big-data, gut-feelings, intuition

The argument comparing the usefulness of big data vs intuition needs some clarification of definitions and thought experiments.

A thought experiment:

“One picture is worth a thousand words”. Let’s suppose that the word is a an entry into a field (tuple).  A thousand words would be a large number of fields to describe each object.  As for the picture, I’m assuming that the saying originally started with a gray scale image.

Now, if the above statement is true, then a ‘color’ photo would give even more information. To continue in order of increasing information:

1. 1000 words

2. grey scale photo

3. color photo

4.  color video recording

5. 3D color video hologram

6. 3D color video hologram with olfactory input (may clarify a sound or action)

7. # 6 above with a secondary database of the history of everyone in the photo, every piece of equipment available, and all hearsay concerning such

8. #7 above with understanding of subtle body language of people in the picture—something like a ‘tell’ in poker

9. #8 above with a background in multiple disciplines

10. #9 above with the ability to ask terse, insightful questions of others gaining important information from them

Number 10 would qualify as a basis for gut-feeling or intuition.

But, how about the gambler who has a ‘gut-feeling’ that the next role of the dice will be 7?  That gut-feeling would certainly not be called intuition.

So, intuition would have access to much more data that may not need a rigorous mathematical analysis to clarify a situation.  However, quantitative analysis concerning parts of the data that is used in the intuition would be very helpful as has previously been stated by others.

Another thought experiment:

Let’s take an person who already knows how the world works and selectively integrates data to support prior beliefs (hard not to do).  The intuition, in this case, is not using all the available data…any small piece of data that might significantly change the analysis. A database (hopefully) does not discard data, even though it selectively acquires it (which may or may not be worse).

How about a person with Asperger’s at a cocktail party. An autistic person at a comedy show?  Lots of data is not absorbed. A list of people attending the party might be just as good as a report from the Asperger’s attendee; and a list of jokes from the comedy show might be just as entertaining to the autistic person.

About Brian D Gregory MD, MBA

Board Certified Anesthesiologist for 30 years. TOC design and implement for 30 years. MBA from U of Georgia '90: Finance, Data Management, Risk Management. Practiced in multiple US states and Saudi Arabia at KFSH&RC and KFMC Taught residents in two locations. Worked with CRNAs for 20 years.
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