Knowledge is not enough, insight must follow

Knowledge is freely available and acquired through study. Given the huge proliferation of MOOCs, this has become widely disseminated and available at minimal cost should someone hunger for it.

Gone are the days, when you went to a music tutor for lessons. One can now avail outstanding material on the world wide web, browse through material, and videos, listen to the masters, even practice structured lessons all the while enabling one’s own learning on self paced basis. This is one big change that is happening today. In the past, knowledge was hoarded, passed down from one cohort to another through long years of internship and inspection the key being to ensure quality. Examples are the Gurukul system, or the German Masters training their protégés. Today, Universities with Libraries, action research and conferences allow for the broadening of knowledge.

To be competent in a field requires Knowledge, but that by itself is not enough.

It needs application of that knowledge. Herein comes the issue of theory versus practice. How best to deploy that knowledge to create impact. It is only through insight that knowledge can be trusted, when deployed effectively and payoffs are seen. Many of us have experience in Change Management, reading post Kotter etc but that is practically useless unless deployed in the workplace and the results seen.

Having knowledge and deploying it allows for insight. This is a critical stage. When we rely on insights acquired through experience we can build confidence in what we do.

In Nayan Philosophy, true knowledge is that which is acquired from observations, directed outwardly (through our sense organs) and inwardly ( through observing one’s own thoughts and emotions, aka self awareness. Through this Inference is made. By this I mean, we can assume fire on a hill if we

See smoke. The object (hill) creates a major observation (smoke) that leads to a universal knowledge ( smoke means fire) then applied to this hill ( this hill is on fire). This is inferential knowledge: Inductive, deductive or associative.

Knowledge also comes from comparison. Some one says to you, when you go to the forest on a jungle safari look out for the barking deer: it is a deer, but barks like a dog. Even though you have no image, this shared knowledge from someone who has seen this animal before would help you recognize this animal were you to spot it. Another example would be if someone should you a hill and requested that the temple be built similar to this structure: Bingo, you would have an idea of a pyramidal structure. What we see, confirms our views: the tents gave way to house with triangular roofs, or the dome shaped tents gave way to dome shaped structures.

This far I have dealt with observations, Inferences and comparsion. I now move to Sabda: divine knowledge. These are the knowledge from scriptures and to be accepted as truths passed through generations. All other truths are secular testimony and may not be truth. We have seen how the Eucliadin view of the world gave way to the Newtonian view, to be replaced by Einstein’s relativity. Hence even scientific discovery are observations sharing truth observed by the observer. This observation expands with new discoveries. Science only offers more perspectives to us: it at best responds to the questions it asks of itself. Change the question and you change the observation.

In summary, knowledge is free. Useful if supported by insight. This experience counts. Experience that does not build insight, is wasted experience. Through this experience wisdom is gained. Through this foresight is born. The future becomes clear through foresight.

Knowledge dispels darkness of ignorance. Knowledge must be acquired through verification by testing (personal experience). There is no truth in beliefs. What is known is known. The known then used in unknown situations is insight. It is hoped that we all gain foresight through practice.

Let's now review Cause and Effect from more recent work: the use of Causal Inference or DAG.

Judea Pearls Rules:

1.     Two variables are d-separated if all paths between them are blocked

2.     Two variables are marginally or unconditionally independent if they are d-separated without conditioning.

3.     If however, the collider is ‘conditioned’ a back entry is possible.

Translating English to Causal Graphs or DAG (directed acyclic graph)

Causation and Association are different.

i.  Cause and Effect    A----> Y                  

      In figure I, A causes Y.

                                             I-----------------------------------I

 ii  Common Causes     L (parent)---->  A (child)       Y (grandchild)    

In figure ii, you could have common causes for Y occurring. For example, you have L (smoking) causing Yellow Fingers (A), but there is no evidence that Yellow Fingers causes Cancer (Y). You may also have causation where Smoking (L) causes Y (Cancer)

                                                               I--------------I

iii.  Conditioning on Common Effect .          A          Y --- >L      

in figure iii, there is a collider L, is an effect by two common causes, Y and A.

iv. Chance - this can occur if the data sample is small, but gets eliminated with larger data sets.