The St. John Cathedral Oak
Part of the eerie beauty of south Louisiana are the trees, particularly the live oaks which can achieve breathtaking size. If you are in Lafayette, it is worth a visit St. John’s Cathedral just to see the 470 year old Cathedral Oak. The Cathedral is located on what was originally the site of the parish church, selected in 1821 specifically because of the oak.
Not many people know, but oak acorns are edible, and a large tree can produce 200 or more pounds of acorns in a good year. The bitter tannins in the acorn can be leeched out by soaking and boiling.
Even fewer people know that there are a few oaks that produce sweet acorns, which can be eaten as is. But you have to find the sweet acorn oaks, you can’t buy the trees. So far, no one has been able to breed them.
There are several reasons for this, but two are pertinent to our conversations here. First, unlike almonds which require a mutation in a single gene to become edible (and for almonds at least, non-poisonous), the bitterness of oak acorns comes from a number of genes, and different combinations of mutations do not reliably produce sweet acorns.
It’s variable feedback: if you take two sweet acorn trees and cross them, you might not get trees with sweet acorns. In everyday terms, variable feedback means that if you act on a problem today, sometimes it works, sometimes it doesn’t.
But the larger problem with oak acorns is delayed feedback. It is not unusual to wait 20 years for an oak to produce its first acorn, an occasionally a tree can take 50 years to begin production. Given business pressures for ROI, and even the long-term length of academic funding, it would be the rare investigator who found the time, who lived long enough, and who was lucky enough to arrive at the proper lineage, to consistently produce sweet acorns.
It is what I call ‘dyschronic feedback’,[1]If anyone knows a better word, I’m open. I considered ‘asynchronous feedback’ and even ‘dyssynchronous feedback’, but rejected them because they seem to invoke other … Continue reading a cause and effect phenomenon that is separated by considerable and highly variable time. Dyschronic feed back is worse than variable feedback, because if I can quickly show some effect, even an inconsistent effect, people will be convinced to at least look further. If we tell then on the other hand, Do something today and maybe – maybe – in 50 years you will get a result, most of them will wave you off.
Together, variable and dyschronic feedback make up loose feedback, which makes it very hard to understand what’s going on.
For instance, we constantly work with dyschronic and variable feedback in medicine. You come in with high blood pressure, we put you on anti-hypertensive medicines. The effects are very hard to gauge; we know that, on average, people live longer with lowered blood pressure, but we cannot tell you how much longer you will live. Even when you die, we have no idea whether you were helped, hurt or unaffected by the medicine. It’s simply a roll of the dice. And it takes a very long time for the dice to come up.
And once they come up, we can’t be confident of exactly what happened.
Similarly, we have talked about the critical role of innovation. We spend phenomenal amounts of public and private money looking for new solutions to problems, and there are no guarantees that research will solve useful problems, nor that it will solve them in a timely matter. We can’t be confident of whether, we can’t be confident of when. We only know that a lack of research will not solve problems.
This was an historical problem in biology. For many years, mathematicians and physicists ridiculed biology, because it wasn’t a ‘real’ science, it yielded no repeatable measurements. It was simply a gentleman’s hobby – all modern science pretty much started out as a gentleman’s hobby – but biology was little more than collecting and categorizing specimens.
That is, it was until Charlie D. came along. But that still didn’t give biology repeatable measurements. For ordinary science[2]See Thomas Kuhn for an explanation of ordinary science. and, I suppose, for ordinary scientists, science is hard numbers, solid, universally demonstrable results that allow one group to yell ‘nanny-nanny boo-boo’ at everyone else.
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So in its origins biology couldn’t generate numbers,[3]A nice rejoinder to metric-minded biologists: Darwin’s Origin of Species contained not calculation one. until statistics came along. It is important to note that mathematicians did not develop statistics, biologists and agronomists did, and they developed it in order to deal with loose correlations, with loose feedback. Of course, since people are also biological, statistics has also proven to be a powerful tool in the behavioral sciences and business; and, in a profound irony, it has become indispensable to the hard sciences, as well.
The problem of fuzziness was always there, of course. We do not really know where the moon, or Jupiter, or the North Star are, we only have approximations. Early scientists took repeated measurements, and intuitively chose something that we would now recognize as an average, a statistical mean. The same is true of all hard sciences; only math is not built upon statistics, because it works in a completely theoretical realm.[4]Mathematics is simply a tool for analyzing reality, and within itself it is completely fictitious.
The point is, people often forget that statistics offers us no proof, only likelihood. In fact, statistical analysis announces, at the outset, not only are the results uncertain, but it announce by how much the results uncertain.
To our deliberations here, statistics allows us to deal with loose feedback, but the conclusions are inescepably fuzzy. And it’s hard to sell fuzzy concepts to most non-scientists. Unless there are dependable, obvious results – preferably short-term results – people don’t tend to believe it, particularly in the area of public policy.
Chief among problems with loose feedback are the hefty investments we need in education, and the great latitude we need to allow talented teachers. We keep making sweeping changes to education, and we don’t involve teachers in the deliberations.
And we won’t know the results of our decisions for decades. Even then, loose feedback means we will have a hard time pinpointing the origins of our problems and successes, and their relative importance.
Loose feedback is a large problem in management, planning, and governance, and we will return to it repeatedly. But the most pressing problem of loose feedback, to my mind, is in trying to understand the long-term impact of changes to our educational policies.
Picture of the St. John Cathedral Oak courtesy of Lafayette Convention and Visitors Commission.
Footnotes
| ↑1 | If anyone knows a better word, I’m open. I considered ‘asynchronous feedback’ and even ‘dyssynchronous feedback’, but rejected them because they seem to invoke other terms – synchronize, synchronic, and synchronicity – which suggest other meanings. |
|---|---|
| ↑2 | See Thomas Kuhn for an explanation of ordinary science. |
| ↑3 | A nice rejoinder to metric-minded biologists: Darwin’s Origin of Species contained not calculation one. |
| ↑4 | Mathematics is simply a tool for analyzing reality, and within itself it is completely fictitious. |