Heroes Not Zombies

I read a lot about complexity, and especially about complex adaptive systems, because it’s the most useful conceptual model I’ve found so far when I’m considering real life issues like health, illness, economics, personal growth (and so on!). An article entitled “Occupational Science and Social Complexity” by Aaron M. Eakman recently reviewed this model in the context of occupational science, and I thought I’d share a couple of the sections with you because he nicely clarifies some key points.

In the article there is a good summary of “characteristics [which] are common to complex systems”. He describes seven of them, and I’ve put in bold what I consider to be amongst the most important points to take on board –

1) Relationships between components of the system are non-linear,
meaning that a small perturbation may lead to dramatically large effects. By contrast, in linear systems the effect is always directly proportional to a cause.

2) Local rules affecting the relationships between components of the system lead to the emergence of global system order;

3) Both negative (damping) and positive (amplifying) feedback are often found in complex systems. The effects of an element’s behavior or the emergent behavior of the system are fed back in such a way that the element itself is altered.

4) Complex systems are usually open systems; they exchange some form of energy or information with their environment.

5) Complex systems are historical systems that change over time, and prior states may have an influence on present states.

6) The components of a complex system may themselves be complex systems. For example, an economy is made up of organizations, which are made up of people – all of which are complex systems.

7) Complex systems may exhibit behaviors that are emergent; they may have properties that can only be studied at a higher system level.

Think what these characteristics mean when you are considering a human being, an organisation, or a society. What are seeing are organisms or organisations which are undergoing constant, unpredictable change. You can guess how things are going to go, based on prior knowledge and experience of other situations which you judge to be similar, but you’re going to have to be constant alert to the fact that things are very likely to go some other way entirely, and you’ll need to adjust your choices accordingly.

In fact living creatures, particularly multi-cellular ones, like human beings can be thought of as a particular kind of complex system – a “CAS” (Complex Adaptive System).

Complex adaptive systems are special cases of complex systems which are adaptive in that they have the capacity to change and learn from experience. John Holland describes a complex adaptive system as a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a complex adaptive system tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves.

In other words, we don’t just constantly change, frequently in unpredictable ways, but we adapt – our changes are not entirely random, they are informed – informed by prior knowledge and experience and informed by constant feedback in the here and now.

That last point about coherent behaviour arising from “competition and cooperation” is a challening one. There are a lot of people who think that competition is THE key in understanding life and evolution. There are others who say, no, it’s cooperation which is the key. It seems the reality is, it’s both.

Complexity science eschews reductionism and determinism by focusing on the emergent properties of a system and the non-linear interactions of a system’s components. Complexity science recognizes that such systems cannot be understood simply by understanding the parts – the interactions among the parts and the consequences of these interactions are equally significant.

Modern Medicine is still stuck in the reductionist and deterministic paradigms. And the problem is they just do NOT reflect reality. We don’t just need the science which shows us how particular cells or organs work. We need the science which shows how what happens when active agents begin to compete and co-operate. We need to discover just how a complex system adapts, repairs, heals and evolves. The old idea of “fixing” the “wonky bits” only works (and only for a limited time) where the scenario conforms to reductionist and deterministic paradigms (in Acute Care for example)

One more thought provoking point from this article –

Finally, Byrne (1998) has asserted that as a basis for social action: Complexity/chaos offers the possibility of an engaged science not founded in pride, in the assertion of an absolute knowledge as the basis for social programmes, but rather in a humility about the complexity of the world coupled with a hopeful belief in the potential of human beings for doing something about it.

Byrne, D. (1998). Complexity theory and the social sciences. New York: Routledge.

I couldn’t agree more.

Humilty

and

Hope

Let’s proceed on that basis.