Is Science becoming too hard even for scientists?

[alex3619]

The idea is that within a given discipline, the area is well explored and the knowledge has been well exploited. However, at the boundaries between two fields there is still uncharted territory that, when waded into, often leads to fruitful new understanding, which within the pharmaceutical industry may lead to the development of a new drug.

I saw this happening in the early 90s. Knowledge is cut up into little areas, people specialize in those areas, and benefits often exist from combining those areas. Specifically I saw it in neural networks and something else. I forget the something else, but in neural nets it was about combining our understanding of genetics and neural net design, which dated to 1988 and was overlooked. In my PhD candidacy one of the things I was looking at was symbiosis and neural net designing, drawing on ideas about mutual coupling developed by Humberto Maturana.

 

[Eeyore]

I do think that right now in medicine there is a problem of not being able to see the forest for the trees. Our medical system is designed to break down and categorize disease by system (nervous, gastrointestinal, cardiovascular, etc.) Everyone is seeing one aspect of the illness, and missing the others. The immunologist sees an immune system problem, the infectious disease specialist sees an infection, and the neurologist sees neurological deficits. We need to look at the big picture - and we aren't good at that anymore.

 

[JaimeS]

I do think that right now in medicine there is a problem of not being able to see the forest for the trees. Our medical system is designed to break down and categorize disease by system (nervous, gastrointestinal, cardiovascular, etc.) Everyone is seeing one aspect of the illness, and missing the others. The immunologist sees an immune system problem, the infectious disease specialist sees an infection, and the neurologist sees neurological deficits. We need to look at the big picture - and we aren't good at that anymore.

The Elephant and the Raja, or the Elephant and the Blind Men, or whichever you choose to call it...

Or, if you prefer, in poetic form.

-J

 

[Eeyore]

Yup, exactly @JaimeS.

 

[Stretched]

Add "unnecessary complexity in statistical analysis". Another way of spinning results, while reducing the number of potential critics.

Bingo, spot on!

If a topic is explored then reviewers want it quantified for more credibility. Or,
if quantified, it gets challenged by some 'superior' data (always open-ended).

Neither the author nor the critic prevails. One simply yields for perceptual reasons as to who has the better goods... . Wasn't there a book ~' Lying with Statistics?'

 

[Eeyore]

It's true that those who do not want to believe something will always ignore the evidence and find a reason why a conclusion is flawed.

Unfortunately, even our "greatest scientists" are very often close minded, linear thinkers. The number of people out there who can work outside the box while remaining scientific is not high.