Our band of intrepid pirates, having resolved previous squabbles over distributing booty amongst themselves and other issues have come across a treasure map fragment. The picture has been destroyed, but the following text can be read:
Stand upon the gravesite and you’ll see two great palms towering above all others on the island. Count paces to the tallest of them and turn 90 degrees clockwise and count the same number of paces and mark the spot with a flag. Return to the gravesite and count paces to the second-tallest of the trees, turn 90 degrees counter-clockwise and count off that number of paces, marking the spot with a second flag. You’ll find the treasure at the mid-point between the two flags.
Fortunately, our pirates knew which island the map referred to. Sadly, upon arriving at the island, the pirates discovered that all evidence of a gravesite had faded. The captain was preparing to order his men to dig up the entire island to find the fabled treasure when one of the more geometrically inclined pirates walked over to a particular spot and began to dig. The treasure was quickly unearthed on that very spot.
In this segment, we give some explanation of how Benford’s Law actually arises in so many settings: why are so many kinds of data logarithmically distributed? And we give a surprising fact about runs of coin tosses, and a new puzzle.
Benford’s Law is really quite amazing, at least at first glance: for a wide variety of kinds of data, about 30% of the numbers will begin with a 1, 17% with a 2, on down to just 5% beginning with a 9. Can you spot the fake list of populations of European countries?
List #1
List #2
Russia
142,008,838
148,368,653
Germany
82,217,800
83,265,593
Turkey
71,517,100
72,032,581
France
60,765,983
61,821,960
United Kingdom
60,587,000
60,118,298
Italy
59,715,625
59,727,785
Ukraine
46,396,470
48,207,555
Spain
45,061,270
45,425,798
Poland
38,625,478
41,209,072
Romania
22,303,552
25,621,748
Netherlands
16,499,085
17,259,211
Greece
10,645,343
11,653,317
Belarus
10,335,382
8,926,908
Belgium
10,274,595
8,316,762
Czech Republic
10,256,760
8,118,486
Portugal
10,084,245
7,738,977
Hungary
10,075,034
7,039,372
Sweden
9,076,744
6,949,578
Austria
8,169,929
6,908,329
Azerbaijan
7,798,497
6,023,385
Serbia
7,780,000
6,000,794
Bulgaria
7,621,337
5,821,480
Switzerland
7,301,994
5,504,737
Slovakia
5,422,366
5,246,778
Denmark
5,368,854
5,242,466
Finland
5,302,545
5,109,544
Georgia
4,960,951
4,932,349
Norway
4,743,193
4,630,651
Croatia
4,490,751
4,523,622
Moldova
4,434,547
4,424,558
Ireland
4,234,925
3,370,947
Bosnia and Herzegovina
3,964,388
3,014,202
Lithuania
3,601,138
2,942,418
Albania
3,544,841
2,051,329
Latvia
2,366,515
1,891,019
Macedonia
2,054,800
1,774,451
Slovenia
2,048,847
1,065,952
Kosovo
1,453,000
984,193
Estonia
1,415,681
841,113
Cyprus
767,314
605,767
Montenegro
626,000
588,802
Luxembourg
448,569
469,288
Malta
397,499
464,183
Iceland
312,384
402,554
Jersey (UK)
89,775
94,679
Isle of Man (UK)
73,873
43,345
Andorra
68,403
41,086
Guernsey (UK)
64,587
34,184
Faroe Islands (Denmark)
46,011
32,668
Liechtenstein
32,842
29,905
Monaco
31,987
22,384
San Marino
27,730
9,743
Gibraltar (UK)
27,714
7,209
Svalbard (Norway)
2,868
3,105
Vatican City
900
656
Looking at these lists we have a clue as to when and how Benford’s Law works. Show Spoiler ▼
In one of the lists, the populations are distributed more or less evenly in a linear scale; that is, there are about as many populations from 1 million to 2 million, as there are from 2 million to 3 million, 3 million to 4 million etc. (Well, actually the distribution isn’t quite linear, because the fake data was made to look similar to the real data, and so has a few of its characteristics.)
The real list, like many other kinds of data, is distributed in a more exponential manner; that is, the populations grow exponentially (very slowly though) with about as many populations from 100,000 to 1,000,000; then 1,000,000 to 10,000,000; and 10,000,000 to 100,000,000. This is all pretty approximate, so you can’t take this precisely at face value, but you’ll see in the list of real data that, very roughly speaking, in any order of magnitude there are about as many populations as in any other– at least for a while.
