Choice V

 

Freedom of Choice

A victim of collision on the open sea

Nobody ever said that life was free

Sink, swim, go down with the ship

But use your freedom of choice

But it is your choice, not my choice.

Variance, σ², is the number that expresses the range of choices.  If there are two choices, e.g. a two‑sided coin, then the two choices are 1, heads and 2, tails, and the square root of the variance, σ²,  is 1/6 because the mean choice, µ, is 1.5, and 1.5 plus 3σ, where σ is the square root of the variance, includes those two choices.  For a six-sided die, with outcomes of 1, 2, 3, 4, 5 or 6, the mean choice is 3.5, the square root of the variance is 2.5/3=.83333, and the variance is 0.69444 .  For a one-sided die, a mobius strip, where the choice is only 1, the mean choice is 0.5, which is also the odds, and the variance is 1/36.  If there is no choice, then the variance is NOT zero. It is undefined.  Acting as if there is no choice, which is virtually identical to saying that everyone should be making my choice, may be why we are in the current dilemma. 

Saying that Lies are equal to Truth means that there were two choices.  Saying that you have chosen Truth and everyone else should choose Truth does not change the fact that there were originally two choices, not one choice.  Saying that there is Truth and No Truth, but Truth is greater than No Truth means that there is only one choice,  i.e. is pro-choice, not no choice.  

The following graphs are intended to visualize the problem.  The exponential distribution is one attempt to show how outcomes are distributed given an input.  Its Probability Density Function, PDF, is λe^-λx and its Cumulative Distribution Function, CDF, is 1-e^-λx for x>0.  This can be coordinate transformed to a new location from a location of 0 to a location of μ, as PDF =λe^-λ(x­-μ) and CDF  Cumulative Distribution Function as 1-e^-λ(x-μ) , both for x>µ. However all inputs, not just x>µ , should be considered and the exponential distribution can only consider a limited number of inputs.  It might be mirrored to consider all inputs.  

A random normal distribution is the logistics distribution whose PDF is ½*(1/2s)*sech²((x-μ)/2s) and whose CDF is ½*(tanh((x-μ)/2s)+1).  S is a range variable that is related to the variance by σ²=s²π²/3.  If the variance is equal to the odds for one choice, Random Normal 2 , then its PDF looks like the exponential distribution and its mirror PDF.  However if s, not the variance, is equal to the odds for one choice, Random Normal 1, then its  PDF no longer looks like that of the PDF for the exponential distribution and its mirror.  The exponential distribution and its mirror also appears more similar to two choices rather than one choice when its CDF is considered.  And the CDF is absolutely not like no choice which would be a flat line.

Calling the variance, the odds for choice, because you don’t like the choice is no different than calling yourself Indiana because you don’t like the name Junior.  https://www.youtube.com/watch?v=DYtWWLqeSKA  It does not change the fact that your name is Henry Junior, and the dog’s name was Indiana.