THE DEVELOPMENT OF APPLIED SYSTEMS THINKING The history of applied systems thinking can be presented in terms of e¡orts to

overcome the weaknesses of hard systems thinking as set out in the previous The development of applied systems thinking 17

section. Success in this endeavour has been hard-won, but over the last 30 years or so signi¢cant developments have taken place and the systems approach is now valued as making an important contribution to resolving a much wider range of complex problems than hard systems thinking was able to deal with. We can understand these developments best using a frame-work for classifying systems methodologies, developed by Jackson and Keys in 1984, called the System Of Systems Methodologies (SOSM).

2.3.1 Problem contexts

The starting point in constructing the SOSM is an ‘ideal-type’ grid of problem situations or problem contexts. This grid has been described and presented in various ways (see Jackson and Keys, 1984; Jackson, 1993, 2000; Flood and Jackson, 1991), but an easily understandable version is shown as Figure 2.1.

We argued earlier in the book that problem contexts become more di⁄-cult to manage as they exhibit greater complexity, change and diversity. In very general terms, systems thinkers see increasing complexity, change and diversity as stemming from two sources: the ‘systems’ managers have to deal with, as they become larger and subject to more turbulence; and the

‘participants’, those with an interest in the problem situation, as their

Figure 2.1 Jackson’s extended version of Jackson and Keys’ ‘ideal-type’ grid of problem contexts.

values, beliefs and interests start to diverge. This gives rise to the ‘systems’

and ‘participants’ dimensions used to establish the grid.

The vertical axis expresses a continuum of system types conceptualized at one extreme as relatively simple, at the other as extremely complex. Simple systems can be characterized as having a few subsystems that are involved in only a small number of highly structured interactions. They tend not to change much over time, being relatively una¡ected by the independent actions of their parts or by environmental in£uences. Extremely complex systems, at the other end of the spectrum, can be characterized as having a large number of subsystems that are involved in many more loosely structured interactions, the outcome of which is not predetermined. Such systems adapt and evolve over time as they are a¡ected by their own purpose-ful parts and by the turbulent environments in which they exist.

The horizontal axis classi¢es the relationships that can exist between those concerned with the problem context ^ the participants ^ in three types:

‘unitary’, ‘pluralist’ and ‘coercive’. Participants de¢ned as being in a unitary relationship have similar values, beliefs and interests. They share common purposes and are all involved, in one way or another, in decision-making about how to realize their agreed objectives. Those de¢ned as being in a pluralist relationship di¡er in that, although their basic interests are compati-ble, they do not share the same values and beliefs. Space needs to be made available within which debate, disagreement, even con£ict, can take place.

If this is done, and all feel they have been involved in decision-making, then accommodations and compromises can be found. Participants will come to agree, at least temporarily, on productive ways forward and will act accordingly. Those participants de¢ned as being in coercive relationships have few interests in common and, if free to express them, would hold con£icting values and beliefs. Compromise is not possible and so no agreed objectives direct action. Decisions are taken on the basis of who has most power and various forms of coercion employed to ensure adherence to commands.

Combining the ‘systems’ and ‘participants’ dimensions, divided as suggested above, yields six ideal-type forms of problem context: simple^

unitary, simple^pluralist, simple^coercive, complex^unitary, complex^

pluralist and complex^coercive. This notion of ‘ideal type’ is crucial in understanding the SOSM and what it is seeking to convey. The grid does not wish to suggest that real-life problem situations can be de¢ned as

¢tting exactly within any of these boxes. Weber (1969), the originator of the notion, describes ideal types as stating logical extremes that can be used to construct abstract models of general realities. The grid presents some The development of applied systems thinking 19

abstract models that reveal various ways in which problem contexts might be typi¢ed by managers and management scientists. It is useful to us here if we are able to show, as we seek to do in the next subsection, that the developers of di¡erent systems methodologies have themselves been governed by particular ideal-type views of the nature of problem contexts in producing their systems approaches.

2.3.2 Systems methodologies related to problem contexts

The ideal-type grid of problem contexts is useful in helping us to understand how applied systems thinking has developed over the last few decades. It enables us to grasp the variety of responses made by systems practitioners in their attempts to overcome the weaknesses of hard systems thinking in order to tackle more complex problem situations. We are able to discern a pattern in the history of the development of applied systems thinking.

