eJournal: uffmm.org,
ISSN 2567-6458, 23.Januar 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de 

CONTEXT

An overview to the enhanced AAI theory  version 2 you can find here.  In this post we talk about the first chapter where you have to define the problem, which should be analyzed.

DEFINING THE PROBLEM

1. Generally it is assumed that the AAI theory is embedded in a general systems engineering approach starting with the clarification of a problem.
2. Two cases will be distinguished:
   1. A stakeholder is associated with a certain domain of affairs with some prominent aspect/ parameter P and the stakeholder wants to clarify whether P poses some ‘problem’ in this domain. This presupposes some explained ‘expectations’ E how it should be and some ‘findings’ x pointing to the fact that P is ‘sufficiently different’ from some y>x. If the stakeholder judges that this difference is ‘important’, than P matching x will be classified as a problem, which will be documented in a ‘problem document D_p’. One can  interpret this   analysis as a ‘measurement M’ written as M(P,E) = x and x<y.
   2. Given a problem document D_p a stakeholder organizes an analysis to find a ‘solution’ which transfers the old ‘problem P’ into a ‘configuration S’ which at least should ‘minimize the problem P’. Thus there must exist some ‘measurements’ of the given problem P with regard to certain ‘expectations E’ functioning as a ‘norm’ as M(P,E)=x and some measurements of the new configuration S with regard to the same expectations E as M(S,E)=y and a metric which allows the judgment y > x.
3. From this follows that already in the beginning of the analysis of a possible solution one has to refer to some measurement process M with an accepted standard E, otherwise there exists no problem P and no possible solution.
   

EXAMPLE

The mayor of a city wants to know whether the finances of his city x are in a good state compared to some well accepted standards E. Already the definition of  a ‘good state’ of the finances can pose a problem.  Let us assume that such a standard E exists and the standard tells the mayor that a ‘good state’ for his finances would ideally equal y or all values ‘above y’. If the measurement M(x, E) would generate a result like x < y, then this would indicate in the ‘light of the standard E’ that his city has a problem P. Knowing this the mayor perhaps is interested to analyze this problem P by organizing a process which gives him as a result a configuration S which generates after a measurement M(S,E) the further result that x = y or even x > y. Thus this new configuration S would be an attractive state which should be a valuable goal state for his city.