Science


May 14, 2008: 12:01 am: CalvinDudeMath

(Click on graphic to have it open in a new browser window if it doesn’t display fully for you.)

Okay, after having worked on the equations for a bit more (and discovering I had left off an important set of parentheses in the previous function formulas), I have found the simplified version of each of them. These equations will once again use two variables, but since the 6n +/- 1 format is already established for primes, I’ve reworked it. For the following, n = the row you’re trying to build on the graph. This is slightly misleading because each row is actually patterned off of the 6n +/- 1 format itself. Therefore, there are two rows for each n. The 6n - 1 and the 6n + 1 value. And for purposes of the chart, a 6n - 1 number is black and a 6n + 1 number is red.

Finally, there is a controlling x value that determines how far to the right you’ll place the cell. Basically, if you were testing for a prime, you could loop x starting at 0 and running the equation, then increasing x by 1 and running the equation, repeating until x equals the n value you’re searching for. If you’re testing a 6n - 1 number, then any black values in the n column will be factors; if you’re testing a 6n + 1 number, then any red values in the n column will be factors.

Now are you ready for the massively complex new equations?

x(6n - 1) + n ; (Use to find black values on the 6n - 1 line.)
x(6n + 1) + n ; (Use to find red values on the 6n + 1 line.)
x(6n + 1) - n ; (Use to find red values on the 6n - 1 line. x must be greater than 0.)
x(6n - 1) - n ; (Use to find black values on the 6n + 1 line. x must be greater than 0.)

And just to demonstrate it, here’s the values for the first couple of rows.
n = 1
x = 0

Black finds black @ 1
Red finds red @ 1
Red finds black @ - 1*
Black find red @ - 1*

n = 1
x = 1

Black finds black @ 6
Red find red @ 8
Red find black @ 6
Black finds red @ 4

n = 2
x = 0

Black finds black @ 2
Red finds red @ 2
Red finds black @ - 2*
Black finds red @ - 2*

n = 2
x = 1

Black finds black @ 13
Red finds red @ 15
Red finds black @ 11
Black finds red @ 9

* = why X must be greater than 0 for the final two equations.

By the way, the first two equations are equivalent to the equations that I came up with before if n = S + 1. The second two would not be due to some misplaced parentheses in my original formulas :-( Oh well. This way is simpler and more “elegant.”

May 13, 2008: 2:50 pm: CalvinDudeMath

Note: X must be > 0 to work correctly.

F(S, x) = S + (1 + x) + x[(4S + 2) + 1 + (2S + 1)].
F(S, x) = S + x + 1 + x[(3(2s + 1) + 1]
F(S, x) = S + x + 1 + x[(6S + 3) + 1]
F(S, x) = S + x + 1 + (6sx + 3x + x]
F(S, x) = S + 2x + 1 + 6Sx + 3x
F(S, x) = S + 5x + 6Sx + 1

F(S, x) = S + 1 + x(4s + 4) + 1 + (x – 1)(2S + 1)
F(S, x) = S + 1 + 4sx + 4x + 1 + 2sx + x - 2S - 1
F(S, x) = S + 1 + 6Sx + 5x - 2S
F(S, x) = 6Sx + 5x + 1 - S

F(S, x) = S + (1 + x) + x[(4S + 4) + 1 + (2S + 1)]
F(S, x) = S + 1 + x + x(4S + 4) + x + x(2S + 1)
F(S, x) = S + 1 + x + 4Sx + 4x + x + 2Sx + x
F(S, x) = S + 1 + 7x + 6Sx

F(S, x) = S + 1 + x(4S + 2) + 1 + (x-1)(2S + 1)
F(S, x) = S + 1 + 4Sx + 2x + 1 + 2Sx + x - 2S - 1
F(S, x) = -S + 6Sx + 3x + 1
F(S, x) = 6Sx + 3x + 1 - S

(6n - 1) functions:
F(S, x) = S + 5x + 6Sx + 1
F(S, x) = 6Sx + 5x + 1 - S

(6n + 1) functions:
F(S, x) = S + 1 + 7x + 6Sx
F(S, x) = 6Sx + 3x + 1 - S

Rewritten:

(6n - 1) function:
F(S, x) = 6Sx + 5x + S + 1
F(S, x) = 6Sx + 5x - S + 1

(6n + 1) function:
F(S, x) = 6Sx + 7x + S + 1
F(S, x) = 6sx + 3x - S + 1

May 12, 2008: 11:57 pm: CalvinDudeMath

What would happen if you made a chart based on the following patterns (wherein everything repeats except for the “Initial Skip” which is a one-time event):


Initial Skip Red/Black Skip Red/Black Skip
0 Black 2 Red 1
0 Red 4 Black 1
1 Black 6 Red 3
1 Red 8 Black 3
2 Black 10 Red 5
2 Red 12 Black 5

Etc.

You would get a graphic like this one:

You might be thinking, “So?” Well, one of the things that I enjoy about mathematics is that it is easy to come up with several different paths to the same destination. In fact, I remember my geometry teacher in high school say the following about someone’s proof: “Your method gets us to the solution, but it’s like driving from here [a small town near Pueblo , Colorado ] to Colorado Springs via Fairbanks , Alaska . You get there, but it’s the long way.”

Mathematicians typically seek the most “elegant” solution, where elegance is defined as the simplest solution. You can have bulky proofs that get you where you need to go, but a sleek, elegant proof is preferable. However, in my opinion while they may be preferable in the ethereal sense, the various perspectives on mathematical problems actually help us to more fully understand the concepts involved. And indeed some mathematical truths may be easier to see in one perspective than in another perspective.
That is one of the reasons that I’ve been looking at the “Factor Field” sheet I’ve made in Excel and drawing conclusions from it. Now I’ll be the first to admit that I’m never very good at calculations in mathematics, but I am a very visual mathematician (this is why I’ve always liked geometry the most out of all kinds of math). Some who are really good at calculations have already proven such things as: All prime numbers exist in the format of 6n +/- 1. They proved this by showing that any other number than 6n +/- 1 must be divisible by 2 and or 3, and therefore cannot be prime. I came to this same conclusion independently by looking at the Factor Field and seeing the 6-spike there and wondering what it would look like if I converted it to base 6 numbers.

I think the visual representations are therefore a very powerful tool. Again, I’m not the greatest at calculations and therefore if I had to limit myself to calculations I wouldn’t get very far. But because in our day and age computers make it easy to come up with visual representations, someone like me can look at these patterns and then formulate equations just as complicated as those who are good at calculations come up with. That’s what I did with the above graphic. So how did I come up with it?
Well, remember this graphic?

I started by looking at the 5 column. Then I counted the 6-spikes and put on a graph whether the number was higher or lower than the 6-spike. So for the first one, the 5 line starts with the number higher. It got a black cell. After that, the 5 column skips 2 6-spikes before touching lower on the 6-spike. Therefore, I skipped 2 and then put a red cell. After that, it skips 1 6-spike (where it’s actually on the 6-spike) and repeats the sequence.

I did the same with 7, 11, 13, etc. until I got the above chart. Here’s it reproduced with the vertical and horizontal axis labeled appropriately as well as a “total” line at the bottom:

Now here’s how to understand what is there. Look at the column headers. They are all multiples of 6. Those who remember that P numbers (as I’ve defined them) are in the format of 6n +/- 1 should already know where this one is heading. A cell will be black if it is in the 6n – 1 format; red if it’s in the 6n + 1 format.

The total line is created in this way. Excluding the first red and black (the cells that appear after the initial skip, but not after any of the repetitions), if there is a red cell (or more than one) and no black cells in the column, the cell for that column on the total line gets marked red. If there is a black cell (or more than one) and no red cells, it gets marked black. If there are both red and black cells, it gets marked blue. If there are no red or black cells, it remains gray.

Now if there is a red cell on the total line, that means that the 6n + 1 number is not prime. If it’s black, then 6n – 1 is not prime. If it’s blue, then neither 6n + 1 nor 6n – 1 are prime. If it’s gray, then both 6n + 1 and 6n – 1 are prime.

Pick one of the numbers on the total line (let’s use 84). The cell on 84 is red. Red refers to a 6n + 1 number. Therefore, we know that 84 + 1 is not prime. And sure enough, 85 is not prime. Furthermore, you can look in the column above the total line and see the factors of 85 listed out in all the red cells: 17 & 5.

Since 84 is red (not blue) then we know that the 6n – 1 of 84 (83) is prime.

