Filibuster V

 

 

Happiness Is

Happiness is finding a pencil.
Pizza with sausage.
Telling the time.
Happiness is learning to whistle.
Tying your shoe for the very first time.

You’re a Good Man, Charlie Brown.

Senator Ted Cruz tried to dis former White House Press Secretary Jen Psaki by calling her Peppermint Patty. It did not work because Ms. Psaki was flattered and liked Peppermint Patty. But it did point out that right-wing "Republicans" apparently read Peanuts.

I would like to make a point on the filibuster using Peanuts, the comic strip by the late, great Charles Schultz. Every fall, Charlie Brown tried to kick a football held by Lucy, only to land on his back when Lucy yanks the ball away at the last second. Charlie Brown keeps playing, and Lucy keeps frustrating him. It is a two player game.  Does anyone seriously expect Linus, or Rerun, or Sally, or Schroder, or Jackson .... or even Peppermint  Patty ....to yank the football away. ( Snoopy might… but only to be funny). 

The point is that there are different outcomes and strategies for a two-player game than for a multi-player game.  If Senator Chuck “Charlie Brown” Schumer wants to play with Senator Mitch “Lucy Van Pelt” McConnell and try to pass a bill, i.e. kick a football, then the result is that the bill is filibustered, i.e. the ball is yanked away. This is because it is a two-player, Democrat vs. Republican, game. It was never supposed to be like this. There are 100 Senators, not 2 Senators. The problem is that if Republican Senators don’t vote with him, Senator McConnell and his Party will retaliate against those Republican Senators. Because of that threat, it reverts to a two-player game. However if votes to end the filibuster are secret, he does not have enough information to retaliate. If Senator Schumer plays secretly with, e.g. Senator Romney, perhaps he will get a chance to kick the ball.

And that's the way it could be Charlie Brown.

Normal

It Had To Be You

Some others I've seen might never be mean
Might never be cross or try to be boss,
But they wouldn't do.
For nobody else gave me a thrill.
With all your faults, I love you still,
It had to be you, wonderful you,
It had to be you.

So what does the Mean have to be for things to be “normal”.

Nerd alert. 

If there is an absolute truth, then the number of samples can be as small as one and you should still  find the same value, i.e. truth. The Standard Error is defined as the square root of the Variance divided by the square root of the sample size. Thus if the value is an absolute, and there is no error, then the Variance must also be zero.

Scientists try to reduce error and establish absolute values. In doing so they are reducing the Variance of those observations of the absolute. If the Variance is one billionth of a percent then, this is not zero, but it is very, very close to zero. The Standard Deviation is the square root of the Variance. If the Variance is one-billionth, then the Standard Deviation is 32 one-millionth of a percent. 

While reducing the Standard Error also means reducing the Variance and the Standard Deviation, knowing the upper bound of the Standard Deviation can also give the upper bound of the Skew of the distribution. If you know the Standard Deviation, then you know what percentage of the values that fall within multiples of that Standard Deviation. For example, in a normal distribution, 68.27 % of the values fall within one Standard Deviation from the Mean; 95.45% of the values fall within 2 Standard Deviations from the Mean; 99.73% fall within 3 Standard Deviations from the Mean. The Three Sigma (three Standard Deviations), rule is generally sufficient in the physical sciences. In particle physics, the standard is Five Sigma  (99.99994%). 

This rule requires that a distribution be somewhat normal. While the usual intent is to lower the variance, given a Mean and the requirement that the minimum value of a distribution is zero, it is possible to estimate the required Standard Deviation. If 99.7% of the values fall within 3 Standard Deviations of the Mean, and the minimum value must be zero, then the Standard Deviation must be approximately the Mean divided by three. The are several distributions that are somewhat normal but allow for a non-zero skew.    

A common one is the exponentially modified Gaussian (normal) distribution. In probability theory, an exponentially modified Gaussian distribution describes the sum of independent normal and exponential random variables. It will not allow any non-zero values. An exponentially modified Gaussian distribution will have a skew between 0.0 ( closest to a pure normal distribution) and 0.31 (closest to a pure exponential distribution). In an exponentially modified Gaussian distribution, the mean will always be greater than the median, but the closer the mean is to the median, the smaller the skew. Given a maximum Skew of 0.31, Pearson’s Second Coefficient of the Skew, and a Standard Deviation of Mean/3, then the Median must be greater than 0.897 times the Mean, which is equivalent to saying that Mean must be less than 1.15 times the Median in order for this to be an exponentially modified Gaussian (normal) distribution. 

