Complexity Made Simple

There is no single definition of complexity science or an agreed general definition of complexity. Different practitioners use different definitions. There is, however, general agreement that complexity stands apart from positivism or research methods that offer single solutions to single problems.

Complexity is therefore best deployed in cases where we are not sure that we can find a single answer or where more than one approach may be taken. For example we may talk about:

“complex systems in which the ‘simple, microscopic’ components consist of people (or companies) buying and selling goods, and the collective behaviour is the complex, hard-to-predict behaviour of markets as a whole, such as changes in the price of housing in different areas of the country or fluctuations in stock prices” (Mitchell, 2009, p. 9).

This casts the starting point of complexity as interactions and what emerges from those interactions. This approach defines complexity as ‘emergence from interactions’. This open definition allows complexity to apply to different domains (Stacey, 2005).

Complexity also uses the term ‘autopoiesis’ to refer to something capable of reproducing and maintaining itself. Autopoiesis is basic to the living individual. What happens to the individual is subservient to its autopoietic organisation for, as long as it exists, the autopoietic organisation remains invariant (Maturana & Varela, 1987).

This means that the identity of an individual, and therefore their emergent global properties, are generated through a process of self-organisation, within their network of components. Here the process of self-organisation is conditioned by a two-way process of local-to-global and global-to-local causation.

Complexity work often uses complex adaptive systems. As systems they are not explicitly in the present or in time at all. However, they shape our thoughts and actions which are in the present (Johnson-Laird & Byrne, 1995).

For the current purposes we can say that complex adaptive systems use models to develop and build theories of interactions. They imply the use of models and indeed regard systems as models. The models show how systems behave within fixed constraints i.e. the terms of the model. It would therefore be wrong to say that models deliver a rich epistemology.

So by way of a general definition we may say that a complex adaptive system is something that exhibits a particular kind of behaviour. This particular kind of behaviour requires self-organisation, and it requires behaviour that leads to the emergence of something new, here at the social level. This emergence is then revisited and fed back into the system in such a way that something else emerges.

Complexity may also be considered in terms of complex responsive processes. They deal with interactions in the present and involve reflections on interactions that take place in time. You cannot, however, stop time so these present reflections always refer back to a present now gone (Stacey, 2011).

These approaches to complexity may be considered as complementary for both complex adaptive systems and complex responsive processes address how we behave, respond and think within a context. The context could be the wellbeing of communities or the prevention and management of disasters. This means that we may identify and explore the strengths and similarities of both approaches.

We may take a general definition of complexity and use complexity terminology to work out a complexity approach that suits our research activities. In the social realm this is relatively easy to do for people interact and as they do so the situations they are involved with become more and more complex. We may then consider the complexity of human situations in terms of the awareness of the people involved in those situations and their competence in judging, emoting, planning, etc. (Zeeuw, 2011).

Bibliography

Johnson-Laird, P. & Byrne, R. (1995).  A model point of view. Thinking and Reasoning, 1, 339-350.

Maturana, H. & Varela, F. (1987). The tree of knowledge: The biological roots of human understanding. Boston, USA: New Science Library.

Mitchell, M. (2009). Complexity: a guided tour. Oxford: Oxford University Press.

Stacey, R. (Ed) (2005). Experiencing emergence in organizations: Local interaction and the emergence of global pattern. London: Routledge.

Stacey, R. (2011). Strategic Management and Organisational Dynamics. Harlow: FT Prentice Hall.

Zeeuw, G. (2011). Improving non-observational experiences: Channelling and Ordering. Journal of Research Practice, 7(2), Article M2.

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