Let’s look at another number: 120. 120 is blue, so we know that neither 119 nor 121 is prime. And sure enough, we can find the factors of each in the above graph. 119 has factors of 17 and 7 (119 is a 6n – 1 number, so look for the corresponding black cells) and 121 has 11 as a factor (121 is a 6n + 1 number, so look for the corresponding red cell).

Now here’s the thing about this graph. I made it without doing a single factor calculation! Instead, I looked at the pattern, and the pattern shows us this (where a “-“ refers to a 6n – 1 number and a “+” refers to a 6n + 1 number, represented by black and red respectively):

5:   0 - 2  + 1
7:   0 + 4  - 1
11:  1 - 6  + 3
13:  1 + 8  - 3
17:  2 - 10 + 5
19:  2 + 12 - 5
23:  3 - 14 + 7
25:  3 + 16 - 7

And furthermore, from looking at this pattern, I’ve been able to deduce a general rule whereby you can figure out what the repetition of any specific line (L) on the graph would look like. To find it, you do the following:

1. Calculate if number N is a 5 or 1 number (i.e. if it is in the 6n + 1 or 6n – 1 format).
A. Convert to base-6, see if last digit is 5 or 1 or…
B. Find the remainder of N/6 (you can use N mod 6 on a calculator). It must be either 5 or 1.

E.g. 35. 35/6 = 5 r 5. It is a 5 number.
E.g. 49. 49/6 = 8 r 1. It is a 1 number.

2. Calculate Initial skip for starting skip variable (S).
A. If 5 number, the integer (non remainder) portion of N/6 is the Initial Skip: S = N/6
B. If 1 number, the integer (non remainder) portion of N/6 less 1 is the Initial Skip. S = (N/6) - 1

E.g. 35. 35/6 = 5 r 5. 5 is Initial Skip.
E.g. 49. 49/6 = 8 r 1. 8 - 1 = 7. 7 is Initial Skip.

3. If N is a 5 number (Black), the pattern is: S + Black + (4S + 2) + Red + (2S + 1).

E.g. N = 35. S is the integer portion of 35/6 = 5. Substitute into the equation and you get 5 + Black + 22 + Red + 11.

4. If N is a 1 number (Red), the pattern is: S + Red + (4S + 4) + Black + (2S + 1).

E.G. N = 37. S is the integer portion of 37/6 less 1 (i.e. 6 – 1 = 5). Substitute into the equation and you get: 5 + Red + 24 + Black + 11.

The two patterns are:

S + Black + (4S + 2) + Red + (2S + 1)
S + Red + (4S + 4) + Black + (2S + 1)

In reality, the initial S is not part of the pattern, it just slides the pattern over. If we put the portion that repeats into brackets [], you get:

S + [Black + (4S + 2) + Red + (2S + 1)]
S + [Red + (4S + 4) + Black + (2S + 1)]

Repeat what’s in the brackets for however many times you need.

Now as you can see from the graph, you don’t really need to check many of the values to determine if a specific number is prime or not. You only need to find the column that the number appears in after sliding over from the left and make sure that there are no corresponding marks in the column above it.

So let’s start with one that we can empirically verify on the chart. The number 35. 35 is a “Black” number because it is a 5 number (as shown above). The Initial Skip value (S) is 5. This means that there are 5 skips before the number appears, so the number will appear in the 6th column. So you can always find the column (C) by finding S + 1. In our test, C = 6.

Now since 35 is a “Black” number (i.e., a 6n – 1 class number) we only have to worry if there are any other “Black” numbers in the same column. Red numbers do not matter because they will not match up with the Black numbers. However, there will be two functions that we have to use in order to test a specific value. The first function is the one that calculates where on the grid a Black number places a Black mark. The second function calculates where on the grid a Red number places a Black mark (after all, they alternate back and forth as they go across).

So when testing where a Black number puts a Black mark on the grid, we use this function:

F(S, x) = S + (1 + x) + x[(4S + 2) + 1 + (2S + 1)].

To us this function, we’ll need a nested loop (a loop within a loop). First start with S and x both equaling 0. We can set up the test in the following computer pseudo-code:

For S = 0 to C
X = 1
            While TestValue < C
                        TestValue = F(S, x)
                        x = x + 1
            End While Loop
If TestValue = C, then the number is not prime and you should break loop.  Otherwise…
Next S

By the way, if you’re wondering why we start with x = 1 instead of x = 0, it’s because whenever x = 0 then the function value will always equal the S value + 1. And therefore, F(S, x =0) can be ignored because it will always be less than the C value except when it equals the C value (which means you’re testing the number against itself, which is pointless).

Let’s go ahead and test it with our variables as x = 0 and S = 0 to show this. Remember that when S = 0 that means the initial skip value is 0. Since we’re looking at Black numbers, we know that this corresponds to the first row on the chart, so we can check that to verify if the function works correctly. We are therefore going to re-create the first row, but we only need to do so up until we get to column 6 since the number N that we are testing is 35.

F(S, x) = S + (1 + x) + x[(4S + 2) + 1 + (2S + 1)].
F(0,0) = 0 + (1 + 0) + 0{[4(0) + 2] + 1 + [2(0) + 1])}
F(0,0) = 1

When x = 0, therefore, we see that F(S, x = 0) will equal S + 1. And this is accurate: The first Black mark does indeed show up in the first column. (You can multiply the F(S,x) answer by 6 to get the correct header if you want, but the labels aren’t actually relevant here.)

Anyway, let’s continue with x = 1:

F(S, x) = S + (1 + x) + x[(4S + 2) + 1 + (2S + 1)].
F(0,1) = 0 + (1 + 1) + 1{[4(0) + 2] + 1 +[2(0) + 1]}
F(0,1) = 2 + (2 + 1 + 1)
F(0,1) = 6

At this point F(0,1) is not less than 6 because it in fact equals 6. Therefore, the loop breaks out. The program then tests if the final value equals the value being searched for. It does. Therefore, 35 is not prime and the program terminates.

Just to show it works, let’s see what happens if we pick a prime “Black” number, such as 29. First, we calculate the S number. S = the integer portion of 29/6 = 4.

C = S + 1, so C = 5.

Now we start through the function, once against starting with S =0 and x = 1. Obviously, the first result of F(0,1) will yield the exact same results as what is shown above. Since the C value is now 5, however, we have F(0,1) = 6 which is greater than 5 but it is not equal to 5. This means we increase the S value to 1 and reset the x to 1 and repeat the loop:

F(S, x) = S + (1 + x) + x[(4S + 2) + 1 + (2S + 1)].
F(1,1) = 1 + (1 + 1) + 1[(4(1) + 2) + 1 + (2(1) + 1)]
F(1,1) = 3 + [6 + 1 + 3]
F(1,1) = 13.

If you check the chart, you will see that the second black mark for the 11 row is indeed located at column 13.

Now we can see that {F(1,1) = 13} greater than {C =5}. Therefore, we increase the S to 2, and reset the x to 1.

F(S, x) = S + (1 + x) + x[(4S + 2) + 1 + (2S + 1)].
F(2,1) = 2 + (1 + 1) + 1[(4(2) + 2) + 1 + (2(2) + 1)]
F(2,1) = 4 + (10 + 1 + 5)
F(2,1) = 20.

{F(2,1) = 20} greater than {C = 5} so increase S to 3 and reset x to 1.

F(S, x) = S + (1 + x) + x[(4S + 2) + 1 + (2S + 1)].
F(3,1) = 3 + (1 + 1) + 1[(4(3) + 2) + 1 + (2(3) + 1)].
F(3,1) = 5 + (14 + 1 + 7)]
F(3,1) = 27.

Now I’m sure you can see the pattern that is resulting from this function. Increasing the S value will always yield a larger answer (in fact it’s the previous answer + 7). Therefore, if at any point when x = 1 the function is greater than the C value, we know that there are no more factors that we need to test. Note that this wouldn’t prove that the number is prime; it merely proves that no other Black numbers are factors for it. We still have to do a function for where the Black marks appear on the Red numbers, and we get that in the following function:

F(S, x) = S + 1 + x(4s + 4) + 1 + (x – 1)(2S + 1)

Once again, we can test it with figures we know from the chart. For instance, when S = 0 and x = 1:

F(0,1) = 0 + 1 + 1(0 + 4) + 1 + (0)(0 + 1)
F(0,1) = 6

If you look at the chart, the first “Red” number is on the 7 line, and the first black mark is indeed at column 6. Testing S = 1 and x = 1, we see:

F(1,1) = 1 + 1 + 1(4(1) + 4) + 1 + (0)(2(1) + 1)
F(1,1) = 1 + 1 + 8 + 1
F(1,1) = 11.