As noted above, normal distributions follow the 68/95/99.7 rule.  That is 68% of the values are in the range Mean ± 1 Standard Deviation;  95% of the values are in the range Mean ± 2 Standard Deviations; and 99.7 % of the values are in the range Mean ± 3 Standard Deviations.  As noted above, in particle physics the standard is  99.99994% of the values are in the range Mean ± 5 Standard Deviations.  By making the Standard Deviation as small as possible the range of possible values becomes the Mean.  In normal distributions, the Mean is equal to the Median.  In slightly Skewed “normal” distributions, the 68/95/99.7 rule this means that 100% is approximately equal to 99.7%. This means that 100% is in the range Mean ± 3 Standard Deviations and 50% is in the range 0 to the Median. This means that a distribution is "normal" if (Mean- 3 * Standard Deviations)/2 = Median.  The Standard Deviation must be 1.5 divided by the Median multiplied by the Mean . If the ratio of the Mean to the Median exceeds 1.5, then the underlying distribution can not be “normal”.  A distribution can be made up of several “normal” distributions, but it might itself not be "normal".

Critical Race Theory IV

 

I Won’t Grow Up

If growing up means it would be beneath my dignity to climb a tree
I'll never grow up, never grow up, never grow up....Not me
Not I
Not me (not me)

We should want our children to know that it will NEVER be beneath their dignity to climb a tree.

I know that I will vote against Article 20 at the Mansfield Town Meeting on May 17^th, 2020. That article is “To see if the Town will vote to prohibit public K-12 schools in the Town of Mansfield from teaching, instructing or training students to adopt or believe, or causing anyone else to teach, instruct or train any students to adopt or believe, certain DIVISIVE CONCEPTS, regarding race or sex.”   Is it necessary to explain what these DIVISIVE CONCEPTS are?

In trying to understand this article I found not only Michigan State Senator Mallory McMorrow’s speech   https://www.youtube.com/watch?v=iLWo8B1R0MY but the latest episode of This American Life. https://www.thisamericanlife.org/605/kid-logic.

Senator McMorrow said that supporting the rights of marginalized people does not mean that you are yourself a marginalized person. Characterizing those that oppose you as being groomers threatening children is vile, hateful, and untrue.

This American Life, which shall forever be immortalized by the line in The O.C, the teenage Prime Time Soap Opera on Fox,  as “the show where hipster know-it-alls talk about how fascinating ordinary persons are” presented an episode on how Kid Logic is different than Grown Up Logic. I will never forget the face of my college  roommate Keith when he found out the phase he learned as a child, “It’s a doggie dog world”, was actually “It is a dog eat dog world.”

When I was in college the phrase “My Country, Right or Wrong” was claimed to mean that you love your country. The best response to that is that the full phrase should be “My County Right or Wrong. If it is Right, keep it Right. If it is Wrong, make it Right.”  That is loving your county. Teaching children that you country  was never wrong is a DIVISIVE CONCEPT. You should know that you can climb a tree and still grow up.

Boldness vs. Caution

 

My Back Pages

Yes, my guard stood hard when abstract threats
Too noble to neglect
Deceived me into thinking
I had something to protect
Good and bad, I define these terms
Quite clear, no doubt, somehow
Ah, but I was so much older then
I’m younger than that now

Why are the young bold, when they should be cautious?

When you are six years old,  the time from one birthday to the next seems like forever.  It is 1/6 of your life.  When you are 70 years old, the time from one birthday to the next is an eyeblink.  It is 1/70 of your life.  The same effect in the reverse may be why the young are bold when they should be cautious, and why the old are cautious when they should be bold.

If life expectancy is 90 years old, when you are 20 years old, then each event in the future is only 1/70 of your life.  When you are 70 years old, each event in the future is only 1/20 of your life.  It becomes even more problematic when this is rounded to two decimals places. 1/70 is .0142571, which rounds to .01.  1/20 is .05, which rounds to .05.

Mastery of a subject is suppose to take 10,000 hours.  Most twenty-somethings can not be expected to have mastered any subject.  A seventy year old most probably has mastered at least one subject.

The risk of an action is the consequences of that action multiplied by its likelihood.  The consequences should be the same for both the 70-year old and the 20-year old.  If the mastery is not considered, but the life expectancy is considered, then the risk of the 20-year old is 1/5 the risk of a 70-year old.  The problem is that likelihood should be unrelated to how many time you play the game, i.e. your remaining life expectancy.  The likelihood without mastery, should be the same for young and old.  But mastery should be greater for old than young, which means that the likelihood and thus the risk, should be lower for the old than for the young.   The young may be bolder, but they should be even more cautious than the old.

The value of the future should be unrelated to your age.  But if you ignore any future that occurs after you have died, even if your group endures, you are not valuing any future events as being real. 

If the risks taken  when an individual is young are greater than the risks that same person takes when they are old, that might indicate that person only has an individual perspective, and does not have a group perspective. In those cases, youth is wasted on the young.

Mathematics

 

Would I Lie to You?

Would I lie to you?
Would I lie to you honey?
(Now honey would I lie to you?)
Now would I say something that wasn't true?
I'm asking you sugar
Would I lie to you?

If you use Mathematics, we don’t have to doubt, you CAN'T lie to me.

