Complexity

COMPLEX ADAPTIVE SYSTEMS

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The brain, the immune system, gene-regulation networks, the formation of clouds, the fluctuations in the stock market, weather prediction and artificial intelligence are all examples of complex adaptive systems. For a system to be considered complex and adaptive it must consist of many different interactive components which are themselves non linear and dynamic. This in turn leads to multiple levels of organisation within the system. 

The dynamic of all social human interactions, both interpersonal and societal, are governed by complexity. 

"What a distressing difference there is between the radiant intelligence of the child and the feeble mentality of the adult." Sigmund Freud."

Children exhibit all the behaviours of a complex adaptive system that is constantly shifting and changing in relation to his or her environment whereas most adults exhibit state bound behaviour which in essence is the exact opposite of a responsive behaviour.  

Complexity is a model for thinking about the world around us and viewing a human being through this lens opens up a variety of new options ultimately giving more choice and more freedom.  

COMPLEX ADAPTIVE SYSTEMS have many properties, the most significant of which are:

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Rather than being planned or controlled the agents (individual parts) in the system interact in apparently random ways. From all these interactions patterns emerge which informs the behaviour of the agents within the system and the behaviour of the system itself. For example a termite hill is a wondrous piece of architecture with a maze of interconnecting passages, large caverns, ventilation tunnels and much more. Yet there is no grand plan, the hill just emerges as a result of the termites following a few simple local rules. 

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EMERGENT

All systems exist within their own environment and they are also part of that environment. Therefore, as their environment changes they need to change to ensure best fit. But because they are part of their environment, when they change, they change their environment, and as it has changed they need to change again, and so it goes on as a constant process. 

Some people draw a distinction between complex adaptive systems and complex evolving systems. Where the former continuously adapt to the changes around them but do not learn from the process. And where the latter learn and evolve from each change enabling them to influence their environment, better predict likely changes in the future, and prepare for them accordingly.

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CO-EVOLVING

A complex adaptive systems does not have to be perfect in order for it to thrive within its environment. It only has to be slightly better than its competitors and any energy used on being better than that is wasted energy. A complex adaptive systems once it has reached the state of being good enough will trade off increased efficiency every time in favour of greater effectiveness.

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SUB OPTIMAL

The greater the variety within the system the stronger it is. In fact, ambiguity and paradox abound in complex adaptive systems which use contradictions to create new possibilities to co-evolve with their environment. Democracy is a good example in that its strength is derived from its tolerance and even insistence in a variety of political perspectives.

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REQUISITE VARIETY

The ways in which the agents (individual parts) in a system connect and relate to one another is critical to the survival of the system, because it is from these connections that the patterns are formed and the feedback disseminated. The relationships between the agents are generally more important than the agents themselves.

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CONNECTIVITY

Complex adaptive systems are not complicated. The emerging patterns may have a rich variety, but like a kaleidoscope the rules governing the function of the system are quite simple. A classic example is that all the water systems in the world, all the streams, rivers, lakes, oceans, waterfalls etc with their infinite beauty, power and variety are governed by the simple principle that water finds its own level.

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SIMPLE RULES

Simply put iteration means repetition. In terms of complex adaptive systems each iteration or repetition builds upon a the result of an earlier repetition. What results from one iteration is used as the starting point for the next iteration. Thus, the starting point or initial condition for each iteraction is always different. A rolling snowball for example gains on each roll much more snow than it did on the previous roll and very soon a fist sized snowball becomes a giant one.


Even very minute changes in the initial conditions of the system can have significant effects after they have passed through the emergence - feedback loop a few times. This so called 'butterfly effect' is the sensitive dependence on initial conditions in which a small change in one state of a nonlinear system can result in large differences in a later state.

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ITERATION

Self Organising - meaning there is no hierarchy of command and control in a complex adaptive system. There is no planning or managing, but there is a constant re-organising to find the best fit with the environment. A classic example is that if one were to take any town in the Western world and add up all the food in the shops and divide by the number of people in the town there will be approximately two weeks supply of food, but there is no actual 'food plan', 'food manager' or any other formal controlling process. The system is continually self organising through the process of emergence and feedback.

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SELF ORGANISING

Edge of Chaos: A system in equilibrium does not have the internal dynamics to enable it to respond to its environment and will slowly (or quickly) die. Complexity theory is not the same as chaos theory, which is derived from mathematics. But chaos does have a place in complexity theory in that systems exist on a spectrum ranging from equilibrium to chaos. A system in chaos ceases to function as a system. The most productive state for any system to be in is at the 'edge of chaos' where there is maximum variety and creativity, leading to new possibilities and a much better chance of long term survival.

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EDGE OF CHAOS

Most systems are nested within other systems and many systems are systems of smaller systems. If we take the example we used in the description of 'self organising' and consider a food shop. The shop is itself a system with its staff, customers, suppliers, and neighbours. It also belongs the food system of that town and the larger food system of that country. It belongs to the retail system locally and nationally and the economy system locally and nationally, and probably many more. Therefore it is part of many different systems most of which are themselves part of other systems.

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NESTED SYSTEMS