Random reflections on CHAOS

What I “learnt” about chaos in Natural systems!


Every natural system evolves over time. It inherently has randomness, complexity and chaos as its intrinsic characteristics. This is the symptom or a necessary condition for an emerging system. These systems have agents in them, which or who interact with their neighbors and acquire knowledge with the feedback they get. The negative feedback forms a very important part of the feedback. As, it acts as a benchmark and enhances evolution and development.

The system, on the other hand has “order” in chaos. This order can be seen as dominant patterns, which keep repeating themselves in the system, these are called the fractals. ( I should still read more about this). In a “normal” natural system, there is always a conflict to establish balance between this “order” and “chaos”. This conflict, leads to evolution. In one sense, conflict is inevitable in a natural evolving system. Therefore, a natural system normally “lives on the edge” rather “lives on the edge of chaos”.

It generally exists or moves towards an equilibrium state in chaos, called “Lorentz’s Strange Attractors”. Whenever, there is a “disturbance” in the system, it means that there is deviation from regular chaos and the system faces excess of chaos. Now, system tries to regain back to its attractors or it goes to another plane of chaos and establishes in new “Attractors”.

Now coming to the examples of chaotic system, we can say every naturally “evolving or emerging” system is chaotic Viz. a nation’s economy, immune system in a human body, cities, weather.
It is difficult to model these systems or predict them. Moreover, even if we model, we can only prepare a “map” not a “territory”. Chaos theory reinforces the un-deterministic nature of a natural system.

As such, Chaos has its roots in non-linear mathematics. However, it is a field, which integrates all the natural and social sciences, because chaos is a phenomenon that exists in every natural system. The proponents of chaos theory are against “over specialization” existing in the present day research. They say that, this leads to certain assumptions in model development that makes models less efficient.