In this post let us try to classify and understand scientific disciplines based on the degree of complexity. We get many exciting inferences and correlations by such a classification, and hence I thought of publishing this idea in this blog.
Let us define 'complexity' as the degree of involvement of fundamental units of nature, considering maximum functional efficiency. What I mean by fundamental units is that they are the basic units of nature which cannot be further simplified by any known measurable quantity. So what are these fundamental units? Can it be those units which are dependent on others for its own existence? For example time which is considered as a fundamental unit cannot exist by itself independent of space. This makes time and space dependent on each other to maintain the constancy of the speed of light. There is much to be thought in this line, but let us restrict this post to scientific hierarchy.
I do not know if we can start from the universal constants like speed of light (c), Avogadro's number and Boltzmann's constant which can be set to a fixed dimensionless number, say to unity. But prior to any attempt of theoretical justification of this philosophy, let me take it granted based on some intuitive grounds. So the hierarchy goes roughly like this,
Universal constants (eg. speed of light in vacuum) → Fundamental units (eg. time, space, mass) → Atoms → Inorganic Molecules → Organic Molecules → Genes → Cells (including unicellular organisms) → Multicellular Organisms (In the order of evolutionary hierarchy) → Animal Memes → Human Memes
This particular order is obviously rough and crude, but gives a broad idea of what I intend. It can be further simplified to this order,
Physical Hierarchy → Genetic Hierarchy → Memetic Hierarchy
There is much thought in such an attempt to unify all the natural science in the order of complexity, because, for example, human beings who are most complex in terms of memes (mental cognition and neurological complexity) may not be genetically most complex (as per our present understanding of genomic functions). But if such a linear order of complexity is justified in future, human beings must also be genetically most complex in terms of genetic efficiency (See C-Value Paradox).
Assuming such a linear nature of complexity, considering functional efficiency, let us try to highlight a few interesting inferences.
Classifying scientific disciplines based upon such a hierarchical classification roughly leads us to,
Mathematics → Theoretical Physics → Nuclear Physics → Inorganic Chemistry →Organic Chemistry → Genetics → Microbiology → Tissue Culture → Agriculture → Psychology
Again my confession for such a crude order, because just in the subject of psychology comes a vast area of study from personal to global behavior.
Observing such a classification closely, leads us to some important inferences which have high philosophical and scientific values.
1) Scope of Influence:
What I mean by the scope of influence, is the maximum limit a technology can influence a natural system. The lower we go in the scientific hierarchy the higher is the scope of influence. The influence may be either negative or positive. For example, the scope of Psychology in the negative side may be an organizational or political collapse which does not cause much harm to any units which are present in the lower scientific hierarchy. At the same time the destruction that can be caused by nuclear physics (eg. nuclear bomb) is capable of destroying everything in the higher level of the scientific hierarchy but not the fundamental constants of universe. This kind of influence is very well seen in our daily life too. For example the genetic contamination through improper use of rDNA technology may result in a more catastrophic impact than the negative impact caused by traditional agricultural practices. As the old saying goes, if the foundation is cracked the whole house falls. Thus if mathematics is destroyed, say when odd + odd is not equal to even, then the whole natural system will be destroyed according to our percent understanding. At the same time we can also understand the same philosophy on the positive side, on how we can modify the fundamentals for our own benefit.
2) Independent of size, lifespan and mass:
Our hierarchical classification is based on complexity (according to our definition) and thus independent of size, shape and lifespan. For example a tortoise may live longer, blue whale may have much larger brain, or a planet by itself may be billions of times heavier than human, yet humans stand higher in our classification. Similarly a planetary system is lower in order compared to any form of life. Another example that justifies this inference is C-Value paradox , which highlights the incoherency between genomic size and evolutionary hierarchy. This inference is purely a result of our definition of complexity.
The more complex we go, more irregular it is. What I mean by irregularity is that it is non-cyclic through space and time. One may argue that Brownian motion which must lie in the lower hierarchy is random and irregular. But the reason of its randomness is due to the motion of many atoms (and molecules) which is governed by a few laws of motions. At the same time imagine a box with hundred ants, its motion will be much more random and complex compared to the Brownian motion.
Predictability is basically guessing the nature of a system at a particular time in future. The precision of prediction decreases as the complexity of a system increases. For example we could precisely predict the behavior which involves just physics, but most difficult to predict the nature of psychological behavior. We could exactly predict the position of Titan around Jupiter a thousand year from now, at the same time we cannot predict the stock price of a company or the economic situation of a country or even the psychological behavior of a person an year from now. More evidence comes from the tendency to use pharmacological drugs, chemical fertilizers, genetic engineering while compared to Ayurveda, organic fertilizers and Agriculture respectively.
I believe there may be much more inferences that can be driven from our model. At the same time I do not claim that our models as well as our inferences are completely right.