Let us consider initially the assumptions made by hard systems thinking about the nature of problem contexts. It is clear that they assume they are

‘simple^unitary’. In other words, hard systems approaches take it for granted that problem contexts are simple^unitary in character and recom-mend intervening accordingly. It is not surprising given the circumstances in which they were developed that they came to rely on there being a shared and, therefore, readily identi¢able goal. If you are trying to win a war or are engaged in postwar reconstruction, it is completely reasonable to make unitary assumptions. Later in the 1960s and 1970s, when hard systems approaches were taken into universities to be further ‘re¢ned’ by academics, an original bias toward quanti¢cation became an obsession with mathematically modelling the system of concern. To believe that this is possible you have to assume that the system you are dealing with is relatively simple. So the underlying assumptions of classical OR (and this is true, if to a lesser extent, of systems analysis and systems engineering) are simple^

unitary. Hard systems thinkers remain stuck in that area of the grid of problem contexts where it is assumed that people share values and beliefs and that systems are simple enough to be mathematically modelled. And it is true that these assumptions have served them well in tackling a whole variety of operational issues; in the case of OR for inventory, queuing, scheduling, routing, etc. problems.

Unfortunately, di⁄culties arose when attempts were made to extend the range of application of hard systems approaches, exactly because of the assumptions embedded within them. As was mentioned earlier, it is often di⁄cult to de¢ne precise objectives on which all stakeholders can agree. In

these circumstances, methodologies demanding a prede¢ned goal cannot get started because they o¡er no way of bringing about any consensus or accommodation around a particular goal to be pursued. Similarly, if the system of concern is extremely complex, then any mathematical model pro-duced can only o¡er a limited and distorted view of reality from a particular perspective ^ and one which, in a turbulent situation, becomes quickly out of date. In the 1970s, therefore, came a general understanding of the lack of usefulness of hard systems thinking for more complex problem situations, and in problem contexts that were deemed to be more pluralist and coercive in character.

It is to the credit of applied systems thinking that it has not remained stuck in its simple^unitary ghetto. The last 30 or so years have seen an attempt to extend the area of successful application of systems ideas by developing methodologies that assume that problem contexts are more complex, pluralist and/or coercive in nature. This is the progress in applied systems thinking that we now seek to chart.

We begin with the vertical axis of the ideal-type grid of problem contexts, and our concern, therefore, is with those systems practitioners who wanted to move down the axis by assuming that problem contexts were more complex than hard systems thinkers believed. The aim of hard systems thinking was to optimize the system of concern in pursuit of a known goal, and to do this it appeared necessary to model the interactions between all those elements or subsystems that might a¡ect that system of concern. In complex systems, the vast numbers of relevant variables and the myriads of interactions make this an impossible requirement. The solution, suggested by those wishing to progress down the vertical axis, was to identify those key mechanisms or structures that govern the behaviour of the elements or subsystems and, therefore, are fundamental to system behaviour. It is regarded as impossible to mathematically model the relationships between all the variables that ‘on the surface’ appear to be involved in what the system does. You can, however, determine the most important structural aspects that lie behind system viability and performance. This ‘structuralist’

approach enables the analyst to determine, at a deeper level, what is going wrong with the present functioning of the system and to learn how to manip-ulate key design features so that the system can survive and be e¡ective over time by continually regulating itself, and self-organizing, as it adapts to internally and externally generated turbulence.

The systems approaches responsible for making this shift down the vertical axis show a common concern for understanding the nature of complex adaptive systems and with ensuring they are designed to have a The development of applied systems thinking 21

capacity for goal seeking and remaining viable in turbulent environments. In this book we concentrate on ‘system dynamics’, ‘organizational cybernetics’

and ‘complexity theory’ as systems approaches that assume, in this manner, that problem contexts are extremely complex and need tackling in a ‘structur-alist’ fashion. In each case, as we shall see, they identify di¡erent key structural aspects that need to be understood and manipulated in dealing with complex-ity. In the case of system dynamics it is the relationships between positive and negative feedback loops that can give rise to ‘archetypes’ of system behaviour. In the case of organizational cybernetics it is cybernetic laws that can be derived from the concepts of black box, feedback and variety.

With complexity theory it is ‘strange attractors’ and the variables that have to be adjusted to ensure that an ‘edge of chaos’ state is achieved.