The first black mark on the second Red number (13) is indeed at column 11.

So that means that we can test any “Black” number (i.e. 6n – 1) to see if it’s prime by running it through these two functions:

F(S, x) = S + (1 + x) + x[(4S + 2) + 1 + (2S + 1)]
F(S, x) = S + 1 + x(4s + 4) + 1 + (x – 1)(2S + 1)

Testing for “Red” numbers (i.e. 6n + 1) is similar. In those cases, the functions look like this:

F(S, x) = S + (1 + x) + x[(4S + 4) + 1 + (2S + 1)]
F(S, x) = S + 1 + x(4S + 2) + 1 + (x-1)(2S + 1)

You can test them to see they match up to the chart if you want. I will simply conclude this by pointing out that using math no more complex than simple Algebra, you can test whether a number is prime or not…all without doing massive amounts of division! The only division you have to do is to divide your test number by six to get the starting integer (so you can get the S value) and the remainder (so you can determine if you’re looking for a 6n + 1 or 6n – 1 number).

May 11, 2008: 11:24 pm: CalvinDudeMath, Science

Even if you only casually read through news websites (such as those of CNN or FOXNews), several times per month you will notice headlines such as the following:

TOO MUCH, TOO LITTLE SLEEP TIED TO ILL HEALTH IN CDC STUDY

Study: Long-Term Breast-Feeding Will Raise Child’s IQ

WOMEN, WANT A HEALTHY MARRIAGE? MARRY MAN UGLIER THAN YOU, STUDY SAYS

STUDY: FOOD IN MCDONALD’S WRAPPER TASTES BETTER TO KIDS

Study: 1 in 50 U.S. babies abused, neglected in 2006

And naturally we’re all aware of the competing studies that exist too. One study shows that eggs are bad for you; another that they’re good for you. One study shows how margarine is a healthier alternative than butter; another that butter is better for you. With so many competing studies, you can find a scientific backing for just about any position you want to take (especially in health matters).

The existence of so many studies helps to emphasize a point regarding statistical analysis. Despite being a powerful tool, if you do not set up the guidelines and restrictions for your samples properly any statistics you observe won’t amount to a hill of beans. And we’re not even talking about the inherent fluctuations that require the existence of error bars (that’s the line that says +/- 3%, for example). Nor are we even addressing political manipulation of statistics in the form of pollaganda. Instead, I’m talking about something at the heart of statistics itself—it’s a universal.

To demonstrate what it is, let us first ask a simple question. When we do a statistical analysis of some observation, for what reason are we doing it? As you can see in the above headline examples, most of the time studies are done to find a causal linking between some object and/or action and some result. Thus, the first headline above says that too much or too little sleep (the cause) is “tied” to “ill health” (the effect). We also see that women should marry uglier men for a healthy marriage (in a study obviously written by an ugly man).

Now let us assume that there is a correlation that all these studies found. Let us assume that it is the case that people who sleep less than six hours a night weigh more than those who sleep eight hours a night, and that women who married uglier men (however that is defined) are in healthier (however that is defined) marriages. The fact of the matter is that when you compare any subset of a group, however you wish to define that subset, with the rest of the group as a whole, you will find things that the small group has in common at a statistically higher rate than the group as a whole. This happens automatically and does not mean that it is relevant in a causative sense!

To give a simple example, let’s examine hockey (since I like hockey). There are 30 teams in the NHL. Of those 30 teams, 7 are named after animals (the Penguins, Bruins, Thrashers, Panthers, Ducks, Coyotes, and Sharks) and 7 are named after people-groups (the Islanders, Rangers, Canadiens, Senators, Blackhawks, Oilers, and Kings). Each group of 7 constitutes 23% of the teams in the League.

There have been 80 Stanley Cups awarded since 1926. During that time, teams named after animals have won 8 Stanley Cups, which means that they won 10%. However, teams named after people-groups have won 39 Stanley Cups during that time, which means they won 49% of them. Clearly, having a team named after a people-group instead of after an animal provides a statistical advantage to a hockey team…

Perhaps someone could argue that the statistical data isn’t fair. After all, the Thrashers (1999), Panthers (1993), Ducks (1993), Coyotes (1996), and Sharks (1991) are all teams that did not exist before the 1990s! On the other hand, the Rangers, Canadiens, Senators, and Blackhawks all existed in 1926 (the start of this survey). Furthermore, the Kings were founded in 1967, the Oilers in 1971 and the Islanders in 1972. Of the animal teams, only the Bruins were around in 1926 (the Penguins were founded in 1967). Thus, using 1926 as the baseline (since before that there were other teams besides just NHL teams that could play for the Cup), the average year of founding for animal teams is 1981 and for people-group teams it’s 1945.

However, we can adjust for that. Animal teams have won a Cup on average every 3.25 years they’ve existed; while people-groups win a Cup for every 1.59 years they’ve existed. Clearly, it still remains better to have a team named after a people-group than an animal. (And I’m not biased since I cheer for the Avalanche, which is neither a people-group nor an animal…)

Now here’s the thing. The statistical data that I’ve given here is all correct (assuming I didn’t make any typos or anything of that nature), but every rational person would immediately recognize that the type of name a sports team has, has no bearing on the performance of that team. This is an attribute that is linked statistically, but the statistical linkage is accidental rather than causative.

Every time that we do these surveys and examine the numbers we have to realize that there are some number of things that will be discovered in common that are accidental correlations. The problem is that we ignore most of these connections. And when I say we ignore them, I don’t mean that we test the data and then go, “This isn’t relevant” but we do not even look for them in the first place. After all, were it not for the fact that I was looking for an example for this blog entry I would never have cared what percentage of teams named after animals won the Stanley Cup. This correlation would have been excluded a priori as being irrelevant.

But these irrelevant correlations are important to statistical analysis! Why? Because since a certain percentage of linkages are accidental, we have to account for them in our conclusion. In other words, we have to have some way of determining if the link we discover is causative or if it is merely the kind of statistical fluke you get when examining hockey mascots. And that means that we would need to examine all possible connections and discard those that are accidental in order to find out if the statistical percentages are covered.

That, however, is impractical to the point of impossibility. After all, it is relatively easy to come up with statistical correlations between things. For instance, with my hockey example it took me all of 15 minutes to come up with that correlation. The longest part was pulling up the Wiki sheets on the number of Stanley Cup wins various teams had had. Indeed, based on my experience I would argue that it is so easy to come up with meaningless links between data that it will always remain more likely that a correlation is accidental than causative. That is, for every one true causative link between a subset of a group and the average of the entire group, I would argue there are several accidental links. And these accidental links are not always as obviously accidental as the examples I’ve given. (For a less obvious example, think of the correlation between diabetes and obesity. Does one cause the other? Or is it just a statistical fluke, similar to the names of hockey teams?)

If it is so difficult to prove our position statistically due to the possibility of accidental links, then what good is it to come up with a statistical correlation in the first place? For most studies that you read about in the media, the answer is: “None.” However, for scientists there remains one thing that a truly causative link can do that an accidental link cannot do that saves the field. A truly causative link will enable you to make a prediction that you can test and verify. If something is causative then it will continue to cause the effect at the same rate. On the other hand, if it is accidental then it is a random linkage, and random linkages will break down through further testing. For instance, the fact that people-group teams have won more Stanley Cups than animal teams does not help us predict who will win the Stanley Cup this year or next year or the year after that; therefore, it is an accidental link rather than a causative link. However, if further testing shows that the percentages of obese people who get diabetes remains constant, then we can have more confidence that that is a truly causative link rather than simply a statistical accident.

So there are some ways to salvage statistics. But it requires that we be able to conduct further tests with our predictions in place in order to sort out whether we have a meaningful causative link or a meaningless accidental link. If we cannot conduct those further tests, then any causative links will be lost in the noise of the countless accidental links. They may be true, but it is impossible to verify it.

May 7, 2008: 3:56 pm: CalvinDudeMath

By the way, here’s something I just thought of with the factor field. First, check out this graphic:

As you can see, the red cells are there to highlight gaps in the line with the lines that come off in both directions on the arms of the “starburst.” Now here’s the thing about that: wherever a factor exists on the 6-spike, it blocks the same area at whatever distance that is above and below the line. This means that if there’s a factor at 5, then 5 above and 5 below that 6-spike cannot be prime.

