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Mathematical Matrix Insight

—–Original Message—–
From: Rivas J’Kara [mailto:x_rivas_x@hotmail.com]
Sent: Sunday, October 19, 2003 07:34 PM
To: jaced.com
Subject: Matrix Insight

I found your writings on The Matrix interesting. You talked a lot about computer programming, 2+2=4, etc. in your article. Something to consider… have you ever heard of a Stochastic Model? Or Bayesian math? Or a Kalman filter? Or maybe Numerical Methods? These are all related forms of mathematics… let me explain.

In very complex computer systems, such as aircraft navigation, statistical error models must be constructed that model the real world and make certain anticipations/assumptions. Think of probability theory wrapped up in matrix math wrapped up again in integral/derivative calculus equations. It works like this, a computer system uses inputs from sensors to observe the outside world, anticipate them, and then tries to react to those observations. The programmers of the system are smart enough however to know that the sensors themselves are prone to error, and not only that, the system you are observing, a real world system, will respond in certain ways you can not certainly predict all of the time because of a certain element of real-world randomness. So the best you can do is construct an error model of what the most likely outcomes of that particular observation will be. Over time as you make more observations your error model becomes increasingly more and more precise. You continuously refine your equations because the error rendered by your algorithm gets fed back into the systems where it is integrated (a calculus term) over and over again, causing the error to become increasingly smaller and smaller, until finally the error may still be present but it is small enough that it is of no concern to your overall larger solution… so you just ignore it. They will always be there, but they are no longer of concern. When the errors grow large enough to be a concern, you re-integrate the equation.

Occasionally highly improbable things can happen in the real world, which in turn will cause your error model to break down. When this happens hopefully the error is manageable and will eventually dissipate in time after multiple integrations of the error. If it is beyond your thresholds you will have to restart the system.

I found it interesting that The Architect calls Neo the “integral anomaly,” that he is the sixth emergence of the integral anomaly and that they need to “reinsert his code into the prime program….” This of course also means all the monitors on the walls behind Neo make a lot of sense given this type of math, this whole scene is about probability. And of course choice, or that certain real-world randomness, is the problem that probability theory is trying to construct a ruleset around.

I think Neo represents the completely improbable outcome, and the error model of The Matrix is now past all thresholds/points of return. This would mean that the third movie is a totally open book and anything can happen. This would also probably mean that Neo is human. Anyway it all makes perfect sense from a probability standpoint.

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