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The idea of complex evolving systems grew out of the somewhat simpler concept of “complex adaptive systems.” The difference is that the evolving systems not only adapt and survive — they also learn better adaptations over time as a form of “meta-adaptation.” But for simplicity, we will use the terms interchangeably.
The human brain is one example of a complex evolving system, as are human economies, human cities, and most interesting systems in the universe that utilise “intelligence,” in the broadest sense of the word.
Here is a brief overview of complex adaptive systems:
Complex adaptive systems have many properties and the most important are,
· Emergence: 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 perspectives.
· 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 and feedback.
· 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.
These systems are not deterministic, not predictable. The Earth’s climate is an example of a complex adaptive system. Its enslavement to thermodynamics — while remaining essentially unpredictable — is an excellent model of how other, more complex systems such as the human nervous system, are also slaved to thermodynamics.
It is important to note that corrupt intellectuals, scientists, politicians, and activists have simplified the climate system to the point of buffoonery — reducing it to a quasi-religious cult in an attempt to achieve a false sense of certainty and predictability. Human movements and enterprises abound with such distortions of complex systems. Only by studying such systems can brighter and more honest individuals begin to comprehend the challenge set in front of anyone who tries to understand the world (universe) he lives in.
CNES was founded on the principle that all intelligent systems are open thermodynamic systems capable of self-organization, whereby structural order emerges from disorder as a natural consequence of exchanging energy, matter or entropy with their environments.
These systems exist in a state far from equilibrium where the evolution of complex behaviors cannot be readily predicted from purely local interactions between the system’s parts. Rather, the emergent order and structure of the system arises from manifold interactions of its parts. These emergent systems contain amplifying-damping loops as a result of which very small perturbations can cause large effects or no effect at all. They become adaptive when the component relationships within the system become tuned for a particular set of tasks.
CNES promotes the idea that the neural system in the brain is an example of such a complex adaptive system. A key goal of CNES is to explain how computations in the brain can help explain the realization of complex behaviors such as perception, planning, decision making and navigation due to brain-body-environment interactions.
We seek to apply the thermodynamic basis of self-organization to study a broader class of problems via physical manifestations of intelligence that may one day explain the emergence of behaviors in a wide range of complex physical systems—from animate systems, financial networks, social networks and other very large-scale complex systems.
Whole Systems: Foundations of Complex Evolving Systems
Encyclopaedia Autopoietica: The concept of “autopoiesis,” or self-organisation as explicated by Humberto Maturana and Francisco Verela.