Here’s what I thought of. Whenever you have factorial numbers, then these arms will block primes from appearing. So if you have 10 factorial (10!), for instance (which would occur at 3628800 (which is 10 x 9 x 8 x 7 x 6…), then there cannot be any prime numbers occuring for 10 ahead and 10 after. It’s impossible, because the arms that branch off would block them.

So then…if you have 100!, then it blocks 100 before and 100 after. 1000! would block 1000 before and 1000 after. And so on. The bigger the number you’re doing the factorial for, the more numbers before and after get blocked. Which means there are huge sections of numbers that cannot possibly be prime.

Which really makes you wonder about infinity factorial… Because an infinite number of numbers before and after it would be blocked from being primes….

: 12:12 pm: CalvinDudeMath

Mike Jones commented on my post over on the T-blog:

It’s actually not too difficult to show that all primes will end in 1 or 5 in base 6 (although not all numbers ending in 1 or 5 are primes), with the exception of 2 and 3.

We can categorically eliminate numbers that end in 0, 2, and 4. These are non-odd numbers. No prime other than 2 will be even.

That leaves 1, 3, and 5.

Let Xn be any number that ends with the digit 3 in base 6, except 3. This number may be represented as:

Xn = 3 + 6n, where n > 0

We can factor 3 out of the expression:

Xn = 3(1 + 6n), where n > 0

Therefore, all numbers ending in the digit 3 in base 6 are divisible by 3 => No prime numbers will end in 3 in base 6.

That leaves 1 and 5.

Your hypothesis is fact.

:D

This got me to thinking about my other hypothesis that all P numbers are either prime or have factors that are other P numbers. And now I’ve been able to prove that.

Remember that I previously defined a P number as: “The P class is defined as any number that is N +/- 1″ and N was defined as “a positive number that is divisible evenly by 6.” In reality, this definition is too restrictive, so allow me to redefine it slightly:

A P class number is any number in base-6 whose last digit (read left to right) is either a 1 or a 5.

Because this is a base-6 definition, everything in my previous definition still works (N is a multiple of 6 in base-10, so all N numbers will convert to ending in a 0 in base-6; N + 1 or N - 1 in base-10 will convert to a number that ends in either 1 or 5 in base-6 respectively).

Under this new definition of a P class number, we can also include the number 1 (as it is a number whose last digit is either a 1 or a 5).

Now, my hypothesis is that a P class number must be either prime or its factors must also be P class numbers. Since prime numbers are divisible by 1 and 1 is a P class number, we can actually exclude the “prime” portion from the above and simply say: A P class number can only be divisible by another P class number.

To prove this, we need to demonstrate that A) any P class number multiplied by another P class number will give us yet a third P class number (which I demonstrated in my previous post) and B) no non P class number can ever be multiplied by any other number (P class or not) to form a P class number. So to prove it:

1. Any number that ends in digit x multiplied by a number that ends in 1 will have x as it’s terminating digit. Therefore, if multiplying by a P class number that ends in 1 to get another P class number, you must multiply by a number that ends in either 1 or 5. Therefore, multiplying a P class number that ends in 1 requires multiplying by another P class number to yield a P class number.

2. In an even base system (and base 6 is an even base system since 6 is even), any number x multiplied by any even number will be an even number. Examples (in base-6):

3 x 2 = 10 (x = 3)
4 x 2 = 12 (x = 4)
12 x 4 = 52 (x = 12)

Therefore, any number multiplied by 0, 2, or 4 must be an even number and cannot end in either 1 or 5.

3. An even number multiplied by a number that ends in 3 will end in 0 in base-6 (we don’t really need to demonstrate this step, since even numbers were excluded in step 2, but I’m including it just to be complete); an odd number multiplied by a number that ends in 3 will end in 3 in base-6. Example:

2 x 3 = 10.
3 x 3 = 13.
4 x 3 = 20.
5 x 3 = 23.

Therefore, no number multiplied by a number ending in 3 could end in 1 or 5.

4. This leaves only 5. Taking a look at the multiplication table of 5 in base-6 is quite interesting:

5 x 1 = 5
5 x 2 = 14
5 x 3 = 23
5 x 4 = 32
5 x 5 = 41
5 x 10 = 50

If you note, this has the same type of thing we find in base-10 systems with the number 9. In base 10, you can construct the multiples of 9 by simply writing a column of numbers from 0 - 9. Then, to the right of each number, write the inverse (9 - 0) next to the previous number. Hence:

09
18
27
36
45
54
63
72
81
90

Here, the same thing is seen in base-6, only with the range of 0-5:

05
14
23
32
41
50

Likewise, just as the digits in the 9 sequence add up to 9 (i.e. 18 is 1 + 8 = 9, 36 is 3 + 6 = 9), so in base-6 in the 5 sequence the digits add up to 5 (23 is 2 + 3 = 5; 41 is 4 + 1 = 5). I’m guessing (though I haven’t tested it) that this is the case of any even base system. (I.e., given base N where N is an even number, then the multiplication table of N - 1 will display the above behavior.)

In any case, that’s just something I discovered but it’s not relevant to the current discussion. What is relevent is this: a number that ends in 5 can only end in 1 if multiplied by a number that ends in 5; and a number that ends in 5 can only end in 5 if multiplied by another number that ends in 1. Therefore, the only way to make another P class number multiplying by a P class that ends in 5 is if you multiply it by another P class number.

Conclusion: A P class number can only be divisible by another P class number.

May 6, 2008: 10:30 am: CalvinDudeMath

I’m sure you’ve probably heard the phrase, “It just clicked into place.” Last night (or rather, very early this morning) I experienced that. Literally. Like it was an actual audible “click” sound as a realized something regarding the prime numbers in the “factor field” that I’ve developed in Excel.

To give some background, I’ve been conversing with someone via e-mail after my post the other day that included my reference to the factor field. This person has looked over my spreadsheet and given some comments, and last night I responded to him. Which meant that in the process I was looking over the sheet a great deal and doing lots of mathematical conversions and the like.

Anyway, I went to bed after I sent the e-mail. And at about 12:30 in the morning, I suddenly shot up in bed because I heard the “click” as something slid into place in my brain. Yeah, I did the whole caricature thing of having the light dawn on me :-)

Of course the only problem is that there’s like maybe a dozen people on Earth who would care about my realization, and I don’t know any of them personally. But I figure why not post it into the Internet anyway? So I will.

First, I should note that with the factor field, I’ve mentioned the “spike” that occurs spaced out every 6 digits. Because of this, I wanted to see what prime numbers would look like in base-6 format (using only 0-5 for your digits, just as binary uses only 1 and 0). Last night, I compiled a short list of some of the primes and e-mailed them to the person I’ve been corresponding with, so here’s the list of primes from 2 - 101 with their corresponding base-6 conversion:

2 = 2
3 = 3
5 = 5
7 = 11
11 = 15
13 = 21
17 = 25
19 = 31
23 = 35
29 = 45
31 = 51
37 = 101
41 = 105
43 = 111
47 = 115
53 = 125
59 = 135
61 = 141
67 = 151
71 = 155
73 = 201
79 = 211
83 = 215
89 = 225
97 = 241
101 = 245

And just for fun, converting the last 10 primes on the Excel sheet gives us this:

65413 = 1222501
65419 = 1222511
65423 = 1222515
65437 = 1222541
65447 = 1222555
65449 = 1223001
65479 = 1223051
65497 = 1223121
65519 = 1223155
65521 = 1223201

So as you can see, all the prime numbers after 2 & 3 end in either 1 or 5 in base-6.

Now because my hypothesis (which I lack the mathematical skills to prove beyond a shadow of a doubt) is that all prime numbers end in 1 or 5 in base-6, as I was trying to fall asleep I thought: “At what point do the prime numbers interfere with the 6-spike?” That is, at what point on the number series do prime numbers fall either 1 above or 1 below the spike (1 above the spike corresponds to a number ending in 5, one below corresponds to a number ending in 1).

Obviously 2 and 3 are ruled out from the get-go, because 2 x 3 creates the 6-spike. So I started with 5. And here’s what I got:

In this graphic, the blue lines are the 6-spike. The red cells are those that occur either 1 above or 1 below the 6-spike. The black cells are the other cells that do not fall either one above or one below the 6-spike.

