# 4.5 Choosing to distinguish between complex situations and complex systems

Within some of the lineages of systems thinking and practice (Figure 24), the idea that system complexity is a property of what is observed about some ‘real world’ system, is known as classical or type 1 complexity. Exploring type 1 complexity, Russell Ackoff (1981, pp. 26–33) claimed for a set of elements to be usefully viewed as a system, it was necessary that:

- (a) the behaviour of each element of the set should have an effect on the behaviour of the whole set;

- (b) the behaviour of the elements, and their effects on the whole set, should be interdependent;

- (c) however subgroups of the elements are formed, each subgroup should have the same effect on the behaviour of the whole and none should be completely independent.

Following in the footsteps of Ackoff, and with others, Schoderbeck et al. (1985) described the complexity of what they regarded as a real or physical system as arising from the interaction of:

- (a) the number of elements comprising the system, for example, the number of chips on a circuit board;

- (b) the attributes of the specified elements of the system, for example, the degree of proficiency of musicians in an orchestra;

- (c) the number of interactions among the specified elements of the system, for example, the number of neuronal connections in the brain;

- (d) the degree of organisation inherent in the system, for example, the social arrangements in a beehive or an ants' nest.

They regarded systems as ranging from living organisms to individual families and governments.

Type 1 classification was subsequently regarded as insufficient by other practitioners because it excluded any complexity arising from culture and from human behaviour. Nor did it encompass the complexity arising from the properties of the observer, as discussed in Part 3, Section 3 (as exemplified by the language used in the list above, these authors saw ‘systems’ as real entities existing in the world. Some contemporary authors make the same claims about CAS – this idea is explored in more detail later in this section.)

Systems theorists have in the past had to confront some of the same issues as complexity theorists began to confront during the 1990s. The issues they confronted can be put rather bluntly as a series of questions:

- (a) Do systems exist ‘out there’ in the so-called ‘real world’?

- (b) Do systems have certain properties, some of which can be described or classified as complex and some as simple?

- (c) Are systems distinguished by an observer in a context? Is systemicity, the quality of being a system, a choice made by an observer when they perceive complexity in a ‘real world’ situation?

- (d) What can I learn about a situation I experience as complex by engaging with the situation using a process of inquiry that formulates systems of interest?

These are not questions that have definitive answers. The view I choose to adopt will, however, have implications for my systems thinking and my systems practice. Exploring the implications will assist in deciding what course of action will work best for any particular practitioner.

I have constructed Table 6 from the characteristics Casti (1994) claims are exhibited by simple and complex systems as well as those claimed by Plsek (2001) to characterise complex adaptive systems. The examples are also theirs.

Spend a few minutes reading through the table and then do the activity that follows.

The activity should take no more than about 15 minutes.

## Table 6 Characteristics ascribed to simple and complex systems and complex adaptive systems

Simple systems | Complex systems | Complex adaptive systems
--- | --- | ---
Have predictable behaviour; e.g. a fixed interest bank account. | Generate counterintuitive, seemingly acausal behaviour that is full of surprises; e.g. lower taxes and interest rates leading to higher unemployment. | The elements of a system can change themselves.
Few interactions and feedback or feed forward loops; e.g. a simple barter economy with few goods and services. | A large array of variables with many interactions, lags, feedback loops and feed forward loops, which create the possibility that new, self-organising behaviours will emerge: e.g. most large organisations, life itself. | Complex outcomes can emerge from a few simple rules (this relates to initial starting conditions and the idea that complicated targets and plans may stifle creative and adaptive ability).
Centralised decision making; e.g. power is concentrated among a few decision makers. | Decentralised decision making – because power is more diffuse, the numerous components generate the actual system behaviour. | Small changes can have big effects and large changes may have no effect – i.e. non-linearity operates (e.g. in the UK a small band of lorry drivers interconnected by mobile phones almost brought the country to a standstill by blocking petrol deliveries to service stations).
Are decomposable because of weak interactions; i.e. it is possible to look at components without losing properties of the whole. | Are irreducible – neglecting any part of the process or severing any of the connections linking its parts usually destroys essential aspects of the system behaviour or structure. There are dynamic changes in the system and the environment. | Thrive on tension and paradox. (It is argued that healthy organisations exist on the edge of chaos – a region of moderate certainty and agreement).
|  | Are embedded within larger complex systems, and are made up of smaller complex systems.

