Posts Tagged ‘Linearity’

Chaos and Complexity #5: Chaos vs. Complexity

by Gary Monti on October 12, 2010

What is the difference between chaos and complexity? Many of the previous blogs have referred to both terms. While related, they are distinct. Here they will be differentiated.

Chaos vs Random

First, let’s look at what chaos is and isn’t. In everyday language chaos and randomness are considered synonyms. In chaos theory they are very different.

Random refers to a lack of structure at any level. No intelligence or pattern can be discerned.

Chaos does have observable patterns present. Chaos refers to the unpredictable behavior a deterministic (rules-driven) system displays. Chaotic systems are non-linear. This means small changes might produce a large change at certain times (tipping points). At other times a chaotic system can display remarkable robustness and remain intact when being hit with many, substantial impacts. There are other characteristics associated with chaotic systems, which we will explore in later blogs. For now, one more characteristic will be addressed which leads into the development of complex systems – emergence.

Emergence and Adaptation

Emergence is the appearance of patterns or intelligence arising from the interactions of components at a granular level. The most important distinction with emergence is the bottoms-up rather than top-down development of patterns. The resulting patterns can’t be predicted but they can be capitalized upon, amplified, and used to push adaption.

Adaptation is a transformative modification of the initial system, i.e., the system one ends up with can be different from the one started with. A good example of this is the map of Europe before and after World War II. The war began with England and France’s response to Germany’s invasion of Poland. The initial goal was the preservation of the sovereignty of Poland. In the end the German’s were defeated but Poland was lost behind the Iron Curtain. Notice how the adaption can have beneficial effects but may not necessarily result in the desired goals being met. This is a good example of the riskiness associated with working in the realm of chaotic systems. It still is better than trying to work in a deterministic fashion on a dancing terrain. Do you remember CompuServe? It had a chance to buy AOL, felt satisfied with being the big dog in business computing, stuck to a linear model, failed to adapt, got bought by MCI and now is a part of Verizon’s network.

Complexity

Complex systems are a special type of chaotic system. They display a very interesting type of emergent behavior called, logically enough, complex adaptive behavior. But we are getting ahead of ourselves. There’s a need to back up a bit and describe a fundamental behavior that occurs at the granular level and leads to complex adaptive behavior. It is self -organization.

Self Organization occurs when the individual components in a chaotic system come together to work as a team to achieve the desired goal. Remember the non-linear component of chaotic systems? This applies during self-organization and means teams may form, work for a while then fall apart and reconstitute in a different form when an obstacle is met to keep on moving forward.

Complex Adaptive Behavior is the name given to this forming-falling apart-reforming-falling apart-… behavior. Specifically it is defined as many agents working in parallel to accomplish a goal. It is conflict ridden, very fluid, and very positive. The hallmark of emergent, complex adaptive behavior is it brings about a change from the starting point that is not just different in degree but in kind. In biology a good example of this is the emergence of consciousness. Another example is the Manhattan Project and the development of the atomic bomb.

Back to Linearity

The development of a complex system within a chaotic situation has a big plus. Complex systems can cross over into predictability where the newly developed rules work, e.g., the actual development and delivery of the atomic bomb. Remember the equilibium-disequilibrium talked about in the previous blog?

We now have a good basis for moving forward. In future blogs we will draw upon both the vocabulary and frame-of-mind presented here to look at how one leads in chaotic situations.