Applied systems thinkers have also made considerable progress along the horizontal axis of the ideal-type grid of problem contexts. If we move part way along that axis we ¢nd that a number of methodologies have been developed that assume that problem contexts are pluralist and provide recommendations for analysis and intervention on that basis. This tradition of work has become known as ‘soft systems thinking’ to distinguish it from the hard systems thinking that was left behind.

Soft systems thinkers abandoned the notion that it was possible to assume easily identi¢able, agreed-on goals that could be used to provide an objective account of the system and its purposes. This was seen to be both impossible and undesirable given multiple values, beliefs and interests. Instead, attention had to be given to ensuring su⁄cient accommodation between di¡erent and sometimes con£icting world views in order that temporary coalitions could be fashioned in support of particular changes. The solution was to make subjectivity central, working with a variety of world views during the methodological process. In Checkland’s ‘soft systems methodology’

(1981), a highly developed approach of this kind, systems models expressing di¡erent viewpoints, and making explicit their various implications, are constructed so that alternative perspectives can be explored systemically, compared and contrasted. The aim is to generate a systemic learning process in which the participants in the problem situation came to appreciate more fully alternative world views, and the possibilities for change they o¡er, and as a result an accommodation, however temporary, becomes possible between those who started with and may still hold divergent values and beliefs.

Systems practitioners seeking to progress along the horizontal dimension emphasize the crucial importance of values, beliefs and philosophies. Their primary interest is in exploring the culture and politics of organizations to

see what change is feasible and in gaining commitment from participants to agreed courses of action. Such soft systems thinkers are not trying to devise system models that can be used over and over again to reveal how real-world systems can be improved. This is felt not to be relevant or useful because of the widely di¡erent viewpoints about purposes that will be present in pluralist problem contexts. Instead, what is usefully replicated, as Checkland argues, is the methodology employed. The same approach to bringing about consensus or accommodation is tried again and again and is gradually improved. As well as studying Checkland’s ‘soft systems method-ology’ we will be considering ‘strategic assumption surfacing and testing’

and Acko¡ ’s ‘interactive planning’. All these soft systems approaches have by now been well researched. As a result we know much better than previously about some methodological processes that can assist in bringing about accommodations between di¡erent value positions and generate commitment among participants to implement agreed changes.

If we shift further along the horizontal axis of the grid of problem contexts, the issue arises of how to intervene in problem situations that are regarded as coercive. Soft systems thinking fails to respond appropriately because of its pluralist bias that consensus, or at least accommodation, between di¡erent stakeholders can be achieved. Systems practitioners have, therefore, sought to formulate ‘emancipatory’ systems approaches based on the assumption that problem situations can be coercive. Ulrich’s ‘critical systems heuristics’ allows questions to be asked about who bene¢ts from par-ticular system designs and seeks to empower those a¡ected by management decisions but not involved in them. Beer’s ‘team syntegrity’ seeks to specify an arena and procedures that enable all stakeholders to debate openly and de-mocratically the issues with which they are confronted. Both these ap-proaches are considered.

Finally, there are systems practitioners who worry about the claims of any systems methodology to be able to guarantee generalized improvement.

They advocate postmodern systems practice in the face of the massive and impenetrable complexity and coercion that they see as inherent in all problem contexts. Suppressed viewpoints must be surfaced and diversity encouraged as in the emancipatory systems approach. All that is possible however is contested, local improvement justi¢ed on the basis that it feels right given local circumstances. Chapter 13 is devoted to this version of applied systems thinking.

In short, the argument of this section is that applied systems thinking has developed over the past few decades taking into account the characteristics of a much wider range of the ideal-type problem contexts represented in The development of applied systems thinking 23

the grid. It has progressed along the vertical dimension to take greater account of complexity. It has progressed along the horizontal dimension acknowledging that problem contexts can be de¢ned as pluralist and coercive. These conclusions are summarized in Figure 2.2. The intersecting lines that constructed the particular problem contexts in the grid of Figure 2.1 have been removed in this representation of the SOSM. This should be taken to mean that it is only indicative of the assumptions made by di¡erent systems approaches about the nature of problem contexts. There is no inten-tion to pigeon-hole methodologies and a more sophisticated treatment of their underlying assumptions will be presented in Part II.

2.4 THE MAIN STRANDS OF APPLIED SYSTEMS THINKING