Data like this has a kind of “scale invariance”, especially if this kind of pattern holds over many orders of magnitude. What this means is that if we scale the data up or down, throwing out the outliers, it will look about the same as before.
The key to Benford’s Law is this scale invariance. Data that has this property will automatically satisfy his rule. Why is this? If we plot such data on a linear scale it won’t be distributed uniformly but will be all stretched out, becoming sparser and sparser. But if we plot it on a logarithmic scale, (which you can think of as approximated by the number of digits in the data), then such data is smoothed out and evenly distributed.
But presto! Look at how the leading digits are distributed on such a logarithmic scale!
That’s mostly 1’s, a bit fewer 2’s, etc. on down to a much smaller proportion of 9’s.
I’m overdue to post a puzzle, but I’m momentarily tapped out. Here’s a curiosity in the meantime: You can provide a very good estimate of a conversion from miles to kilometers by choosing sequential Fibonacci numbers. The conversion rate is 1.609344 kilometers to a mile. So this gives us:
1
2
1.609
2
3
3.219
3
5
4.828
5
8
8.047
8
13
12.875
13
21
20.921
21
34
33.796
34
55
54.718
55
89
88.514
89
144
143.232
144
233
231.746
233
377
374.977
377
610
606.723
610
987
981.700
987
1597
1588.423
This leaves you in pretty good shape if you need to get from Cincinnati, OH to Destin, FL at 610 Miles, but what if you need to convert some distance that doesn’t happen to be a Fibonacci number? Just build it up from parts!
100 miles is 89+8+3. So in kilometers, that’s 144 + 13 + 5 or 162 kilometers. (160.9344 by conversion…)
OK. Here’s a puzzle, sort of. I found this interesting set of numbers recently:
When two men get up ridiculously early to fire pistols at each other we call it a duel. Personally I prefer to lie in.
But what is the right term when three men skip breakfast to fire pistols at each other?
In a cold, misty field near the outskirts of Paris the sun peers over the horizon to see three men face each other with pistols. Xavier is an expert shot, he never misses. Jean-Christophe is a very good shot, he will get you four times out of five. Francois only has a fifty/fifity chance of hitting his target.
They each take turns to fire their pistol.
What is the best strategy for each of them and what odds would you give for the last man standing?
After the bodies had been cleared away I started to wander home only to hear the referee say ‘Maintenant, M. Galois et ami’
In Living With Crazy Buttocks, Stephen Morris told us of a rather interesting party. The story continues…
After winning their trip to Paris, the guests became elated and celebrated with the consumption of some adult beverages. Ever responsible, the host confiscated the keys to all cars to ensure that no one drove home drunk. Later on, when things started to calm down, party-goers started to request the return of their keys claiming to be sober enough for the drive home.
Having once been out-done by the guests, our host took another whack. He distributed all of the keys, but did so randomly. He then presented a challenge he felt sure they’d only be able to satisfy if they were indeed sober enough to drive. They were allowed to exchange keys, but only in rounds. During each round, each party-goer could either do nothing or pair up with another party-goer and exchange the sets of keys each was holding. (Each party-goer could be part of at most one pairing per round.) No one would be allowed to drive home unless everyone recovered their own keys.
The host wished to allow only a fixed number of rounds. To be fair, he wanted to be sure that it would indeed be possible to make the change. However, he also wanted to make it as difficult as possible for the party-goers. What is the minimum number of rounds must allow them to ensure that an exchange would be possible?
For clarity, all key recipients can discuss, share information such as who has the keys of whom, and agree upon a strategy. Also, careful readers will realize that there were 20 guests at the party originally. Sadly, it was a rather disorderly party and some guests did leave early, but many more appeared. Everyone present at the key ceremony had a key confiscated, and everyone with a key confiscated received a key for this challenge, but neither you nor the host knows just how many there are.
{ Hi, Steve here. Jeff asked me to post a solution and I’m more than happy to oblige. It’s a fun puzzle with some nice maths to explore. I learnt a lot about graph theory and a new theorem (new to me), Turan’s theorem. More on that later. }
You have eight batteries, four good and four dead. You need two good batteries to work the device; if either battery is dead then the device shows no sign of life. How many tests using two batteries do you need to make the device work?