The German Tank Problem
eadan.net
eadan.net
But AFAIK those targets were selected based on pre-war "traditional" intelligence what the likely bottleneck resources would be, not statistical analysis of captured equipment.
https://en.wikipedia.org/wiki/German_tank_problem
Matter of fact...
According to conventional Allied intelligence estimates, the Germans
were producing around 1,400 tanks a month between June 1940 and September 1942.
Applying the formula below to the serial numbers of captured tanks, the number
was calculated to be 246 a month. After the war, captured German production
figures from the ministry of Albert Speer showed the actual number to be 245.It's nearly impossible for a bomber to navigate long distances in the dark over a blacked-out country, so the Germans came up with a radio navigation system involving beams transmitted from the mainland to intersect over the target, which the British figured out how to jam; the Germans came up with another nav system, and the Brits eventually jammed that one too.
The British knew the Germans would be trying to find yet another way. They'd learned from Enigma decrypts about a new device called Wotan. One researcher looked up the word, learned that it was the name of a one-eyed god, and concluded that the new system would use a single transmitter with a rangefinding transponder aboard the bomber, instead of multiple beams like the previous ones. Starting from there, they had a countermeasure online and ready to go before the Germans even deployed Wotan. When the Nazis realized they'd been outmaneuvered from the start, they gave up on radio-guided bombing completely, at least against Britain.
British intelligence was pretty impressive during WWII.
https://www.amazon.com/Most-Secret-Penguin-World-Collection-...
And yes, the military intelligence of the Germans sucked in WW2. Didn't help neither that the culture, military and political, was highly idiological. When truth cannot be spoken and power won't listen facts are ignored. It cannot be what's not allowed to be. And then reality bites your ass ultimately.
A patriot could also have decide d to sabotage the war effort to end the war faster, to get rid of Hitler or to somewhat save the reputation of Germany.
He didn't openly defect but that doesn't mean much for a spy.
Yes, I agree it is quite a feat of mental acrobatics. My impression was that he somehow seperated Nazis and the German nation. And that the war was a German and not really a Nazi thing. Maybe he just didn't want to see that Germany and the Nazis were the same thing at the time, maybe he also wanted a round two after WW1 or maybe he wasn't able to shake decades of upbringing and training.
Either way, he was one of the few "good" Germans, even if not on Schindler levels, and definitely a very interesting person. Just look up his WW1 adventures.
Notable, so, is that even in WW1 and after he was not necessarily a trained spy intel guy, AFAIK.
Considering wars of aggression acceptable wasn't all that unusual either.
Sabotaging the war effort would have meant helping the Allies fight Germany. Sabotaging war crimes and the Holocaust meant trying to stop Germany from something evil and stupid (at least if he considered German Jews German). While there were reasons for a patriot to sabotage the war effort, only sabotaging the crimes was also a consistent position.
[1] Especially when it isn't democratically elected. The last multi-party elections in 1933 weren't free. The communist Reichstag members were jailed, many others were intimidated to make them support the enabling act.
[1] - https://en.wikipedia.org/wiki/Law_for_the_Restoration_of_the...
If M is the maximum serial number of N is the total number of observations, using the formula in the post:
M + (avg. spacing) = M + M / N - 1 = (N + 1) / N * M
To me that gives a more clear picture of what the unbiased
estimator is doing: inflate the maximum value by a factor that
limits towards one as the sample size grows.Or does it make a difference?
Additionally, the German army command didn't think that way. Where the US relied on overpowering by materiel dominance, and the Soviets fought and won through unimaginable human sacrifice, the considerable initial success of the German army was based on better, smarter tactics, individual leadership, bravery, ruthlessness, etc. The leadership assumed they'd be able to win the war that way, even when the war had turned into a much more industrial operation.
You can see that in operations such as the Battle of the Bulge, the war in Normandy, and most importantly in the the Russian campaign.
This is of course over-generalizing, but I believe the general mode of thinking was there, and that'd explain the lack of attention on such details.
In either case, terrible times that we should be thankful not to have been born into.
I recall seeing actual numbers (proportion of American steel in Soviet production), but couldn't find them, does someone have a source?
Anyway, e.g. this article talks about it:
https://www.rbth.com/defence/2016/03/14/lend-lease-how-ameri...
The scale of resources delivered to prop up the Soviets was extraordinary, including what the British sent them.
