Before we delve into the whole language, misunderstanding and flawed assumption thing (introduced yesterday), it's a beautiful second day of spring here in the southwest Panhandle of Nebraska. Here are a couple of images.
|Western Meadowlark, lookin' for a girlfriend.|
|Soakin' up the sunshine.|
We're supposed to be getting a winter storm tomorrow. That's what spring on the High Plains is all about!
And now, on to the main feature.
Who was it that said the British and the Americans are two peoples divided by a common language?
As it turns out, at least so far as I can tell, the phrase is most often blamed on George Bernard Shaw. However (ain't there always a however?), the story of his coining the phrase is perhaps apocryphal.
Whether the profound utterance of a profound thinker or a bit of doggerel cobbled together in obscurity, it's a great line. If you've ever conversed with a Briton (and conversely, for you British readers, if you've ever conversed with a Yank), you know exactly what the saying means.
So. A few weeks ago over in Sarge's neighborhood, at the U.S. Naval War College in Newport, Rhode Island, a British mathematician gave an Eight Bells Lecture. The speaker was Niall MacKay, and the topic was "A Bayesian Study of the Battle of the Dogger Bank.
You can read about Bayesian Analysis here and here, and about the Battle of the Dogger Bank here and here.
Regarding Bayesian Analysis, it's simply a form of statistical modeling. It can be an astonishingly powerful and useful tool, however, as with all models, its utility is utterly dependent on understanding it and using it correctly, rigorously and honestly.
As for the Battle of the Dogger Bank. Quick aside; the Brits seem to always include the second "the" when referring the battle, that is, "The Battle of THE Dogger Bank." I do not know why. I suspect it is a Brit thing. See the title of this post. Well then. In a nutshell, Five Royal Navy Battle Cruisers and attendant escorts met four (or three, if you discount Blucher, read about it and draw your own conclusions) Battle Cruisers and escorts of the German High Seas Fleet near Dogger Bank in the North Sea on January 24, 1915. The Germans lost Blucher, and Seydlitz was heavily damaged and nearly lost. German personnel casualties totaled 1,223 killed, wounded or captured. On the Royal Navy side Lion was badly damaged and very nearly lost, and a destroyer was damaged. Personnel casualties totaled 47; 15 killed and 32 wounded.
And what, you may be wondering, does any of this have to do with "language, misunderstanding and flawed assumption?"
Well, just watch this. Actually, I don't expect you to watch the whole thing, which is 75 minutes long. Not too long for me, but I find it fascinating. I'm strange that way. The point I'd like to illustrate, which does address the "language, misunderstanding and flawed assumption" bit, begins at 1:08:57 and runs to 1:09:35.
During the Q&A, a fellow in the audience notes that "It sounds as though your (RN) fire control equipment was not up to the Germans."
Yet another brief aside. This is the same fellow who sat through MacKay's entire lecture on Bayesian Analysis as a potentially useful tool in refining the historical understanding of the Battle of the Dogger Bank. He had the first question of the Q&A, which was "What was Churchill doing?" There's always one.
To which MacKay responds that the German fire control was clearly faulty, as evidenced by the terrible flash fire that erupted aboard Seydlitz, which nearly caused the loss of the ship.
|SMS Seydlitz afire during the Battle of the Dogger Bank, January 24, 1915. S|
Which prompted my mind to say, "'Ang on, 'ang on, t'hell are you on about, mate? In naval terms, fire control is about warheads on foreheads! What's this firefighting nonsense?"
Ah. Language. Misunderstanding. Flawed assumption.
Not at all caused by the differences between the Queen's English and American English after all. Sorry, I cheated. Hate to let a great line go to waste. Anyway, the genesis of this misunderstanding springs from the difference between professional naval terminology and civilian terminology.
To his credit, MacKay quickly recovers, and after a brief detour into shipboard firefighting, returns to fire control.
Both topics, in the context of WWI naval battles, are hugely interesting and fascinating. On the firefighting side we have "There seems to be something wrong with our bloody ships today" and on the fire control side we have excellence on the High Seas Fleet side versus the muddled mess of Pollen and Dreyer and the abysmal state of RN gunnery in 1915.
A couple of thoughts are in order here. Well, whether they're in order or not, here they are.
Firstly, I agree with MacKay that Bayesian Analysis is an exciting and potentially very useful tool to add to the historian's chest. The real utility here is the way the tool works. To oversimplify, you take all the parameters which may have led to the outcome, and then analyze them more or less in reverse to determine the likelihood that they actually caused or contributed to the factual effect in the way you hypothesized.
For instance, and to be extremely over-simplistic, let's say I hypothesize that Blucher was destroyed because of the excellence of British guns, shells, and gunnery, and that Lion was saved by the inadequacy German guns, shells, and gunnery.
The factual outcome is that Blucher sank and Lion did not. But when we analyze British and German guns, shells, and gunnery, we find that my parameters (assumptions) were weighted exactly bass-ackwards. The Brits scored fewer hits with less effective shells fired from less capable guns, and the Germans scored very many more hits with far better shells fired from superb guns.
In this case, my parameters were badly flawed, but the tool allows me to refine them to close in on more likely causal parameters. At the end of the day, this tool, if used correctly, can in some cases bring us closer to the truth of what actually happened, which is of course the whole point of history.
And yes, this is what historians are already doing. I get that. The potential utility of Bayesian Analysis and other mathematical/computational modeling approaches is twofold. On the one hand, computers allow modeling to be done on huge scales and at great speed. On the other, they offer an opportunity to escape the trap of bias. Both of these things are good, good, good.
The tool can be misused, of course, and MacKay takes pains to point this out from the beginning of his lecture. One must input the correct data (garbage in/garbage out) and apply real and appropriate scientific and mathematical rigor. Or as they say in Great Britain, great rigour.
To that end, MacKay points out, it's up to the wielder of the tool to understand how it works and use it appropriately.
A final thought here then. We expect historians to be honest and rigorous and unbiased. We expect the same of scientists and mathematicians and doctors and school teachers and plumbers.
We're right to expect this, and we're right to realize that sometimes (even perhaps often) professionals fall short. So who checks them? And if such checkers exist, who checks the checkers?
You and I do of course.
And here's the fun and exciting part. None of us are too dumb to check the professionals or to check the checkers.
We've all been told, time and again, that experts are really, really smart. The implication there is that "non-experts" (you, me, and us) just ain't that smart.
All men are created equal. You believe that, don't you? Sit back, close your eyes, and cogitate on that proposition for a while. Take it to its natural conclusion.
Then pull those harness straps tight, lock the inertial reel, and stand by for some g-warm.