Shannon's Journal Discussion

[Shannon]

Much to my shock and surprise, however, the models repeatedly and consistently have shown me that it is not running BAMM 2.0 that best leads to my financial goals in the long term, but running DMSI 3.0.1-A. I do not (yet) understand this, but I do know that when the models give the same exact answer 26 times in a row... it is 99.999999999999999% likely to be correct.

Hi Shannon,

I was wondering if you could explain in more detail, please, what do you mean about these models you're referring to.

What they are, how do you implement them and how you found them; when you have the time to do so, of course.

Just find the whole thing fascinating; maybe I can pick up a thing or two.

Many thanks.

I've explained this before. The models are currently a trio of software programs I wrote that use slightly different approaches to achieve the same goal. They're the result of literally decades of research, experimentation and development concerning the nature of time/space and a variety of prediction methods. When two or more agree within a specific percentage of difference, they have a history of being very accurate. And, when run against a series of queries that all deal with the same thing in a specific way (such that all have to be true by virtue of their answers and how their answers interact) , when they give answers that agree consistently, they are also very accurate.

For example. If I ask about how things will go for me in the next 4 years in terms of becoming a millionaire, and all three models give radically different answers, and do not agree, I know that the answer is incorrect, and the likely culprit is an error inputting the data required to run the models.

If two or more agree within their required minimum parameters, I know from observing the results of a long line of past runs that they are going to be accurate within a specific range according to how many agree and to what degree.

If I ask a series of related questions that would have to say specific things for the first question to be true, based on indicated timing and results, and they all agree with question #1, then I know that question #1's answer is increasingly likely to be correct according to how many such related questions agree with question #1's answer.

I found the way to these models by thinking outside the box and by realizing through my years of R&D that certain very fundamental assumptions about how things work are deeply flawed because of our point of view concerning them.

Once you consider them from a point of view of how they actually behave, it becomes possible to forecast what will happen with high accuracy, given enough of the right input data points and given that those input data points are accurate to begin with.