I left the factors in the cells too. As a result, trace the 5-line down and you see that the first time it falls 1 above or 1 below the 6-spike (1 above in this case) is at 5 x 1. The next time it falls 1 above or 1 below the 6-spike (1 below in this case) is at 5 x 5. The next time (1 above) is at 5 x 7. Then again (1 below) at 5 x 11. Finally, it comes 1 above at 5 x 13.

Now look at the 7 line. 7 does the exact same thing but with the above/below polarity switched! The first time it appears is 1 below at 7 x 1. Then at 1 above at 7 x 5, etc. We see the same thing with the 11 and 13 lines. Thus we have:

N = multiple of 6.

N - 1 goes in an above/below sequence.

N + 1 goes in a below/above sequence.

And the real kicker…the N +/- 1 is itself the number that the factors are based on! Thus, take a factor of 6. Subtract 1. It is now 1 below a factor of 6. Multiply by 5 (i.e. 6 -1) and you will be 1 above a factor of 6. Multiply by 7 (i.e. 6 + 1) and you will be 1 below a factor of 6. Multiply by 11 (i.e. [2 x 6] – 1) and you will be one above a factor of 6. Multiply by 13 (i.e. [2 x 6] + 1) and you will be one below a factor of 6.

Let’s give an example. 24 is a factor of 6.

24 – 1 = 23. 23 x 5 = 115. 115 – 1 = 114. 114 = 6 x 19.

24 + 1 = 25. 25 x 5 = 125. 125 + 1 = 126. 126 = 6 x 21.

24 – 1 = 23. 23 x 7 = 161. 161 +1 = 162. 162 = 6 x 27.

24 + 1 = 25. 25 x 7 = 175. 175 – 1 = 174. 174 = 6 x 29.

24 + (2 x 6) – 1 = 35. 35 x 5 = 175. 175 – 1 = 174. 174 = 6 x 29.

24 + (2 x 6) + 1 = 37. 37 x 5 = 185. 185 + 1 = 186. 186 = 6 x 31.

24 + (2 x 6) – 1 = 35. 35 x 7 = 245. 245 + 1 = 246. 246 = 6 x 41.

24 + (2 x 6) + 1 = 37. 37 x 7 = 259. 259 – 1 = 258. 258 = 6 x 43.

So, to generalize it further, let us define an N class number as a positive number that is divisible evenly by 6.

1. (Nx - 1) x (Ny - 1) = X. X – 1 is an N class number.
2. (Nx + 1) x (Ny - 1) = X. X + 1 is an N class number.
3. (Nx - 1) x (Ny + 1) = X. X + 1 is an N class number.
4. (Nx + 1) x (Ny +1) = X. X – 1 is an N class number.

To test this, let Nx = 36 and Ny = 12.

1. (36 – 1) x (12 – 1) = 35 x 11 = 385. Subtract 1 and 384 = 6 x 64.
2. (36 + 1) x (12 -1) = 37 x 11 = 407. Add 1 and 408 = 6 x 68.
3. (36 – 1) x (12 +1) = 35 x 13 = 455. Add 1 and 456 = 6 x 76.
4. (36 + 1) x (12 + 1) = 37 x 13 = 481. Subtract 1 and 480 = 6 x 80.

But we can further generalize this by creating a new class, which I will call the P class. The P class is defined as any number that is N +/- 1. So take any N class, add or subtract one from it, and that is a P class number. From the above, we therefore know that any P class multiplied by another P class number yields another P class number. It comes in the following format.

Let us define Pdown as a N – 1 class number and Pup as an N + 1 number.

1. Pdown x Pdown = Pdown.
2. Pup x Pdown = Pup.
3. Pdown x Pup = Pup.
4. Pup x Pup = Pdown.

Now my theory is that all prime numbers are P class numbers, but not all P class numbers are prime numbers. After all, since a P class x a P class yields a P class, then we have proof that P classes can exist with factors. But here’s my theory on that: the only P class numbers that are not primes are those P classes that are created by multiplying other P class variables.

In other words, when thinking about primes, one need not worry about anything other than P class integers.

Let me explain by showing the first few primes again. After 2 and 3 (which create the 6-spike in the first place) we have 5, 7, 11, 13, 17, 19. Each of these shows both sides of the 6-spike.

The first “break” occurs after 23, because 25 has factors. But what are the factors of 25? Only 5 x 5. And 5 is a prime number. In fact, 5 is the smallest prime number that comes into play (again, because 2 and 3 are working to create the 6-spike so they are irrelevant here). In fact, if we multiply the smallest relevant primes, we get:

5 x 5 = 25.
5 x 7 = 35
7 x 7 = 49
5 x 11 = 55
5 x 13 = 65
7 x 11 = 77
5 x 17 = 85
7 x 13 = 91
5 x 19 = 95

And these results are all the numbers that are missing from the 6-spike as primes.

In any case, I think it’s safe to say that we can define a prime number as any P class number that is not divisible by any other P class number. And I also think that P class number that are divisible by any numbers at all are only divisible by other P class numbers. Therefore, we need not worry about any other numbers when testing for primes.

May 4, 2008: 8:41 pm: CalvinDudeMath, Philosophy, Science

One thing that can be both a blessing and a curse about my nature is that I am often able to find ways to keep myself awake almost all night long. It doesn’t matter how tired I am when I try to go to bed, if I think of something that gets my mind going then I’d much rather continue to think on it than sleep. This happened to me the other day as I was thinking of a few statistical quirks regarding Natural Selection, random mutations, and the like.

Unfortunately, I’m not yet able to write the blog post that I wanted to write, because while I know exactly what I’m talking about, it lacks sufficient groundwork for many other readers to be able to follow along! Since I am an apologist at heart (one who would love to preside over the complete destruction of ideological Darwinism, mind you) I do wish to expand on my thoughts and present them to others, so this leaves me with the necessary task of providing some starting groundwork before I get to the main point. And besides, although it’s tangential to my ultimate point, some of this stuff is just plain kewl :-)

In any case, since a great deal of what I will be focusing on in future posts will deal with statistical analysis, I thought it might be beneficial to give a quick overview of The Three Prisoners Problem in order to A) melt your brain if you’ve never heard of it and B) show how statistics can be logical and yet make no sense at first glance (mostly due to a wrong perspective).

The Three Prisoners Problem was originally mentioned by Martin Gardner in his “Mathematical Games” column in the October, 1959 edition of Scientific American, but under the Monty Hall guise (its mathematical equivalent) it has gathered more infamy, especially after Marilyn vos Savant’s article in Parade magazine in 1990. If this doesn’t make sense to you at first glance, you can take comfort in the fact that it has fooled Nobel laureates, professional mathematicians, and Mensa members countless times. Here I will give my own version of the problem.

There are three prisoners in the king’s dungeon: Adam, Bill, and Charlie. The Warden arrives at each cell and says, “The King has decided that two of you shall go free tomorrow.” At this, there is great rejoicing. But the Warden continues: “However, one of you will be executed.”

“Who will it be?” they all ask in turn.

The Warden responds: “The King has told me who will be executed, but he has also forbidden me telling you who will live and who will die.”

Each of the prisoners accepts this answer except for Charlie. Charlie is a shrewd character and because he knows the Warden is scrupulously honest, he asks: “I know you said that you cannot tell me who will be executed or who will be set free, and therefore you cannot tell me my fate. But will you instead give me one name of one of the other prisoners who will be set free?”

The Warden thinks about this for a moment. “Why would you want to know that?” he ponders. “If I don’t give you a name, you know that you have a 1/3 chance of being executed and a 2/3 chance of going free. If I tell you a name, then you will only have a 1/2 chance of going free! It is better for you if you do not know a name.”

“In that case,” Charlie responds, “why not tell me?”

The Warden relents and says, “Adam will go free tomorrow.”

At this, Charlie sits back and smiles because the Warden has inadvertently told him that it is twice as likely that Bill will be executed as it is that he will be executed…

The reason this is a “problem” is because for most of us we reason the way that the Warden did. Surely telling Charlie that Adam will go free has actually reduced Charlie’s odds of survival, hasn’t it? It used to be 2/3 because it could have been Adam, Bill, or Charlie who would be killed and 2 of them would have lived. But now it’s either Bill or Charlie who will be killed and only one of them would live, and that’s a 1/2 chance, isn’t it?

