Hard and Soft Design Problems

‘Hard’ and ‘soft’ are often used with respect to problems in general. The terms have also been used in HCI research to describe design problems, notably by Dowell and Long (1989). The comprehensiveness of their address of the differences warrants inclusion here, as the basis for arguing for their importance. Additional comments attempt to clarify a number of issues in the application of the concepts of hard and soft design problems to HCI research. Read more…..

Dowell and Long (1989)……..

The ‘design’ disciplines are ranged according to the ‘hardness’ or ‘softness’ of their respective general design problems.

‘Hard’ and ‘soft’ may have various meanings in this context. For example, hard design problems may be understood as those which include criteria for their ‘optimal’ solution (Checkland, 1981). In contrast, soft design problems are those which do not include such criteria. Any solution is assessed as ‘better or worse’ relative to other solutions. Alternatively, the hardness of a problem may be distinguished by its level of description, or the formality of the knowledge available for its specification (Carroll and Campbell, 1986). However, here hard and soft problems will be generally distinguished by their determinism for the purpose, that is, by the need for design solutions to be determinate. In this distinction between problems is implicated: the proliferation of variables expressed in a problem and their relations; the changes of variables and their relations, both with regard to their values and their number; and more generally, complexity, where it includes factors other than those identified. The variables implicated in the HF general design problem are principally those of human behaviours and structures.

A discipline’s practices construct solutions to its general design problem. Consideration of disciplines indicates much variation in their use of specification as a practice in constructing solutions. Such was the history of many disciplines: the origin of modern day Production Engineering, for example, was a nineteenth century set of craft practices and tacit knowledge. This variation, however, appears not to be dependent on variations in the hardness of the general design problems. Rather, disciplines appear to differ in the completeness with which they specify solutions to their respective general design problems before implementation occurs. At one extreme, some disciplines specify solutions completely before implementation: their practices may be described as ‘specify then implement’ (an example might be Electrical Engineering). At the other extreme, disciplines appear not to specify their solutions at all before implementing them: their practices may be described as ‘implement and test’ (an example might be Graphic Design). Other disciplines, such as SE, appear characteristically to specify solutions partially before implementing them: their practices may be described as ‘specify and implement’. ‘Specify then Implement’, therefore, and ‘implement and test’, would appear to represent the extremes of a dimension by which disciplines may be distinguished by their practices. It is a dimension of the completeness with which they specify design solutions.

Taken together, the dimension of problem hardness, characterising general design problems, and the dimension of specification completeness, characterising discipline practices, constitute a classification space for design disciplines such as Electrical Engineering and Graphic Design. The space is shown in Figure 2, including for illustrative purposes, the speculative location of SE.

Figure 2. A Classification Space for ‘Design Disciplines’

Two conclusions are prompted by Figure 2. First, a general relation may be apparent between the hardness of a general design problem and the realiseable completeness with which its solutions might be specified. In particular, a boundary condition is likely to be present beyond which more complete solutions could not be specified for a problem of given hardness. The shaded area of Figure 2 is intended to indicate this condition, termed the ‘Boundary of Determinism’ – because it derives from the determinism of the phenomena implicated in the general design problem. It suggests that whilst very soft problems may only be solved by ‘implement and test’ practices, hard problems may be solved by ‘specify then implement’ practices.

Second, it is concluded from Figure 2 that the actual completeness with which solutions to a general design problem are specified, and the realiseable completeness, might be at variance. Accordingly, there may be different possible forms of the same discipline – each form addressing the same problem but with characteristically different practices. With reference to HF then, the contemporary discipline, a craft, will characteristically solve the HF general design problem mainly by ‘implementation and testing’. If solutions are specified at all, they will be incomplete before being implemented. Yet depending on the hardness of the HF general design problem, the realiseable completeness of specified solutions may be greater and a future form of the discipline, with practices more characteristically those of ‘specify then implement’, may be possible. For illustrative purposes, those different forms of the HF discipline are located speculatively in the figure.

Whilst the realiseable completeness with which a discipline may specify design solutions is governed by the hardness of the general design problem, the actual completeness with which it does so is governed by the formality of the knowledge it possesses. Consideration of the traditional engineering disciplines supports this assertion. Their modern-day practices are characteristically those of ‘specify then implement’, yet historically, their antecedents were ‘specify and implement’ practices, and earlier still – ‘implement and test’ practices. For example, the early steam engine preceded formal knowledge of thermodynamics and was constructed by ‘implementation and testing’.

Yet designs of thermodynamic machines are now relatively completely specified before being implemented, a practice supported by formal knowledge. Such progress then, has been marked by the increasing formality of knowledge. It is also in spite of the increasing complexity of new technology – an increase which might only have served to make the general design problem more soft, and the boundary of determinism more constraining. The dimension of the formality of a discipline’s knowledge – ranging from experience to principles, is shown in Figure 2 and completes the classification space for design disciplines.

It should be clear from Figure 2 that there exists no pre-ordained relationship between the formality of a discipline’s knowledge and the hardness of its general design problem. In particular, the practices of a (craft) discipline supported by experience – that is, by informal knowledge – may address a hard problem. But also, within the boundary of determinism, that discipline could acquire formal knowledge to support specification as a design practice.

In Section 1.3, four deficiencies of the contemporary HF discipline were identified. The absence of formal discipline knowledge was proposed to account for these deficiencies. The present section has been concerned to examine the potential for HF to develop a more formal discipline knowledge. The potential would appear to be governed by the hardness of the HF general design problem, that is, by the determinism of the human behaviours which it implicates, at least with respect to any solution of that problem. And clearly, human behaviour is, in some respects and to some degree, deterministic. For example, drivers’ behaviour on the roads is determined, at least within the limits required by a particular design solution, by traffic system protocols. A training syllabus determines, within the limits required by a particular solution, the behaviour of the trainees – both in terms of learning strategies and the level of training required. Hence, formal HF knowledge is to some degree attainable. At the very least, it cannot be excluded that the model for that formal knowledge is the knowledge possessed by the established engineering disciplines.

Generally, the established engineering disciplines possess formal knowledge: a corpus of operationalised, tested, and generalised principles. Those principles are prescriptive, enabling the complete specification of design solutions before those designs are implemented (see Dowell and Long, 1988b). This theme of prescription in design is central to the thesis offered here.

Engineering principles can be substantive or methodological (see Checkland, 1981; Pirsig, 1974). Methodological Principles prescribe the methods for solving a general design problem optimally. For example, methodological principles might prescribe the representations of designs specified at a general level of description and procedures for systematically decomposing those representations until complete specification is possible at a level of description of immediate design implementation (Hubka, Andreason and Eder, 1988). Methodological principles would assure each lower level of specification as being a complete representation of an immediately higher level.

Substantive Principles prescribe the features and properties of artefacts, or systems that will constitute an optimal solution to a general design problem. As a simple example, a substantive principle deriving from Kirchoff’s Laws might be one which would specify the physical structure of a network design (sources, resistances and their nodes etc) whose behaviour (e.g., distribution of current) would constitute an optimal solution to a design problem concerning an amplifier’s power supply.