Complexity Theory

Complexity science forms science's most recent attempt to explain how order and novelty emerge in the world. As the Plexus Institute states:  "The traditional view of the natural world was made up of machine-like entities that you could understand by taking them apart and examining the components. A lot has been learned about nature by this approach. But the vast majority of nature is not amenable to this way of working, because most of nature is made up of what complexity scientists call non-linear, complex adaptive systems - systems created by a number of diverse and independent agents that are constantly changing and interacting with each other. In complex adaptive systems, a study of the parts surely produces an incomplete understanding of the whole."

Thus, complexity science studies dynamic systems that adapt to their context (the faculty committee, body systems, diabetic control). In adaptive systems apparently inexplicable results arise from the interactions between simpler components. Just as the properties of hydrogen and oxygen atoms cannot be simply combined to account for the noise of a brook or the shimmer on a lake, nor can we accurately predict how my son will act when he goes out with his friends. While difficult to predict precisely, such systems are not random and tend to follow patterns. In addition, order and innovation can emerge spontaneously from the interactions within a complex system; they do not need to be imposed from a leader or from outside.

The term ‘emergence’ refers to wholes that are greater than the sum of their parts. This occurs where the system is a collection of individual agents who are free to act in ways that are not always totally predictable, and where the actions of one agent changes the context for other agents. Examples include the immune system, a colony of termites, the financial market, and just about any collection of humans (for example, a family, a committee, or a health care team). In all of these, the interconnections between the parts are more important in explaining the overall results than the composition of the parts themselves. A feature of adaptive systems is that they may not reach steady equilibrium. They operate in a delicate dynamic balance between static and chaotic modes in an area called the 'edge of chaos'.

Understanding such situations lies outside the scope of reductionist statistical techniques, and beyond Newton's ‘clockwork universe,’ in which big problems can be broken down into smaller ones, analyzed, and solved by rational deduction. Note, however, how the mechanistic view has strongly influenced our thinking about medicine and population health.

 

Complicated or Complex?

Simple = following a recipe; Complicated = sending a rocket into space; Complex = raising a child. For the latter, there are no precise formulae; you have to adapt your child-rearing practices to the particular child. You learn on the job and can only follow general guidelines, but even these often have to be modified from actual experience.

Things that are complicated (my computer) may still be understood using reductionist thinking: study the whole by studying its parts and you'll understand it, even if there are lots of parts. In complex systems, however, the parts interact (the whole is more than its parts) in a non-linear way that depend on their history and the context. In addition, complex systems do not necessarily strive toward homeostasis. Instead, complex systems move between non-equilibrium states (known as ‘attractors’) sometimes in very unpredictable and dramatic ways (think of voters swinging between conservative and liberal parties). This movement is not random, but nor is it easily explained or predicted. So,

In a complicated system, many linear interactions strive to achieve the ideal of homeostasis in a manner that is predictable and reproducible, independently of its environment.

In a complex system, non-linear interactions between component parts create effective and evolving states far from equilibrium in a fashion which is highly dependent on connections within the system and its environment.

 

Linear, Non-linear and Random

In linear systems the result of a change is, by and large, predictable. For instance if it is light work carrying a small bag uphill, it will be harder carrying a moderate one and harder still to carry a heavy bag. Non-linear systems exist where the results of changing one factor are not readily predictable but are still replicable. Sometimes a small change in A results in no change in B, other times a huge change in B. Random systems are ones where the results of any action are unpredictable. Even if the same starting circumstances were recreated in a random system, the result at any given subsequent time would be different.

 

Chaos

Closely related to complexity, chaotic behaviour is neither random, nor periodic (although at times it can look like either). It is unpredictable in that there may be no apparent relationship between measurements (it looks random) but it is deterministic in that given the same starting conditions and the same equation, the same results would be produced. This determinism is, however, very sensitive to the initial conditions such that small differences in values at the start may result in huge (or no) differences later on.

 

Complexity and Human Health

In relation to human health and illness there are several levels of complex systems:

The human body is composed of multiple interacting and self regulating physiological systems, with biochemical and neuroendocrine feedback loops

The behaviour of any individual is determined partly by an internal set of rules based on past experience and partly by unique and adaptive responses to new stimuli from the environment

The web of relationships in which individuals exist contains many varied and powerful determinants of their beliefs, expectations, and behaviour

Individuals and their immediate social relationships are further embedded within wider social, political, and cultural systems which can influence outcomes in novel and unpredictable ways

A small change in one part of this web of interacting systems may lead to a much larger change in another part through amplification effects.

For all these reasons neither illness nor human behaviour can safely be "modelled" in a simple cause and effect system. The human body is not a machine and its malfunctioning cannot be adequately analyzed by breaking the system down into its component parts and considering each in isolation. Nonetheless, cause and effect modelling underpins much of the problem solving we attempt in clinical encounters.