There are two ways to look at this. First, let’s look at the mathematical rule involved: fractional statistics must together add up to 1.

When the prisoners are first given information, there is a 1/3 chance for each of them that they will be killed. Thus, we have the odds of death being:

Adam = 1/3
Bill = 1/3
Charlie = 1/3

Now when Charlie asks which of the first two prisoners will go free, since the Warden is honest, he tells him that Adam is one who will go free. But this gives no new information to Charlie about whether or not Charlie will die. Charlie’s odds of being killed remain 1/3. However, Adam’s odds of being killed are reduced to 0. He will survive.

If Charlie has a 1/3 shot of dying and Adam has a 0 shot of dying, then because statistics must balance to 1 (it is a certainty that someone will die), this means that Bill’s odds of dying must be 2/3. As a result, Bill is twice as likely to be executed as Charlie.

Of course, this still doesn’t seem right at all! After all, how can telling Charlie that Adam will go free affect Bill’s odds of survival but not affect Charlie’s original odds of survival?

The second way of explaining this helps to flesh it out a bit better. As we stated, when the problem begins, each prisoner has a 1/3 chance of being killed. Therefore, there are three possible options. Let us examine these three options and what the Warden must respond under each option.

Option 1: Adam is killed. If Adam is the one to be executed, then when Charlie asks for the name of one of the two prisoners who will live, the Warden must respond “Bill.” If he says Adam lives, then he has lied (and we’ve stipulated that the Warden is honest). Conclusion: Charlie lives; the prisoner not named dies.

Option 2: Bill is killed. Like the above, the Warden’s choice is restricted to one answer. The Warden can only say that “Adam” will live. Conclusion: Charlie lives; the prisoner not named dies.

Option 3: Charlie is killed. Here is the only instance where the Warden has freedom. Since Charlie will be killed, then he can name either Adam or Bill. Conclusion: Charlie dies; the prisoner not named lives.

As we see in the above, Charlie’s chances of being killed remain 1/3 because only under option 3 does he die. Further, 2/3 of the time the Warden is forced to name a specific prisoner because the one not named is the one who will die. Therefore, 2/3 of the time the prisoner not named is the prisoner who will be executed.

This is also easier to see if we use bigger numbers. Suppose that there are instead 1,000 prisoners and all but one of them will be set free while the remaining prisoner is executed. Under these circumstances, the Warden reveals 998 prisoners who will be set free, leaving only Charlie and prisoner number 473 behind. Which is more likely, that the Warden was forced to leave prisoner number 473 as an option or that Charlie is going to be killed and prisoner 473 was a random selection? Obviously, there is only a 1/1000 chance that prisoner 473 was a random selection, but there is a 999/1000 chance that prisoner 473 was the forced choice. So in this case, the reason it is counterintuitive has more to do with the fact that we do not realize the Warden is excluding all but one prisoner from his answer. If there were 1,000 prisoners total and Charlie asked for the list of 998 of them that would go free, the Warden would immediately spot this error.

Note, however, that even under the circumstance that Charlie only asked for the name of one prisoner out of the 1000 who would go free, that would decrease the odds of all the other unnamed prisoners surviving, although in this instance the amount the odds change would be negligible. Charlie would remain with a 1/1000 chance of dying, while the 998 unnamed prisoners would have just over a 1.001/1000 chance of dying and the one named one would have a no (0) chance of dying. This equates to 998 prisoners splitting a 999/1000 odds, so you still end up with 1/1000 + 999/1000 + 0 = 1. (1.001 x 998 rounds to 999.)

As I mentioned at the top of this post, this is mathematically equivalent to the Monty Hall Problem. That can be demonstrated while keeping with the prisoner motif in the following manner. Suppose that instead of the Warden talking to the prisoners, the King summons the Warden to his throne room. The King, who enjoys tormenting the Warden, says:

“Warden, I am going to execute two prisoners tomorrow, but I am going to free one of them. I have written his name down and locked it in this chest beside me along with one thousand gold pieces. If you can guess who will go free, you can have all the gold in the chest. If you do not guess who goes free, you will have to join the prisoners being executed!”

The Warden realizes he has a 1/3 chance of gaining riches and a 2/3 chance of dying. Nevertheless, the King has given him no option. So he says, “I pick Adam to live.”

The King smiles and says, “Let us make this more interesting. Before you open the chest and see the name, I will tell you that Charlie is going to die. Now, do you still want to choose Adam to live, or do you want to switch your choice to Bill?”

At this point, what should the Warden decide?

Again, mathematically this is equivalent to the Three Prisoners Problem above. Therefore, we know that when the Warden picked Adam to live, he had a 1/3 chance of being right. The King has now informed the Warden that Charlie will die: therefore, Charlie has a 0 chance of living. Once again, because the numbers have to add up to 1, this means that Bill now has a 2/3 chance of living and Adam only has a 1/3 chance of living. Therefore, the Warden should switch his choice.

And to demonstrate this in the similar manner as above, look at the three options of what would happen after the Warden picks Adam but before the King (who already know who will die) responds:

Option 1: Adam lives. In this case, the King can name either Bill or Charlie as dying. Therefore, the Warden should not switch his choice because whomever the King does not name of the other two prisoners will die.

Option 2: Bill lives. In this case, the King MUST name Charlie as dying. The Warden should change his pick to the prisoner not mentioned (Bill).

Option 3: Charlie lives. In this case, the King MUST name Bill as dying. The Warden should change his pick to the prisoner not mentioned (Charlie).

Again we see that 2/3 of the time, the Warden should change his selection.

So here we see that sometimes statistics can be perfectly logical and rational, yet the result is so counterintuitive that they feel wrong. In my next post, I’ll give an example of the opposite: when statistics are irrational and yet seem to make sense. After that, I will look at a few examples statistics in action with Darwinism.

May 1, 2008: 11:17 pm: CalvinDudeApologetics, Evolution, Math, Philosophy, Science

One of the best offenses against Darwinism is the teleological argument. In fact, that is what Intelligent Design is (teleology = the study of design). This is most damaging to the Darwinist position because on the one hand Darwinists will repudiate teleology, but on the other hand they will employ it at every corner. To give examples of both in the same book, Ernst Mayr wrote:

Another widespread erroneous view of natural selection must also be refuted: Selection is not teleological (goal-directed). Indeed, how could an elimination process be teleological? Selection does not have a long-term goal. It is a process repeated anew in every generation.

Mayr, E. (2001). What Evolution Is. New York: Basic Books. p. 121

Yet Mayr also writes:

When the selective advantage of a skeleton developed among the ancestors of the vertebrates and of the arthropods, the arthropod ancestors had the prerequisites for developing an external skeleton, and the vertebrate ancestors for developing an internal skeleton. The entire evolution of these two large groups of organisms has since been affected by this choice among their remote ancestors.

(ibid, p. 141, emphasis added).

Evolution is an opportunistic process. Whenever there is an opportunity to outcompete a competitor or to enter a new niche, selection will make use of any property of the phenotype to succeed in this endeavor.

(ibid, p. 221, emphasis added).

Likewise, we read:

The legitimate use of the term adaptation is for a property of an organism, whether a structure, a physiological trait, a behavior, or anything else that the organism possesses, that is favored by selection over alternate traits. But the term also has been used quite incorrectly for the process (”adaptation”) by which the favored trait was actively acquired. This view can be traced back to the ancient belief that organisms had an innate capacity for improvement, for steadily becoming “more perfect.” Also, if one accepts an inheritance of acquired characters, activities such as the straining of the neck by giraffes “adapts” the neck to an improved construction. In this view, adaptation is an active process with a teleological basis. Some recent authors still seem to look at adaptation as such a process and therefore reject the whole concept of adaptation. But this is not defensible.

(ibid, p. 150).

Yet of adaptations, we read:

The shift from the quadropedal locomotion of a lizardlike reptile to bipedalism and flight in birds initiated a considerable restructuring of the body plan: a compacting of the whole body to have a better center of gravity, the development of a more efficient four-chambered heart, restructuring of the respiratory tract (lungs and air sacs), endothermy, improved vision, and an enlarged central nervous system. The acquisition of all of these adaptations was a matter of necessity.

(ibid, p. 219, emphasis added).

