The complex prime root of Markov Chains

A Markov Matrix can be raised to a power and give the general occurrence of a process. This is not a reversible process as a lesser complexity does not describe the means by which it is created. The sum or difference or other operation does not contain the methods of its origin. I would suppose the the valuable part of the whole is not the starting point or the ending point, but the action in combination with the origin or the product. However a vector and a point can describe a path of action in either direction.



\begin{bmatrix} x&y\\ \end{bmatrix}

It is said that the genome of the human and apes varies by less than a percent and that genetic variation in the human population is even less than that. What this suggests to me is that as much as people wish to ascribe good and evil to others, it is a slice from the same cloth and the predisposition to conflict is inherent in the design. Thus the attribution of evil is a reflection of intent and not a proper empirical condition. As beauty is in the eye of the beholder, evil resides behind the eyes as well. Thus the purpose of the ascription is simply to facilitate the desired action.

A consequence of some research into the structure and function of various systems leads me to conclude that some of these inherent vectors tend to different end points based on initial conditions. It would seem that, as with the Markov Chains, the end point can be known if the initial conditions and vector are known. In more complex systems there is not final product and the system achieves modes which correspond to what I assume would be called strange attractors when considering a system above a certain complexity. The time variance of two stochastic processes would certainly seem to produce results in much the same way as the interaction of a mixed frequency source. If both influence the same parametric then it would have a multi-modal product. This has more information about the process than a solution to a simple MC.

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