When we play, we do not ask why we are playing ... we just play.
Play serves no moral code except that strange code which,
for some unknown reason, imposes itself on the play.
You will search in vain through scientific literature for hints
of motivation. And as for the strange moral code observed by
scientists, what could be stranger than an abstract regard
for truth in a world full of concealment and deception.
Can it be that all the great scientists of the past were really playing a
game, a game in which the rules were written not by man but by nature?
In submitting to your consideration the idea that the mind is at
its best when playing, I am myself playing. And that makes me feel
that what I am saying may have in it an element of truth.
John Lighton Synge
Mathematical systems are internally consistent artificial systems with their own complete reality (though clearly, not real reality).
But, Godel's proof lets us know that a mathematical system can't be both internally consistent and complete (sigh...)
Keep in mind, though, that mathematics for its own sake isn't the goal in applied mathematical fields like Statistics and Psychometrics. We must always be able to relate the mathematics to reality (the real reality) so that we can determine its usefulness. And when the further development of a mathematical system isn't useful we should move on to something else. (See my adobe acrobat notes on The Future of Psychometrics).
With programming, things are clearly right or wrong --- no gray --- all black and white.
Unfortunately, that's not really true at advanced levels. With complicated programming systems all you know for sure is that the system isn't right! The probability that there are bugs is so close to one that you just know they are lurking around somewhere!
I think the biggest problem in the social sciences is that researchers either study meaningful questions sloppily, or meaningless questions carefully.
I believe that Data Analysis and Visualization can help researchers in the social sciences optain carefully reasoned answers to their carefully constructed scientific questions: Meaningful answers to meaningful questions.
I enjoy Data Analysis and Visualization because I enjoy helping others find meaningful answers to meaningful questions.
What I enjoy most is inventing methods that have the potential to improve and expand the kinds of meaningful answers that people can obtain to their meaningful questions.