I have claimed that you can’t lie when you use mathematics.  Not only is such common a saying as “The Enemy of My Enemy is My Friend” provably false with mathematics, https://dbeagan.blogspot.com/2022/04/twitter.html, but this is true for other common sayings as well. 

“The ends justify the means” is not true in mathematics.  If a minus sign, a negative is bad, then while -5 * -5=25   creates a positive, but the ends, -5 and -5, are still negatives.

 “All is fair in love and war” is not true.  If Fair + Not Fair=All, then this is only True if Not Fair = 0, or Fair=0 in Love and War.  “Fair is foul,  Foul is fair” indeed. 

 “Might is Right” is not true.  Unless Right is wholly contained in Might,  if Might is ever Not  equal to Right then the statement is false. For “Might for Right” to be true, Right and Might only have to intersect.

Variance

Theme from  Batman TV Show

Batman, Batman, Batman
Da da da da da da da da da da da da da da da da da
Batman!

No, Not Batman! Flatman!

In 2006, my firm, Cambridge Systematics, was undertaking a study for the Massachusetts Governor’s Highway Safety Board, GHSB.  The GHSB was submitting  that study to the National Transportation Safety Board.  It required that the result be confirmed  by a certified statistician.  At the time, my former colleague, Kevin Tierney, and I were “chosen” as the potential certified statisticians.  Since it required attesting that the sampling plan for the survey in the memo was correct and since Kevin Tierney was a national expert ( although not certified) on survey design, the required attestation fell to him.  I joked with Kevin at the time that it was a good thing that  it did not require my signature because I was only certifiable, while he could claim to be certified. I am still certifiable (as insane) , but  I am not certified as a statistician, if there is such a thing. But I would like to think that I know something about statistics.

In Flatland, which was written in 1884, an analogy was proposed that if you limit your perception to a Flat, two-dimensional, plane then you will have a tough time explaining things such as spheres, etc.  In other words, perception matters.  Einstein’s Theory of General Relativity, in 1916,  explained the same concept in mathematical terms.  There can be an absolute, (i.e. the speed of light) and our perception of such things as length, weight, time etc., depend on the relationship to that absolute. Despite NOT being a statistician, I would like to use statistics, in the same manner as Flatland did, to make an analogy

If a group thinks that there is an absolute, i.e. the mean, and that group accepts no deviations, i.e. variance, from that absolute truth, then the group has a mean that is greater than 0, but variance of the group is zero. ( This probably also makes all other statistical moments of the distribution, such as the skew or the kurtosis, also zero).  What this defines is a single point. If there are any observations away from this point, then those observations can NOT be members of the group whose variance is zero.  In addition to many other things, distributions with zero variance are inherently unstable, that is they are NOT resilient.  If the truth, e.g. mean, is contradicted, for example, if the group absolutely believes that the World would end on December 21, 2012 when the Mayan Calendar Cycle ended, then when the world did not end, it does not require that the group be dissolved.  In a normal distribution the mean, median, and mode are equal.  If the variance is 0, then 100% of the group, every member, has the mean as their value.  In a uniform normal distribution, the variance is 1.0  The mean, median and mode are still equal, but only 40% of  members of that group are at that mean.  It is thus possible to be a member of the group and NOT have a value that is equal to the mean.  If the mean changes, is found not to be TRUE,  then the group does not have to dissolve.

A problem with a normal distribution is that it allows any observation to be less than zero.  In observations of real data, for example income, what is often required is a distribution that allows only non-zero values.  The exponential distribution is such a distribution, but it has the problem that the closer you get to zero, the larger the number of observations are expected at zero.  If the number of observations at zero should also be zero, but the distribution is still expected to be normal,  then an exponentially modified Gaussian ( normal) distribution is often used.  This distribution can be defined by three attributes: the mean, µ; the variance, σ; and the exponential parameter, λ.  Unlike a pure normal distribution, this distribution can be skewed.  The closer the distribution is to normal, the closer the skew is to zero.  But if the distribution tends towards the exponential, the maximum value of the skew is 0.31. 

So by analogy, a group should not have a variance of zero, belief in an absolute, and accept no deviation.  A normal group can be skewed, but that skew should not exceed 0.31.  If the skew is greater than zero, then there is no way that the variance can also be zero. If Flatland used mathematics to show that not everything could be explained by a flat plane, then statistics  says you can not accept a skew and expect a variance of zero.  Now you know, and knowing is half the battle! Go Flatman!

Winning III

 

Tubthumping

I get knocked down (we'll be singing)
But I get up again (pissing the night away)
You are never gonna keep me down (when we're winning)

Getting up again IS winning.

To err is human, to forgive is divine.  Humans can aspire to the divine, but we probably will not achieve it.  But we are human and we can, and will, err, get knocked down.  What is improbable is that humans will never err, get knocked down.  That is NOT winning.  Getting up again is winning.