Metaphors and analogies exhaust themselves. No matter how useful they are as guides to thought, pushed too far they can loosen our grip on Reality or lead to stagnation, and they can even cause error to be accepted as Truth. All agree with this.
I learned from the Tree of Woe a nice metaphor about analogies, Philip K Dick’s notion of the Black Iron Prison. Our metaphors and analogies form “a system of control over thought.” We are trapped behind bars that we ourselves forged. What makes it worse is that Dick said of some of his characters, which are clearly the same as us, that we are all inside the Prison and none of us know it.
Yet, now, a few do know. It’s clear to some that we have gone as far as we can with our old metaphors and analogies. Rather, that we have gone too far with them. This is so for the culture at large, where our self-wrought mental prison is wreaking havoc, but it also includes science, where the damage caused by old metaphors and analogies is less clear.
I believe most (regular) readers are with me on this. Disagreement comes only in what counts as the one step, or the last leap, too far, the point at which the analogy snaps and thought founders.
We have looked through the years at hundreds of examples of bad science. Some of it is where the metaphors and analogies are not in dispute, like math and data mistakes or fraud. Rank error or cheating to get the results you want is obviously bad philosophy and rotten science. Some is where the metaphors and analogies are in dispute, though, like laws of nature and anything to do with probability. Differences here are profound, and we have a lot of work ahead of us to convince skeptics of our philosophy.
Let’s start easy. Today, evidence research in most fields has petered out. In later posts we argue for a change in fundamental philosophy.
Quite a lot of bad science is not so much bad, as boring. And useless. Many journals exist for sole purpose for academics to publish lest they parish. No harm would come, and a great benefit would incur, by immediately pulping these journals as they left the press, or in diverting the electricity used to host their sites to mine bitcoin instead.
These non-entities are of no real harm in and of themselves, at least individually, because most are ignored, and should be. However, they do cause problems when they are massed together. Because of funding and peer review, each non-entity must still be “peer” reviewed and funded, the whole adding to the burgeoning bureaucracy. The mass makes it too easy for rulers to find “the science” which they want to support, too.
These works are all also beholden to the old metaphors and analogies, and their great bulk, married to the Voting Fallacy, gives the false impression that because so many people are working under these premises, there must be something to them. This leads to a great slowing down, even as, seemingly paradoxically, the number of researchers increase.
The paradox is resolved by recalling that the metaphors exhaust themselves and that the more researchers there are, necessarily the dumber the average intellect among researchers.
As empirical evidence of this, enter the paper “Are Ideas Getting Harder to Find?” appeared in 2020 by Nicholas Bloom and others in American Economic Review. Here’s the Abstract:
Long-run growth in many models is the product of two terms: the effective number of researchers and their research productivity. We present evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore’s Law. The number of researchers required today to achieve the famous doubling of computer chip density is more than 18 times larger than the number required in the early 1970s. More generally, everywhere we look we find that ideas, and the exponential growth they imply, are getting harder to find.
Now this is put in terms of economics and something called Total Factor Productivity (TFP), so there is some scientism built into this, but not so much we can’t find something useful. See, too, links to many other studies on the same question. The results are always the same: we’re bottoming out. I highlighted another of these (blog, Substack) on the fading away of “disruptive science” (paper details at blog, Substack). The reasons for the slow fade are those already given.
Of course, trying to quantify precisely quality research output is doomed to fail, the idea not being amenable to strict measurement. Whereas numbers of researchers can be given a better (but still imperfect) number. So this picture of theirs is only crude:
I don’t want to make too much of these numbers, because even if they could be accurate quantifications, they’re not identical in every field. This is a good cartoon of the situation, though.
An area in which measurement is a lot easier, and in less dispute, is Moore’s Law (so-called).
More and more researchers are required to get the same return. But note the direction of causation cannot be inferred, and can go both ways. More researchers, poor ones, are bogging things down as well as helping.
Another area with easier quantification is crop output.
They put a smoother on the data, which annoys me. But you have the idea. The story is the same.
The situation is somewhat better in cancer and heart disease:
But the “Years of life saved per 1,000 people” looks like it’s bottoming out. Note the different time scales here. The evidence into the 2020s indicates the mortality rates have also flattened, and may have grown worse. The covid panic accounts for some of this.
Here’s a better way to look at it:
Again note the different time scales. Most of this, too, is really before the woke hit and we got “pregnant men” and other medical insanities. So it will be worse by now.
The authors conclude, and we agree qualitatively:
Our robust finding is that research productivity is falling sharply everywhere we look. Taking the US aggregate number as representative, research productivity falls in half every 13 years: ideas are getting harder and harder to find. Put differently, just to sustain constant growth in GDP per person, the United States must double the amount of research effort every 13 years to offset the increased difficulty of finding new ideas.
One last cartoon, from the paper “Papers and patents are becoming less disruptive over time” mentioned above, by Michael Park, Erin Leahey and Russell J. Funk.
Their quantification is as sketchy as the others, so not much can be read into the absolute numbers. The idea, however, is plain, interesting “disruptive”, meaning truly new, work is waning. Notice that this ends in 2010, also before the woke really degraded things.
My post is not a knock-out proof that our old metaphors and analogies have been milked far past dry. But it is reasonable evidence in the direction. More to come.
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