze? The graph inside the maze preserves coordinates and path length. Also in the graph on the right nodes have coordinates and edges have lengths. However, they are not meaningful anymore. They were ‘sacrificed’ for the sake of a certain view.*

Notice that a lot of further questions apply in the graph on the right: does the top element represent the starting point? Is 1-2-4-6-8 a kind of primary path (typical issue in process models)? Is it better to have all edges of equal length, to indicate the abundance?

So far, just a few examples that came to my mind.

*Stachowiak gives a similar example in his book “Allgemeine Modelltheorie”. The book at Google books.

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Modeling Theory and Abstraction Awareness in strive for scientific rigour and relevance to information systems engineering.
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5 Responses to Examples of Preterition and Abundance in Modelling

  1. TY says:

    Cool! Nice post with excellent examples of model:-)
    Continue the previous topic. Almost any model, of carouse, has somewhat Preterition or Abundance, but these are not the sufficient or the essential issues. It needs more exact and specific description about how or what a model is modeled its target (i.e., the Original by Stachowiak), just like the description of the examples in the post, that is, what I called “modeling knowledge” in the model-driven mechanism. In addition, such the image of chess is a good instance to show that why I called that “knowledge” but not (just) “metamodels”.

  2. in case of the chess image, wouldn’t a ‘metamodel’ have either the complete colour spectrum or just the black to white spectrum? In this case the ‘knowledhe’ would be part of the ‘metamodel’.

  3. TY says:

    In fact I have been hesitant to the use of ‘knowledge’ in MDM. I see the knowledge involved in exactly and adequately access to operate on a model is indeed much than that in intuition, and its boundary often appears blurred. So, I tend to use ‘metamodel’ in more strictly way than ‘knowledge’, though it seems more likely to be accepted (in computer field). I think knowledge is a more general concept than metamodels.

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