In just 3 1/2 years the British sent them[1]:
3,000+ Hurricanes aircraft, 4,000+ other aircraft, 27 naval vessels, 5,218 tanks, 5,000+ anti-tank guns, 4,020 ambulances and trucks, 323 machinery trucks, 1,212 Universal Carriers and Loyd Carriers, 1,721 motorcycles, £1.15bn worth of aircraft engines, 1,474 radar sets, 4,338 radio sets, 600 naval radar and sonar sets
And the US sent them:
427,284 trucks, 13,303 combat vehicles, 35,170 motorcycles, 2,328 ordnance service vehicles, 2,670,371 tons of petroleum products (gasoline and oil) or 57.8 percent of the High-octane aviation fuel,[32] 4,478,116 tons of foodstuffs (canned meats, sugar, flour, salt, etc.), 1,911 steam locomotives, 66 Diesel locomotives, 9,920 flat cars, 1,000 dump cars, 120 tank cars, and 35 heavy machinery cars. Provided ordnance goods (ammunition, artillery shells, mines, assorted explosives) amounted to 53 percent of total domestic production
Beyond Russia also notes:
"The USSR received a total of 44,000 American jeeps, 375,883 cargo trucks, 8,071 tractors and 12,700 tanks. Additionally, 1,541,590 blankets, 331,066 liters of alcohol, 15,417,000 pairs of army boots, 106,893 tons of cotton, 2,670,000 tons of petroleum products and 4,478,000 tons of food supplies"
The notion of sending a country 375,000 trucks and 1,900 locomotives in just three years, is incredible to think of today.
80 percent of all German military casualties occurred on the Eastern Front.
Just think about those numbers.
Second, all calculations were done by hand in those days (and documents that weren't printed in bulk had tp be retyped by hand) so sequential numbers were not only easier to issue but to track (e.g. if you have a production problem you can say "let's check all tanks with S/Ns between A and B" rather than having to maintain a list mapping production dates to serial numbers that might be in a file cabinet somewhere distant from where you are.
Because there weren't well-known examples of the risk of not doing that, and not doing it is the easy and obvious thing if you have no clear reason to do it, and makes lots of things you might use those numbers for yourself easier (and if it wasn't for your own use, you wouldn't issue the numbers at all.)
The Germans did (eventually) make some effort to obscure details of their supply chain--they forced manufacturers to use three-letter codes instead of their normal trademarks--but that still suffered from poor operational security which allowed the codes to be quickly matched up to manufacturers. It didn't help that the British analysts meticulously kept track of everything, allowing them to identify the manufacturer of one unlabelled part by the inspector's number.
The German army was not particularly mechanised or well equipped as a whole, relying on a lot of horse draw vehicles for the entire war.
When you look at the war from a manufacturing perspective, the question is more about how Germany survived for so long again it’s such huge manufacturing nations. For a seemingly dry subject, David Edgerton’s book on this is very readable. https://www.theguardian.com/books/2011/mar/27/britains-war-m...
It's also worth point out that Germany suffered from a severe lack of resources, particularly oil and rubber (although everyone in WWII was short on rubber). While they did have synthetic fuel and rubber plants that they made excellent use of (part of the reason for German superiority in the chemical industry was their need for it), these synthetic routes are not really sustainable for a massive war effort, and Germany ran out of their stockpiled reserves by 1942. Case Blue, the second offensive in the USSR, had obtaining the Baku oil fields as its main objective.
Aircraft (especially fighters) have the same three requirements: until the ME-262 was deployed, Germany was only on par with the allies.
Artillery? Other than the feared 88mm, its artillery was clearly second fiddle to the Allies.
What enabled Germany to have any success was the initial training of its NCOs and officer corp. This allowed them to exploit opportunities faster than their opponents (think of Boyd's OODA cycle).
But all the oft-touted German "super-weapons" were usually over-engineered stuff that didn't work reliably. Note that the ME-109 flew until the end of the war since it was reliable, and effective against bombers until they were escorted by Mustangs and Thunderbolts.
They knew for example that they only had enough oil, with the limited mechanized forces they had, for operational effectiveness until autumn 1941. After that Germany would never again have the resources for grand operations on the strategic level of operation barbarossa. They needed to get to the oil fields of the Caucasus region which they did not even get close to due to some screwed up leadership decisions.
Fall blau was a pale comparison to the earlier operations and Germany's logistics system and resources were beyond tipping point.
And then when it came to Kursk all they could really manage was a single limited scope battle.
Still, the idea that this could leak valuable information is probably more obvious in hindsight, and sequential serial numbers do have some upsides. If there's a design flaw in one version of the gearboxes, you can just pull everything with a serial number between XXXX and YYYY. With randomized numbers, you'd have to maintain some master database, which is a lot harder when most of logging is done with pen-and-ink ledgers, carbon copies, and maybe punchcards.
I think it's more likely that many were aware of the security issues, but it wasn't worth the coordination of coming up with a scheme, giving it to all spare parts suppliers in a secure way, etc. potentially slowing down the war effort. I bet the Allies used a lot of serial numbers too, despite this work.
[0] https://en.wikipedia.org/wiki/German_Workers%27_Party#Adolf_...
