38. Complex Adaptive Systems

Complex adaptive systems (CASs) are complex systems that not only evolve like any other dynamical system, but also learn by making use of the information they have acquired.

http://www.ece.uc.edu/~casl/welcome.html

Learning by a CAS requires, among other things, the evolution of an ability to distinguish between the random and the regular. CASs can undergo processes like biological evolution (or biological-like evolution). They do not just operate in an environment created for them initially, but have the capability to change the environment. For example, species, ant colonies, corporations, and industries evolve to improve their chances of survival in a changing environment. Similarly, the marketplace adapts to factors like immigration, technological developments, prices, extent of availability of raw materials, and changes in tastes and lifestyles. Some more examples of CASs are: A baby learning to walk; a strain of bacteria evolving resistance to an antibiotic; a beehive or ant colony adjusting to the decimation of a part of it; etc.

By contrast, complex materials are examples of nonadaptive complex systems (discussed in my book Smart Structures). Galaxies and stars and other such complex objects are additional examples of nonadaptive complex systems. They are inanimate systems which evolve with time, but within the unchanging constraints provided by the initial conditions and the environment.

I list here the characteristic features of CASs, also called vivisystems. I have in mind systems that are large in terms of numbers of individuals or agents comprising the group.

http://en.wikipedia.org/wiki/File:Complex-adaptive-system.jpg

1. There is a network of interactions among the large number of individuals in the group, acting in parallel.
2. Individuals acquire information about the surroundings and about themselves.
3. Each individual constantly reacts to what the others are doing. Therefore, from the vantage point of any individual, the environment is changing all the time.
4. Individuals identify regularities in the information acquired by them, and condense those regularities into a schema or conceptual model. CASs are pattern seekers.
5. The individuals act on the basis of that schema.
6. There can be many competing schemata, and the most suitable ones survive and evolve, based on the feedbacks received from the interactions with the environment.
7. The control in a CAS is highly dispersed. No one is really in command.
8. Coherent behaviour or order in a CAS arises from both competition and cooperation among the individuals themselves.
9. Emergent behaviour (cf. Part 33) results from competition and cooperation among the individuals.
10. A CAS has many levels of self-organization. Individuals at one level serve as the building blocks for individuals at the next higher level of hierarchy. In the human brain, for example, one block of neurons forms the functional regions for speech, another for vision, and still another for motor action. These functional areas link up at the next higher level of cognition and generalization.
11. In the light of new experience (obtained by feedback), CASs may constantly adjust and rearrange their building blocks. This forms the basis of all learning, evolution, or adaptation in CASs. CASs are thus characterized by perpetual novelty. The processes of learning, evolution, and adaptation are basically the same. One of their fundamental mechanisms is the revision and recombination of the building blocks.
12. The CASs are constantly making predictions, thus anticipating the likely future. The predictions are based on various internal models of the world, and the models are constantly revised on the basis of new inputs; they are not static blueprints. Sheer large numbers and mutual exchange of information result in intelligence, the swarm intelligence.
13. CASs have a certain dynamism not present in nonadaptive complex systems. And yet this dynamism is far from being total randomness. CASs have the ability to establish a balance between order and chaos. This balance line is referred to as the EDGE OF CHAOS. This line (or rather a hyper-membrane) in phase space represents the coexistence of order and chaos.
14. Life signifies both stability and creativity, something that becomes possible in the vicinity of the edge of chaos.
15. The CASs have many niches, each of which can be exploited by an agent which has adapted itself to fill that niche.
16. Filling up of a niche opens up new niches. The system is just too large to be ever in equilibrium. There is perpetual novelty, the stuff biological evolution is made of.

http://commons.wikimedia.org/wiki/File:Biocomplexity_spiral.jpg

As pointed out by Murray Gell-Mann in his great book The Quark and the Jaguar, the crux of a highly complex system is in its non-random aspects. He introduced the notion of EFFECTIVE COMPLEXITY, and defined it (relative to a CAS that is observing it) as roughly the length of a concise description of the regularities of that system or bit string. By contrast, algorithmic information content (AIC) (cf. Part 23) refers to the length of the concise description of the whole system or string, rather than referring to the regularities alone.

A CAS separates regularities from randomness. Therefore a CAS provides us the possibility of defining complexity in terms of the length of the schema used by it for describing and predicting an incoming data stream:

Suppose a bit stream is totally random. Then its AIC is infinite. But its effective complexity is zero, because a CAS observing the bit stream will not find any regularity in it, and therefore the length of the schema describing the regularities will be zero. At the other extreme, if the bit stream is totally regular, the AIC is very small (nearly zero), and so is the effective degree of complexity.

For intermediate situations, the effective complexity is substantial.

Thus, for effective complexity to be significant, the AIC must not be too high or too low. That is, the system should be neither too orderly nor too disorderly. For such situations, the AIC is substantial but not maximal, and it has two contributions: The apparently regular portion (corresponding to the effective complexity), and the apparently random or stochastic portion. Complexity thrives when there is a critical balance between order and chaos.