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Reality and What it Means

As was recently posted on LinkedIn by Dr. Ya'akov Sloman,  "Scientific Realism, in the general case, is the idea that theories developed by the scientific method match what is "really" in the world. That is, if a theory has predictive value it means that what the theory describes actually exists in the world. In the particulars, every philosopher who considers this idea has variations on it, but the core is the epistemic certainty, the idea that what we know from successful scientific theories (those with predictive power) is not just a model that is internally consistent, rather it is a direct description of things-as-they-are." Is it possible to justify this position without resort to simple optimism or outright faith-based arguments?"

Sounds long and philosophical.  Yet this simple post generated a blizzard of response.  Indeed, if you've been reading this column, there is almost nothing true or what you believe is true without some belief or faith involved.  See http://used-ideas.blogspot.com/2013/02/problem-solving-your-marvelous-brain.html
Your most important problem solving tool is based on belief. 

I responded with, "One question is this: are theories developed by the scientific method or does the scientific method support theories? Other theories, such as chaos theory, are almost unverifiable but surely are useful."

That said, what about reality?  Surely, that is that, that is this, and nothing more can be said. Facts are facts, and truth is truth.   Yet we have, "One question is this: are theories developed by the scientific method or does the scientific method support theories? Other theories, such as chaos theory, are almost unverifiable but surely are useful."

The conversation drifted toward the notions of global warming, and even on the erudite site there were massive differences of opinion, i.e. belief. 
I ventured forth with my typical skepticism,  "Perhaps global warming, anthropogenic or cyclic versions, is a good test subject question of this post. For proponents or not, there is the reality of the evidence, but also the reality of how one interprets the evidence, and finally how one makes predictions upon that interpreted reality. In the warming case, complex statistical models are used on past data to predict into the future. So, the question is turned to one's believe or faith in statistical models one don't understand upon any basis of reality or observation to generate yet another reality - to predict the future or to convict culprits of the past, i.e. us.

This does create something of a paradox. Use real data - which you believe - plugged into a mostly incomprehensible black box which in turn ships out predictions or conclusions - for you to believe. (Forget that weather is a thermodynamic process, making predictability problematic, even over the next week or so.) This means the realist used a mixture of faith and fact. So, if one can't remain just a little skeptical, then we must have some dictator in charge of truth. "

The conversation drifted this way.  And with such a topical and really emotion issue, it lingered.  My latest post was this.  Opinion was this way and that way - and that way and this way - never was there a consensus on which way to go. 

"I feel certain that Dr. Sloman's original post has been lost in the debate about climate change. Yet, this topic has engendered a great deal about how perceived reality interacts with an accurate description of theories explanations. Many, many theories on many topics have be posited toward this end. Climate change theory is emerging. Controversy about many new theories have been similar. Historically...

(1) Someone mentioned the earth orbiting the sun - or vice-versa. There was a time, not long ago that Ptolemaic theories ruled to the latter, and almost all scholars agreed. Even Copernicus was reluctant to challenge this theory, publishing his work upon the year of his death in 1543. This is despite that even some ancients advocated the former, e.g. Hipparchus. It took millennia for the current theory to take hold, and probably only then with the monumental work of Newton, whose work made so many accurate predictions that "denial" became impossible. Note the word "denial."

(2) Blood letting was once an accurate and acclaimed theory for the reduction of fever's temperature. It worked! This theory was thought to be sound, and many if not all medical practitioners agreed. Denial meant castigation. To me, it is a good thing politicians did not adopt its tenets, as we may still have it with us.

(3) When quantum theory and quantum mechanics was first theorized by the early practitioners, it was soundly rejected. It persisted, and again, it made such accurate predictions that became impossibly difficult to refute. Not exactly observable reality, but for the confounded double-slit experiments.

We could go on with cells, bacteria and the whole panoply from current biology. We could also mention plate tectonics. All of these eventually generated irrefutable evidence. They took years, if not decades, for acceptance. The vetting process was neither brief nor was acceptance acclaimed by fiat. However, there is a new theory on the block.

(4) M-theory. This is a wonderful venture into the effort to achieve a unified field theory. It is marvelous. It posits that depending on the scale (time or matter or distance) there may be different, even contradictory theories. The only requirement is that they agree at the scale interfaces. I do believe that M-Theory may be a new paradigm for conceiving of and understanding of new accounts of reality. It is a fall-back plan and a serious model - for now.

All of these impact the ideas of reality rendering to us "things as they are." Yet, it must be observed that many of things as they are "are" by inference and by the power of their success at prediction. Toward this end modeling has proved paramount - but statistical modeling is rife with "fudge factors," often called tuning parameters. These models are truly suspect, but intoxicatingly attractive.

As a final note on statistical modeling, I have such a model for something simple. It is the modeling of a typical professional baseball league's season standings. My simple model, truly so, is so accurate that every statistical test I've applied suggest it is the way baseball works. But there are countless factors, such as efficacy, motivation, winning or losing runs, temperature, etc, not included. Yet, it is just a toy model."

My case is just that.  When a new science or endeavor reaches the light of day,  its issues are just beginning.  Once you stand before the stage lights, every aspect of your performance is measured.

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