Cause and Effect
For many years scientists
saw the universe as a linear place. One where simple rules of cause and
effect apply. They viewed the universe as big machine and thought that
if they took the machine apart and understood the parts, then they would
understand the whole. They also thought that the universe's components
could be viewed as machines, believing that if we worked on the parts
of these machines and made each part work better, then the whole would
work better. Scientists believed the universe and everything in it could
be predicted and controlled.
However hard they
tried to find the missing components to complete the picture they failed.
Despite using the most powerful computers in the world the weather remained
unpredictable, despite intensive study and analysis ecosystems and immune
systems did not behave as expected. But it was in the world of quantum
physics that the strangest discoveries were being made and it was apparent
that the very smallest sub nuclear particles were behaving according to
a very different set of rules to cause and effect.
Gradually as scientists
of all disciplines explored these phenomena a new theory emerged - complexity
theory, A theory based on relationships, emergence, patterns and iterations.
A theory that maintains that the universe is full of systems, weather
systems, immune systems, social systems etc and that these systems are
complex and constantly adapting to their environment. Hence complex adaptive
can be illustrated as in the following diagram.
The agents in the
system are all the components of that system. For example the air and
water molecules in a weather system, and flora and fauna in an ecosystem.
These agents interact and connect with each other in unpredictable and
unplanned ways. But from this mass of interactions regularities emerge
and start to form a pattern which feeds back on the system and informs
the interactions of the agents. For example in an ecosystem if a virus
starts to deplete one species this results in a greater or lesser food
supply for others in the system which affects their behaviour and their
numbers. A period of flux occurs in all the populations in the system
until a new balance is established.
For clarity, in the
diagram above the regularities, pattern and feedback are shown outside
the system but in reality they are all intrinsic parts of the system.
Complex adaptive systems
have many properties and the most important are,
Rather than being planned or controlled the agents 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.
· Co-evolution: 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. ( Perhaps it should have been Darwin's "Theory
of Co-evolution". )
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.
· Sub optimal: 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.
· Requisite Variety: 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
· Connectivity: The ways in which the agents 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.
· Simple Rules: 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.
· Iteration: Small changes in the initial conditions of the system
can have significant effects after they have passed through the emergence
- feedback loop a few times (often referred to as the butterfly effect).
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.
· Self Organising: 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 western town and add
up all the food in the shops and divide by the number of people in the
town there will be near enough two weeks supply of food, but there is
no food plan, food manager or any other formal controlling process. The
system is continually self organising through the process of emergence
· Edge of Chaos: 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 equilibrium does not have the internal dynamics to
enable it to respond to its environment and will slowly (or quickly) die.
A system in chaos ceases to function as a system. The most productive
state to be in is at the edge of chaos where there is maximum variety
and creativity, leading to new possibilities.
· Nested Systems: Most systems are nested within other systems
and many systems are systems of smaller systems. If we take the example
in self organising above 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.
Complex adaptive systems
are all around us. Most things we take for granted are complex adaptive
systems, and the agents in every system exist and behave in total ignorance
of the concept but that does not impede their contribution to the system.
Complex Adaptive Systems are a model for thinking about the world around
us not a model for predicting what will happen. I have found that in nearly
all situations I can view what is happening in Complex Adaptive Systems
terms and that this opens up a variety of new options which give me more
choice and more freedom.