Because it happened 80 years ago, when German army (or any other) did not understand statistics as well as they do today. It was a groundbreaking achievement by allies.
I don't think you need deep statistics knowledge to know that if the enemy captured Serial # 0020, 0120, 0439, 1293 and 1356; they would at least have some hint that the lower bound is 1356 tanks.
Furthermore, the interesting part isn't just the number of tanks or planes—though that has obvious strategic uses too— but the insight it gives you into their industrial production. What's the limiting factor in getting a tank to the front--machining the parts? assembly? fuel? Which of our raids affected that?
Congratulations on your nerd snipe!
Confounding question: 1000 years ago, would this argument look any different? Answer: mathematically speaking, it would not. In fact, far more humans have been born than you could have predicted using this method. Conclusion: the argument is flawed.
However, the argument will still give the correct prediction for most humans that try to use it. Just not for the few that were in the special position to be born early in the sequence of all humans. The argument essentially tells you that you have no reason to believe that you are also in that special position.
The tank problem doesn’t tell you how many tanks Germany will go on to build - it just tells you how many they have already built.
‘Good news! The war is almost over! The chances are these tank serial numbers all fall among the last 95% of the tanks Germany will ever produce!’
To apply the doomsday argument to tanks, we need to fix the sampling. It won't work for allies grabbing german tank samples. We have to think from the point of view of the tank; say a copy of your consciousness is being uploaded to an army of tanks. If you wake up as tank #734, could there eventually be millions of tanks? Maybe so, but there's only a 5% chance that you are one of the first 5% tanks, so there's a very good chance that there will be less than 734*20 tanks in total.
A full listing was available through the site's robots.txt sitemaps file, or rather, a listing to the listing of 50,000 user profile sitemap files, with about 44k profiles per file. This worked out to 25 GB of profile listings alone.
Rather than download the full set (though I eventually did), I picked an arbitrary file from near the middle of the listing, and ran some spot checks on the profiles, which seemed to be reasonably randomly distributed by age, location, and other characteristics. With as few as 100 profile page downloads, it was clearly evident that active posting to G+ was limited to about 8-11% ofall profiles. The full 50k profile sample, and a third party's independent (and more robustly randomised) 500k profile sample eventually showed this to be 9.7%.
(And yes, if I was being more rigorous I could have done much more testing or work, but I was mostly addressing personal curiosity and an online disagreement with someone.)
An interesting proof of the power of random sampling.
Larger samples do allow for clearer views of rare phenomena -- such as dialing in on the fraction of 1% of G+ users highly active on the site. Or when I later looked at Communities characteristics, the properties of the very largest (about 50 > 1 million members) of the 8 million total. In that case, I eventually got access (also via a third-party) to a comprehensive summary dataset.
The userID hashing also made approaches such as exhaustively searching the ID space for user pages nonviable. The search space was trillions pf times larger than the target space.
[0] https://www.theguardian.com/technology/blog/2008/oct/08/ipho...
Not much clutter & straight to the point. Loads fast and it’s under 630KB.
Could certainly be improved but it’s nice not having to load >25MB just to read an article.
Are you looking for the answer that is the 'most likely', or one that has the 'lowest least squared error', or maybe one that is 'unbiased' (mean error)?
Probably in today's world this is racist or nationalist or something. But (as someone of German descent) I have to admit it's funny.
http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-get.cgi/2...
Little bit of a funny though: Note how num_tanks ~ Unif(max(captured),2000) was defined, so you already have p[ parameter | data ]. Isn't this already a posterior?
I get however how if you had the r.v.s num_tanks ~ Unif(M,2000), observed | num_tanks ~ Unif(1,num_tanks), M some constant, that you could find a posterior distribution num_tanks | vector<observed> by first finding the joint via E[ 1[num_tanks < t]P[observed | num_tanks] ]
[0] https://www.eadan.net/blog/german-tank-problem/#probabilisti...
[0] https://en.wikipedia.org/wiki/Discrete_uniform_distribution#...
[689, 341, 386, 741, 982, 414, 845, 241, 180, 447, 880, 21, 583, 993, 812]
it’s tough to see an argument for anything other than: a. about 1,000, or b. 1,000ish but there may be a confounding fact pattern we are unaware of....
https://github.com/CamDavidsonPilon/Probabilistic-Programmin...
2 x mean
should be an unbiased estimator of the true mean. But because we are probably under sampling the extremes, we could use the Bessel correction:
1/(n-1) x summation_{i=1}^n(sample_i)
I would guess this comes out to a better estimation than what the article says.
Bessel's correction might be a bit of overkill, since it's intended to work with normal distributions. But I still suspect it comes out to a better estimation that what the blog post says.
You could adjust for such problems but it seems much easier to use the maximum.