#144: Fake Science and the Explanation Vacuum

August 9, 2018

You’ve had your fake news, now get ready for your … Fake Science.

 What is it, and why do we have it?

I.

Modern people are generally agreed that hypotheses respecting natural events must be confirmed before being believed. Such confirmation is expected to take the form of prediction with consequent empirical verification. But this formulation conceals an important complexity. We tend to be so focused on the verification of simple empirical hypotheses that we forget that methodology must itself also be confirmed. Descartes was the first to draw our attention to the importance of method in the acquisition of knowledge. His views on scientific method can certainly be criticized, but not his extraordinary insight that knowledge-seekers must be reflectively and critically aware of the method(s) they are applying.

This insight is as important today as it was in the early 17th century. The reason is that the successes of empirical science have made it an attractive camouflage for academic grifters, purveyors of fake science. Hence we find the existence at most universities of faculties of “social science” and departments which falsely designate themselves as sciences. The most transparent and egregious of these bogus sciences is the ever more popular “Political Science,” ubiquitous among 16 year old hormonal girls aspiring to be social justice warriors. The use of “logy” as a suffix is also an attempt to borrow empirical science’s prestige, the suffix standing for “theory of.” Thus, we have “logies” like Psychology and Sociology and Anthropology. “Theory of” has seemed so remarkably powerful that some at the academy have introduced departments simply called “Theory.”

And, of course, now we have (drum roll) … “Climate Science.” (Ta Dum!)

II.

The effort to pass for “science” is not limited to only the borrowing of a word. The effort also involves taking on the trappings of fake science methodology.

There is no doubt that the progress of the empirical sciences has been in the largest part due to the progress in our ability to render qualitative changes in quantitative terms. The history of empirical science has been largely determined by the increasing power and sophistication of mathematics. But this has made it natural for the imposters to couch their doctrines in numerical terms.

So, how exactly do the snake-oil salesman coming into town package their fraudulent products? Well, they wrap them up nicely in paper featuring lots of “scientific” looking symbols and especially ones “borrowed” from math. Two areas of mathematics have been particularly useful to the academic grifters: statistics and computer modeling.

If statistical reporting were disallowed, the so-called “social sciences” would virtually disappear. They exist on the basis of the endless reporting of correlational data without any discernible causal significance. This is nothing other than fake-science and I call such “data” scientistic rather than scientific, where my term intends superficially resembling science without actually being science. That the “results” of these “studies” are scientistic rather than scientific is borne out by the fact that, in psychology at least, over half the “results” of published papers are not reproducible [https://futurism.com/study-finds-half-psych-research-not-reproducible/]. There is not the slightest doubt in my mind that this is as true for the other “social science” disciplines as it is for psychology. And reproducibility of results is a necessary condition of a study being scientific!

But I digress. People have been suspicious of statistics for quite some time, nothing really new here. Mark Twain famously distinguished lies into “lies, damned lies, and statistics.”

Just to forestall the high-pitched screams of protest from real scientists who use statistics in real science, I do not mean to deny the real utility of this mathematics in a wide array of applications. However, it is precisely the fact that such genuine uses exist that makes it possible for so many empty correlational “studies” to be published. It is the focus, determination, and effort required to distinguish the real from the dross that allows fake science to survive. And the fake can be found not only in the “social sciences,” it can also be found in important places like medicine.

III.

Computer modeling is, however, less widely recognized to be an instrument of academic grifters. And what makes it more insidious is that its position as a legitimate scientific methodology is rarely challenged. Who would challenge it? Academics? And why would they challenge something so very useful in cranking out nonsense subsidised with enormous research grants? Perhaps computer modeling is “just too big to fail.”

What is a computer model? It is nothing other than a “virtual” simulation of some natural sequence of events. That is, it’s an effort to reproduce the circumstances of some feature of the natural world in the form of digital information. The digital information includes facts of some initial state along with a set of putative natural laws governing those facts.