But Mayr is not the only one who falls prey to this. Indeed, when trying to describe their theories Darwinists are forced to use teleological representations. For instance, Gould wrote:

The model of the grabbag is a taxonomist’s nightmare and an evolutionist’s delight. Imagine an organism built of a hundred basic features, with twenty possible forms per feature. The grabbag contains a hundred compartments, with twenty different tokens in each. To make a new Burgess creature, the Great Token-Stringer takes one token at random from each compartment and strings them all together. Voilà, the creature works–and you have nearly as many successful experiments as a musical scale can build catchy tunes. The world has not operated this way since Burgess times. Today, the Great Token-Stringer uses a variety of separate bags–labeled “vertebrate body plan,” “angiosperm body plan,” “molluscan body plan,” and so forth. The tokens in each compartment are far less numerous, and few if any from bag 1 can also be found in bag 2. The Great Token-Stringer now makes a much more orderly set of new creatures, but the playfulness and surprise of his early work have disappeared. He is no longer the enfant terrible of a brave new multicellular world, fashioning Anomalocaris with a hint of arthropod, Wiwaxia with a whiff of mollusk, Nectocaris with an amalgam of arthropod and vertebrate.

Gould, S. J. (1989). Wonderful Life. New York: W. W. Norton & Company, Inc. p. 217-218

Naturally, Gould was trying to be poetic; but one wonders if it is even possible for him to explain his “grabbag” idea without resorting to the teleology of a designer (in the above case, the “Great Token-Stringer”). One suspects not. And those outside the field of biology are oblivious to the fact that evolution is supposed to be non-teleological. In fact, they see quite the opposite. For example, James Gleick in his book on the Chaos Theory wrote:

In science, on the whole, physical cause dominates. Indeed, as astronomy and physics emerged from the shadow of religion, no small part of the pain came from discarding arguments by design, forward-looking teleology–the earth is what it is so that humanity can do what it does. In biology, however, Darwin firmly established teleology as the central mode of thinking about cause. The biological world may not fulfill God’s design, but it fulfills a design shaped by natural selection. Natural selection operates not on genes or embryos, but on the final product. So an adaptationist explanation for the shape of an organism or the function of an organ always looks to its cause, not its physical cause but its final cause. Final cause survives in science wherever Darwinian thinking has become habitual. A modern anthropologist speculating about cannibalism or ritual sacrifice tends, rightly or wrongly, to ask only what purpose it serves. D’Arcy Thompson saw this coming. He begged that biology remember physical cause as well, mechanism and teleology together. He devoted himself to explaining the mathematical and physical forces that work on life. As adaptations took hold, such explanations came to seem irrelevant. It became a rich and fruitful problem to explain a leaf in terms of how natural selection shaped such an effective solar panel. Only much later did some scientists start to puzzle again over the side of nature left unexplained. Leaves come in just a few shapes, of all the shapes imaginable; and the shape of a leaf is not dictated by its function.

Gleick, J. (1987). Chaos: Making a New Science. New York: Penguin Books. p. 201-202

Because of this cognitive dissonance, teleology works well against Darwinists. If something looks designed, the simplest and straightforward reason is that it’s because it was designed. It is because of how much design is apparent in the living world that Dawkins had to take the time to pen The Blind Watchmaker in the first place. If nature didn’t have the designed appearance of a watch, Dawkins wouldn’t have needed to try to come up with an alternate explanation for it.

So teleology has found a niche in anti-Darwinian circles. I, however, would like to expand it out a bit further than that. Most recently, I’ve been studying cryptology as part of my endeavors to better understand such things as information theory, etc. Cryptology is also important since I enjoy dissecting Darwinist arguments and DNA happens to be very prominent in many of them. Since DNA is a “living code” understanding certain principals of cryptology can be beneficial.

Surprisingly, however, my thoughts have strayed from their original course in biology. The living order is teleological, and it is difficult for anyone to honestly look at it and yet still deny the inherent design. But so too is the non-living universe. Teleology surrounds us everywhere we look. It is not just in living systems, but anywhere that there is a system. And because of that, my original focus and my original purpose for reading up on cryptology (besides the fact that I’m weird and actually enjoy the subject) has expanded somewhat.

All reality is teleological.

Since my thinking has come about as the result of reading on cryptology, it perhaps wouldn’t hurt if I gave the specific example that got me thinking on this issue. William Friedman, who was instrumental in the US breaking of the Japanese cipher PURPLE in World War II, wrote The Index of Coincidence and Its Applications in Cryptography in 1920 when he was 28 years old. It was later updated somewhat after Friedman found the solution for a cipher machine using cryptographic rotors. David Khan, in The Code-Breakers, illustrates the theory in this manner:

Imagine an urn containing one each of the 26 letters of the alphabet. The chance of drawing any specified letter, say r, is one in 26, or 1/26. Now imagine another, identical urn. The chance of drawing an r is equally one in 26, or 1/26. What are the odds of drawing a pair of r’s, one after another, in a two-draw situation? The likelihood of drawing the second r is 1/26 of the chance of drawing the first, which is 1/26. So the chance of drawing two r’s in a single event, or “simultaneously,” one from each urn, is 1/26 x 1/26. Similarly, the probability of drawing two a’s is 1/26 x 1/26, of two b’s 1/26 x 1/26, and so on. Consequently, the chance of drawing a pair of letters—any pair of letters, no matter which pair may come up—is the sum of all these probabilities. It is (1/26 x 1/26) + (1/26 x 1/26) + … + (1/26 x 1/26), repeated 26 times, or 26 x (1/26 x 1/26), or 1/26. This quantity may be written as the decimal 0.0385.

Assume now an ideal cryptosystem whose ciphertexts yield a perfectly flat frequency count—one with as many a’s as b’s as c’s…as z’s. Polyalphabetics approach this in varying degrees and may, for practical purposes, be regarded as generating such ciphertexts. These texts are called “random” because they are what would be obtained if letters were drawn at random from the urn (each letter being replaced after being noted and the urn shaken to mix the lot, chance alone dictating their identities). If two such random texts are superimposed, the chance that the letter above will be the same as the letter below is the same as the chance of drawing a pair of identical letters from the two urns. This is 0.0385, or, to put it another way, there will be 3.85 such coincidences in every 100 vertical pairs. Experiment will confirm this.

Now imagine an urn filled with 100 letters of English in the proportion in which they are used in normal text—8 a’s, 1 b, 3 c’s, 13 e’s, and so on. The chance of drawing a specified letter is now proportional to its frequency. The probability that an a will emerge is 8/100ths, that a e will is 13/100ths. With two such urns, the chance of drawing two a’s is, as before, the product of the individual probabilities, or 8/100 x 8/100; the chance of drawing two e’s is consequently 13/100 x 13/100. And the probability of drawing a pair—any pair—of identical letters is the sum of all these pair-probabilities: (8/100 x 8/100) + (1/100 x 1/100) + (3/100 x 3/100) …, and so on through all 26 letters. This calculation has been made (with a slightly different frequency table). The result is 0.0667.

These two plaintext urns may likewise be replaced by two strings of plaintext. If they are superimposed, there will be as much likelihood that two letters will coincide vertically as there was that two identical letters will be drawn from the two urns. This probability is 0.0667, or 6.67 coincidences per 100 paris. For example:


text A wheninthecourseofhumaneventsitbecomesnecessaryforo
text B fourscoreandsevenyearsagoourfathersbroughtforthupo

text A (cont.) nenationtodissolvethepoliticalbandsthathaveconnect
text B (cont.) nthiscontinentanewnationconceivedinlibertyanddedic

There are just seven coincidences in the 100 pairs—precisely what theory predicts.

…[O]ne must recognize first that the superimposition of two monalphabetically enciphered texts will result in the…figure of about 6.67 coincidences per 100 vertical pairs, or 6.67 per cent of coincidences. This is because the coincidences will occur whether the letters are clothed in ciphertext disguises or not. The calculation does not ask the letters for their identities. It merely notes their coincidence. By the same token—and this is important—two polyalphabetic cryptograms enciphered in the same key and superimposed so that the two occurrences of that key are in synchronization with one another will also show 6.67 per cent of coincidences. The reason is this: In a correct (in-phase) superimposition, the two letters of each vertical pair have the same keyletter. Thus whenever a coincidence occurs in the plaintext, the letters of the pair will be identically enciphered. This results in an identical pair—a coincidence—in the ciphertext. It does not matter that a pair of e’s may be enciphered into V’s at one point and into Q’s at another, or that a coincidence of a’s becomes a coincidence of L’s here and a coincidence of F’s there. The toal number of coincidences will remain the same as the number in the plaintext.