 

More Characteristics of Complex Systems

The boundaries of mechanical systems are well defined: we can easily define what is and is not a part of a car. Complex systems typically have fuzzy boundaries. Membership on a committee can change, and people can simultaneously be members of several systems. Because the agents can change, a complex system can adapt its behaviour over time.

  • Systems influence one another. Since each agent and each system is nested within other systems, all evolving together and interacting, we cannot fully understand any of the agents or systems without reference to the others. This can lead to unexpected actions in response to change and so complicates prediction.
  • Agents in a complex adaptive system respond to their environment by using internalized rules that drive action. In a biochemical system, the "rules" are chemical reactions. At a human level, the rules can be expressed as instincts, procedural rules, or mental models. These internal rules need not be shared, explicit, or even logical when viewed by another agent
  • The fact that complex systems interact with other complex systems leads to tension and paradox that can never be fully resolved. In complex social systems, the seemingly opposing forces of competition and cooperation often work together in positive ways: fierce competition within an industry can improve the collective performance of all participants
  • Neither the system nor its external environment are, or ever will be, constant
  • Individuals within a system are independent and creative decision makers
  • Uncertainty and paradox are inherent within the system
  • Problems that cannot be solved can nevertheless be "moved forward"
  • Effective solutions can emerge from minimum specification
  • Small changes can have big effects
  • Behaviour exhibits patterns (that can be termed "attractors")
  • Change is more easily adopted when it taps into attractor patterns
  • Complex systems frequently produce fluctuations that are often explicable only at the level of the whole system.

 

Because the elements are changeable, the relationships non-linear, and the behaviour emergent and sensitive to small changes, the detailed behaviour of any complex system is fundamentally unpredictable over time. Ultimately, the only way to know exactly what a complex system will do is to observe it: it is not a question of better understanding of the agents, of better models, or of more analysis.

 

Attractors

Despite the lack of detailed predictability in complex systems, there are often general patterns that allow us to make useful statements about the behaviour of the system. Patterns called ‘attractors’ have received attention. The idea of attractors arose from chaos theory, in which chaotic systems almost, but not quite, repeat themselves, appearing to gravitate toward characteristic features (absolute values, or constant changes in values). These are called attractors in the sense that they seem to be attracting the fluctuations. Chaotic attractors may not be immediately visible, and may not be single points, but patterns of points. Relatively simple attractor patterns have been found in share prices in the financial market, in biological systems such as beat to beat variations in heart rate, and in human behaviour.

 

Complexity and Chaos

Most writers on complexity identify three zones of systems. Take the example of collaboration between people. In a first zone the group is considering relatively simple decisions, over which there is a high degree of agreement between the actors, and a high degree of certainty in the nature of the task (e.g., a routine hernia operation in an elderly man). This is where simple, linear systems thinking applies. As the degree of certainty and level of agreement between actors declines (e.g., the operation unexpectedly goes badly, the patient bleeds and a tumor is found that no-one suspected) we enter the zone of complex processes in which reactions are not simple to predict. Finally, as the levels of certainty and agreement fall further, we enter the zone in which chaos theory applies.

 

Achieving Change

Based on reductionist thinking, our natural tendency is to troubleshoot and fix things, to tackle one problem at a time; in essence to simplify, to break down ambiguity and move toward a simple system. But complexity science suggests that it is often better to try multiple approaches, to let the system change itself and let direction arise by gradually shifting time and attention towards those things that seem to be working best.

The growing literature on changing patients' behaviour in relation to lifestyle focuses on those who are "resistant to change." Complexity science suggests that "readiness to change" occurs when a system is in a state far from equilibrium; there is then sufficient tension to change. In such circumstances a small influence can have a large effect on behaviour for example, brief advice apparently leads 2% of smokers to quit, while more intensive advice and discussion in the consultation has little additional impact

In place of regarding the human body in a mechanical model, complexity views illness and health as result from complex, dynamic, and unique interactions between components of the overall system. Effective clinical decision making requires a holistic approach that accepts unpredictability and builds on subtle emergent forces within the overall system. The clinician’s intuition and personal experience becomes relevant again.

It is also clear that far from all problems lie in the zone of complexity. Where there is certainty about what is required and agreement among agents (for example, the actions of a surgical theatre team in a routine operation) it is appropriate for individuals to think in somewhat mechanistic terms and to follow assigned roles. Here, individuals give up some autonomy and the system displays less emergent behaviour but the job gets done efficiently.

 

Link to Complexity in Primary Care group; to a pair of very clear introductory papers on complexity by Sunny Auyang (worth reading!); 
- U of Waterloo report on Adaptive Ecosystem Approach
- A series of articles on complexity appeared in the journal "Emergence" (2001; vol. 3, issue #1). Links to these are included under the Additional Readings for Session 9.  
- There's a group called Plexus whose mission is to "Foster the health of individuals, families, communities, organizations and our natural environment by helping people use concepts emerging from the new science of complexity" Their site has reviews of various papers on the topic, and a good general intro to complexity theory.  They also have the Edgeware group that discusses the application of complexity theory to health care organizations

 

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