Thus, for example, we could attempt to identify the relevant starting facts of the 1929 market crash and enter them into a computer database. We could then enter what we considered to be the “laws” acting upon those starting facts. And, finally, we could “run” this simulation and check whether those data and those “laws” actually yield a market crash on the computer.

The methodological fantasy here is that once we have adjusted the model so as to produce the wanted effect, that we can then proceed to use the model to actually make predictions in the real world. But having once adjusted the starting facts and so-called laws precisely to yield the wanted effect, we have absolutely no assurance that this model would work in any other situation. The second we change the starting data assumptions, all bets are off!

Computer simulations are in current use in weather forecasting, economic forecasting, commercial makeup testing, automobile aerodynamics, new drug development, as well as many other places.

But is this scientific reasoning or scientistic? I suggest it is often the latter. Why? Because it regularly fails the most fundamental criterion of scientific methodology, namely predictive success.

Just as we appraise an empirical hypothesis by its ability to make true predictions, so we must appraise a methodology by its ability to make true predictions.

Computer modeling has notoriously been a failure. It’s predictions, for example, with regard to the directions of hurricanes could have been better made with tea leaves. With respect to the economy, one would be advised not to bet the farm on its advice. And, particularly visible, have been its failures on climate change predictions.

[Hat tip to friend A.W.: https://www.thenewamerican.com/tech/environment/item/29748-failed-predictions-of-climate-alarmists-make-future-predictions-suspect%5D

The methodology stinks. It stinks because the variables in real world applications far outnumber any possible computer database capacity and because the “laws” have not been adequately identified and/or refined in detail.

In short, the simulations are not and cannot be adequate representations of reality. As long as that is the case, the method must remain too suspect to employ scientifically.

IV.

It is one thing to recognize the existence of fake science, it’s another thing to account for its presence. Why and when do we wind up with fake science?

The answer, I think, lies in the existence of a gap in the reach of real science, a kind of “science vacuum” which draws the spurious imitation science in.

There is a natural need in human beings for explanations of natural events. But what must be understood is that this need is visceral rather than cognitive. Human beings are not intellectually critical with respect to their needs, not with respect to their appetites, and not with respect to their psychological needs. When we’re hungry or in the mood, we eat, and we mostly eat whatever is at hand, whether it’s “good for us” or not. The same applies to our need for explanations. We’re really pretty easy to satisfy. Before scientific explanations were available, people made do with fantastic ones provided by priests. These stories were utterly useless as predictors, but they had social/political usefulness for the priesthood and they filled the explanation needs of the credulous population at large more or less.

As empirical science progressed, it filled human explanation needs more and more, but regions continued to exist where science simply could not explain to the point of accurate prediction. It is into those regions that fake science penetrates. Climate change at this point in time lies beyond the abilities of predictive science simply because of the enormous size of the phenomena involved. And, as always, such a situation gives rise to a priestly class which thrives on cultivating an “explanation” narrative with all the old hallmarks of religion. It does so in this case because the new religion has the potential of enriching its practitioners beyond all imagination. To paraphrase Rahm Emanuel: Changes in climate are a natural phenomenon simply too good to waste!

Yes, Virginia, this is a religion. There is a deity to worship (Gaia, the Earth); there is human guilt (population, fossil fuels, flatulence); there are hated non-believers (Global Warming Deniers); there are the priests (the Global Warming “scientists”); there are oracles (the computer models); and there are scared texts (the reports of the UN climate change grifters). But it would still be naïve to expect a modern religion to come in the familiar trappings of the old ones.

When we think of religions, we expect a supernatural invisible god, we expect rituals of a certain antiquated kind, we expect special buildings of worship, and so forth.

When we look at this modern religion, we cannot look for those in their familiar form, we must look for their analogues. But what remains the same is 1) the absence of genuine predictive science, and 2) the full panoply of emotions which have always attended the blind want for explanation.