This section presents a method whereby a model might be developed with the
property of generality as discussed. The method draws on the model for generalising models as presented in figure 2.3 (Section 6.8). The basic notion is that models can, and need to, be developed in two directions, refered to as horizontally and vertically.
In this diagram, jobs (scenarios) are represented as sets of attributes, within a universe of all possible attributes. Similarly, models are mapped onto this universe as the sets of job attributes for which they represent behaviour. Different scenarios will share some attributes, and so will intersect. The horizontal development of a model refers to its modification to apply to a novel domain whose set of attributes only partially intersects with those of the model. The set of attributes addressed by the model is thus extended to become (theoretically) the union of both sets. In this way the scope of the model is extended - its set of attributes is now likely to intersect (more fully) with a greater number of domains.
Vertical development of the model refers to its refinement, within its scope. The term
vertical arises from considering the series of sets of attributes (i.e. scenarios) addressed by the model as layered on top of each other, at 90° to the plane of the universe in the diagram. Vertical development, concerned with enhancing the robustness and detail of a model, is thus based on those aspects of a particular job which are repeated between different occasions. For clarity, vertical development further subdivides into three aspects, depending on the way the data affect the model, refered to as Debugging, Consolidating and Unpacking. The following sections briefly outline how each of the four aspects of model development are supported by data.
8.1. Horizontal
Behavioural phenomena new to the model, probably associated with any novel job attributes of the particular instance would be relevant here. It is of course possible that the horizontal extension of the scope of the model does not require any changes. Since it is important to practice vertical development within the intended scope of the model, and this scope may well change as a result of horizontal development, it seems only pragmatic to advocate doing the horizontal part of any development cycle before the corresponding vertical development.
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8.2. Vertical - Debugging.
Debugging addresses the question of whether there are any behavioural data contrary to that predicted by the model. Behavioural data not falling into this category must be at least consolidatory (see below). This is really a question of the internal
consistency of the model, the importance of which is recognised by Chapanis (1988). It is possible (even probable) that during development of the model, a job (i.e. a set of attributes) will be found for which the model does not produce the same
phenomena as the person being modelled. In such a case, the model is obviously not fulfilling its objective. It is important to note that this must be with respect to its intended scope and thus a new task for which the model fails must fall within this scope before it is proper to make any modifications to the model.
8.3. Vertical - Consolidating.
The credibility of the model increases with each successful application to a new task. This is the sort of science which is logically contrary to that advocated by strict Popperianists. It could be said that consolidation is really what is left after
debugging, and might therefore be considered redundant. However, it is thought that such positive support for the model (rather than evidence against it) should be stated explicitly.
8.4. Vertical - Unpacking.
It is likely to be the case that each cycle in the development of the model will
emphasise different aspects of the model. The resulting data can be used to specify a structure or a mechanism more precisely.
Whereas the two other categories of evidence which come under vertical development (debugging and consolidating) are alternatives to each other, unpacking could really be thought of as a sub-category of consolidating. This is because data which are relevant to consolidating may then be of further use in unpacking (and similarly, it is not possible for data to be useful for unpacking without fulfilling a consolidatory role at the same time). Unpacking assumes consolidation. Different instances of
behaviour are likely to yield data with different content biases. Unpacking takes advantage of this to use data which are consolidatory at one level, but which have available more detail, to enhance the level of detail of some of the structures and mechanisms in the model. However, rather than present it as such a sub-sub- heading, it has been decided to class it as an equal activity under the heading of vertical development.
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The above model development method needs to be harnessed to a data gathering strategy which supports it - this has already been discussed (above) and entails the observation of behaviour in partially differing scenarios.