The Kassien Theorem

The Kassien Theorem 
The detection and application of infinite variables of (X) found within the error margin. 

The error in a standard economic model can be eliminated by adding infinite variables of (X). The process of adding infinite variables is forever building. Computed decisions cannot be accurately and ethically made for all using one linear equation, but one always adding variables can. The error margin doesn’t show what we should accept, as it’s used today, but instead it is an indicator for where there are stray variables of (X) not yet defined. It’s where the automation to search for continually variables of (X) is.
The error margin also holds a new (Y) (when considering Y=X…). The Y is in flux only when the variable (or variables) of (X) are scouted by examining the cause, lead or production of the error margin. It is producing a (Y) that has not yet been fathomed by the original equation either by lead (having the second equation line equal a Y already determinable) or by producing a new Y (having the second equation equal a Y that was not previously determined before the error margin was scouted for the stray variable). 
When a new variable of (X) is added, a new constraint needs to be added and is derived from the reasoning of why the stray variable of (X) was in the error margin to begin with. 
This is an equation that is run twice before equaling. The equation must essentially be done twice to be done. 
The equal sign needed for this modeled equation does not yet exist. Its creation is necessary to understand and solve the equation. All equal signs account for all numbers being defined stationary (3 will always be 3) until infinity. In social sciences the equal sign is usually defined as equalled sometimes, but not always. This equal sign is not substantial or fitting for this modeled equation because in this model and other models built with this theorem as a base. This is because in this equation the equal is true as long as it’s only the defined variables being utilized. Numbers as variables can produce yes no or maybe. Current social science models can produce maybe yes or maybe no, due to the error margin. Inclusion of the error margin in the equation begins the process of adding additional variables of (X) not yet defined (not real or unreal, not yet defined). When a variable produces an error margin, that is one line of the equation. The next line of the equation is adding those variables of (X) with their new variables constraints by determining how they lead to the error margin being the second line. The one that is now able to be solved equal in the moment. True in the moment. Until an error margin is again produced by a variable of (X). Thus starting the process again. A forever fluxing equation that is true, but in flux. Until the equation is ran for the variable of (X) producing the (Y) that consists of an error margin, that variable of (X) is to be logically considered as non-existent. In the second line of the theorem,  the original equation is altered (with constraints and impact (addition vs multiplication) to include the reality of the new variable of (X) that was discovered. It’s an equation that is forever in flux. True at all times until it isn’t, but it will correct itself with the human intervention in the addition and full integration of new variables of (X) and the alteration of the overall results of (Y) possible if necessary. Then, once fully solved. The equation is true again. The human handling the model will have to monitor and account for all data available and that means data that comes after the fact. The error margin is an indicator for data that is now coming after the fact. Why there is two lines of the equation with the first equation line being altered and then solved. 
New Equal Sign
New variables exist in the constraint. The variables in the constraint are constant, but its effects or lack of (multiplication vs addition) is dependent on the variable). A constraint with the same variables can equal a different level of output in the solved Y depending on this factor. 
The equation itself is the mathematical representation of the human thought process. Can be used to final tune AI.
AI computing decisions are only made possible with the human application of additional variables of (X) that produce (or lead) to the error margin. 
This is where human judicial systems and AI systems meet. Each human is a variable of (X). No (X) can be grouped logically, because every human is different. (X) can only be grouped for non animated variables of (X), that are independent from human interference. No grouping if it is considered to be ethical in dealing with societal impacts models or judicial decisions made by the modeled equation. This is on the non negotiable basis that each man is different. No two men are the same, not ever. Similar at times, but not the same. 
For judicial AI practices this is a new version of an “appeal”. This is necessary for using machine derived decisions ethically. Every appeal will be the person's specific variable of (X) that has been found in examining the error margin in the first line. This will produce a new (Y) result or form a new (Y) result all together. Both the variable of (X) identification and the search for a possibility of a new (Y) (what is done in the second line, where solving for a true equal is now ready to begin) has to be done by man because only man can understand other man. 
After incarceration or disciplinary action, a new variable of (X) should be introduced. This is to account for the change the human underwent thus changing themselves as a variable of (X) thus producing a new variable of (X) that when applied to the model will result in an error margin first line and repeat of the process of the theorem for it to be equal and true again. The previous variable of (X) should still be included even if it’s determined the human of that variable of (X) has changed and became a new variable of (X). Variables of (X) and their corresponding restraints can only be added, never deleted. Once a variable of (X) is added, it should remain, however, in judicial decisions, the effect of the new variable of (X) should be the only one calculated for.

For judicial AI implications this means a need for expanded mental health services, expanded personality screenings, expanded record keep and as many specific metric scenarios as possibly accounted for. 
In conclusion each man is a variable of (X) in social sciences. It is possible to compute, but it requires an environment where there are infinite variables of (X). An infinite amount of data and constant rewriting of more variables of (X) and their interactions in the model/equation.