On the other hand, if the two cryptograms are improperly superimposed, so that the keys are not in step, any coincidences will result from different keyletters operating on different plaintext letters to accidentally produce the same ciphertext letter. The coincidences will be caused, in other words, by chance. Chance alone will produce 3.85 coincidences per 100 vertical pairs in random text, and polyalphabetic ciphertext is equivalent to random text. Hence an incorrect superimposition should yield about 3.85 per cent of coincidences. But 3.85 per cent is substantially less than 6.67 per cent, and so a comparison of the percentages of coincidences at various test superimpositions should show which superimposition is correct.

An example should make things clear. A cryptosystem with the Vigenère running key THE BARD OF AVON IS THE AUTHOR OF THESE LINES…starts the key for the first message with the first keyletter, but starts the key for successive messages with the third, fifth, and so on, keyletters. If plaintext 1 is If music be the food of love, play on, and plaintext 2 is Now is the winter of our discontent, the encipherment will be these:

key             THEBARDOFAVONISTHEAUTHOROFTH
plaintext 1     ifmusicbethefoodofloveplayon
ciphertext 1    BMQVSZFPJTCSSWGWVJLIOLDCODHU

key         (TH)EBARDOFAVONISTHEAUTHOROFTHESE
plaintext 2     nowisthewinterofourdiscontent
ciphertext 2    RPWZVHMERWABWKVJOOKKWJQTGAIFX

A cryptanalyst, receiving these two cryptograms, will superimpose them so that they start at the same point:

ciphertext 1  BMQVSZFPJTCSSWGWVJLIOLDCODHU
ciphertext 2  RPWZVHMERWABWKVJOOKKWJQTGAIFX

Since there are 28 vertical pairs, the cryptanalyst would expect 28 x 0.0667 coincidences or 1.8676, or about 2, for a proper superimposition. But in fact he finds none, so he shifts the second cryptogram one space to the right and tries again. There will now be 27 vertical pairs. The cryptanalyst again calculates the theoretical expected number of coincidences for random and for correctly superimposed texts of this length so that he may compare the values with what he actually observes. Thus, a wrongly superimposed text would yield 27 x 0.0385 = 0.9695, or about 1 coincidence that would produced by chance alone, while a correct superimposition would yield 27 x 0.0667 = 1.2369. (These fractional differences become more pronounced with longer texts.) One coincidence appears….

Since the differences between the chance and the caused values are so slight with so few letters, the cryptanalyst might wonder whether this is not in fact a random result (which in fact it is…) and try the next superimposition. Here the number of coincidences immediately jumps. This superimposition is obviously correct.

ciphertext 1  BMQVSZFPJTCSSWGWVJLIOLDCODHU
ciphertext 2    RPWZVHMERWABWKVJOOKKWJQTGAIFX

If the cryptanalyst wishes to continue, he will find that at the next superimposition the number of coincidences falls again, to 2, and will return to begin his attack with the third superimposition…

Kahn, D. (1967, 1996). The Codebreakers. New York: Scribner. p. 377-380.

With this as the immediate background, I’ll simply note how my train of thought has progressed. When dealing with language, we are dealing with something that we know is designed. Language requires intelligence, and this is even more evident when it comes to written text. Because text is a product of intelligence, it will always display the hallmark of intelligence. One will be able to differentiate between that which is designed and that which is random.

The above examples demonstrate it beautifully. Take the illustration of putting the opening line of the Declaration of Independence above the opening of the Gettysburg address. Because both texts were written in English, and because English is designed rather than random, English traits will carry through. There will be vertical alignment of almost 7%. Random texts only have 3%. Because this is the case, even hiding English within a cipher does not destroy these traits, although it obscures it at first glance.

Design, therefore, is something that would permeate everything. It might not be immediately apparent at first glance, but there will be traits that can be sought mathematically that will yield results nowhere near what random results would give us.

Now obviously when one thinks about living systems, one can see that there are processes at work that are not random. Even the relatively simple actions of an ion pump inside a cell demonstrate values that are not what one would find in a random environment. A cell becomes charged due to the existence of these ion pumps (which is how the electrical pulse can travel the nerve), but under random circumstances the charge would dissipate.

Indeed, when thinking of what is truly random one immediately must think of entropy. The less entropy there is in a system, the less random it is. If a room has low entropy, it is because everything is ordered. If it has high entropy, it is randomized. The more ordered something is, the less random it must be.

This brings us immediately to questions of the universe as a whole. And not just in terms of entropy amongst galaxies and such. Instead, I want to ask more foundational questions.

Suppose we see iron filings arrayed on a table next to a magnet. The filings will lay in a particular pattern and won’t lay randomly. Why is this the case? Of course the immediate answer is because magnetic forces have arranged the iron filings in that manner. But why is it that magnetic forces would act in that manner? We can dig into the quantum levels, perhaps. But that merely begs the question: why is it that those quantum particals act the way they do? What is it that causes electrons to be repulsed from one another? What is it that causes protons to attract electrons? Why is it that these things always happen this way, that there is no variance…no randomness to it?

Even things that are apparently random turn out to hide hidden order. Take radioactivity for instance. Radioactive elements are used to produce random cipher keys even, because no one can predict when an alpha particle will decay. But despite how “random” the decay is, radioactive elements always decay at a specific rate. Despite the random nature, there is an over-riding law that stipulates what the half-life of that radioactive element will be. We may not be able to predict when the next alpha particle will decay, but we know that after a set amount of time exactly half of the element will have decayed.

Is that not an instance of the non-random showing itself? Like the cipher text that cannot help but display the design of the English language, if one but knew where to look, don’t the underlying laws that govern all the universe scream out that there is underlying order to even what we think is chaos?

Earlier I quoted Gleick’s comment about the shape of leaves, which are governed not by forces of Natural Selection but instead by fractal designs. The key there is “designs.” All of reality is based on these deep, inherent designs. And these designs cannot be random because they are, in fact, distinct from what we would see in a purely random field.

Naturally I know that some chaoticians say that order springs from chaos, and they will use mathematical representations of chaos to illustrate this…all the while ignoring the fact that the mathematical system that they are using to generate those fractals is itself non-chaotic. Indeed, as some may already know I’ve spent lots of time playing with what I call the “Factor Field.” It’s an Excel program that I made (you can e-mail me if you want a copy using my yahoo account. Simply put “petedawg34” and follow it with “@” and finish with “yahoo.com”, and yes defeating spambots is always fun). The Factor Field is simply a graphical representation of integers. The left-most column counts by 1. The second column by 2s. Etc. Because I used Excel, it only shows 256 wide, but it goes 65,536 deep. Here is but one example of what you can see at cell number 60,480:

This shows what I call a “starburst” pattern. You can also see the skeletons of parabolas in there, as well as many different lines of various slopes. All this was created by putting integers in patterns next to each other.

If you were to isolate some of the pixels on the right side of the graphic, the dots would look very chaotic. There would not appear to be any particular rhyme or reason for any of them to be where they are. Yet they came about due to a specific rule. There is an underlying order that created the seeming randomness that is seen. And stepping back, viewing it from the distance where one can see the whole starburst, the order is obvious.

Likewise, the factor field can make it easy to find if a number is a prime number, but it doesn’t make it any easier to predict prime numbers that aren’t shown on the graph (although via observation, I hypothesize that all prime numbers greater than 3 are numbers that end in either 1 or 5 in base-6, but that’s another blog post for another time). One can tell that previous portions of the graph affect later portions, but it is so complex that it is difficult for humans to predict how the effects will play out “off screen.”

This interplay of chaos and order is only possible because the structure of the factor field is built on order. It’s an order that displays chaotic behavior later on, but it remains order. Likewise, all the representation of chaos theory are built on mathematical models that are, themselves, strict. Math doesn’t randomly make 1 + 1 = 7. It cannot happen. And the rules of chaos mean that doing the same math formula over with the exact same data will yield the exact same result. That there are wild differences if the data is even minorly tweaked doesn’t change the fact that not tweaking it yields identical results.

In other words, even in the most random systems we can think of, because they are real, have order underlying them. Reality is not random. Reality is, at heart, the opposite of random. And what is the opposite of randomness?

Design.

April 23, 2008: 12:13 pm: CalvinDudeEvolution

I TOLD YOU SO!

The producers of Expelled made the Dawkin’s rap video :-) (Click the “I told you so” line above to see the ending of the clip.) :-D

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