(After Casti, 1994, pp. 271–273 and Plsek 2001)

## Activity 45

What properties are ascribed to an observed system?

In Table 6 above, Casti has ascribed the terms simple and complex to the word systems. Likewise Plsek has ascribed the words ‘complex, adaptive’ to the word system. In what ways do you experience the terms ‘systems’ and ‘complex’ being used by Casti and Plsek?

What implications might these categories have for systems practice?

Are you able to use any of Casti's or Plsek's categories to make sense of the Microsoft-Linux story described in Box 4?

How does your attempt at doing this activity alter in any way, if at all, your understandings of the terms ‘complexity’ and ‘systems’?

### Answer

My purpose in writing this activity was to invite you to reflect on what it is that we do when we categorise anything. One way of reading this table is as a set of three categories each containing different category members. The mechanism employed in this categorisation is to add an adjective in front of the noun ‘system’. So they are different categories of system. This is another example of the ‘container metaphor’ discussed in my answer to Activity 34 and it is the same process as developing a typology (see Appendix C). Of course this is something we do all the time but I do not think we reflect very often on the implications of this doing! I discuss the implications for systems practice in the text.

The questions I posed in Activity 45 are, for me, extremely interesting but at the same time potentially confusing. The word complex is being used by Casti in some cases to mean the same as system, and some of the characteristics of complexity seem to be applied to system. The phrase complex system is common, as you can see in Appendix C, although the meaning attributed to it is often unclear in my experience. For example, it is unclear to me whether Casti is using system in its everyday sense or in the specific way it is used within the study of Systems to mean a system of interest to someone.

When I consider the examples used in Table 6 there is something qualitatively different about a simple barter economy and the phenomenon of lower taxes and interest rates leading to higher unemployment other than whether they can be described as simple or complex. Indeed, I would question whether it would be helpful to consider a barter economy as simple. Considering the quality of relationships and trust that might be necessary to sustain a barter economy it could be perceived as complex. This notion of quality of relationship seems to me an important additional distinction that could be attributed to complexity over that provided in the earlier list of Schoderbeck et al. (1985) which tends to focus only on the quantity of variables or interactions.

In some circles it is now recognised that what some people call complex adaptive systems offer insights into human action by way of analogy or metaphor (e.g. Stacey, Griffin and Shaw, 2000). Stephen Rose (1997, p. 33–4) argues that analogy ‘implies a superficial resemblance between two phenomena, perhaps in terms of the function of a particular structure’. An example is blood circulation in animals and sap flow in plants. Analogies can provide insight but also mislead – Rose (1997) asks ‘Is it a help or a hindrance to regard the access memory (RAM) in my computer as analogous to memory in chicks or humans or is it merely a metaphor?’ He also distinguishes analogies from homologies which imply a deeper identity that arises from an assumed common evolutionary origin (e.g. the bones in the front feet of a horse may be assumed to be homologous with the bones in the human hand). Each of these distinctions can be drawn into your systems practice if they help in making new distinctions. Each is a choice that can be made. From this perspective one way to engage with the idea of ‘complex adaptive systems’ (CAS) is as a metaphor to trigger new ways of thinking and acting, e.g. lets consider the NHS as if it were a CAS.

It is also possible in practice to attribute systemicity to some of the examples in Table 6. It might make sense as part of my systems practice to look at the activity of paying taxes (in a particular context) as if it were a system, or a living organism as if it were a system, or even a complex adaptive system, or a fixed interest bank account as if it were a system. In doing this though, it is important to ask who is looking at these situations as if they were systems. In the 1970s and 1980s, this confusion began to be addressed in Systems practice. Unfortunately, some confusion remains even now. So, what is the best way to sort out some of this confusion?

OpenLearn - Managing complexity: a systems approach – introduction
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