AAI THEORY V2 –TOP-DOWN OR BOTTOM-UP?

eJournal: uffmm.org,
ISSN 2567-6458, 26.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 third chapter where two different strategies are mentioned.

TOP-DOWN OR BOTTOM-UP?

The elaboration of an actor story AS will happen in a process driven by some actors, which communicate with each other and the environment. This can be done in various ways. Here we consider two main cases:

  1. Top-down: There exists a group of experts EXPs which will analyze a possible solution, will test these, and then will propose these as a solution for others.
  2. Bottom-up: There exists a group of experts EXPs too but additionally there exists a group of customers CTMs which will be guided by the experts to use their own experience to find a possible solution.

In reality  there can be many forms of collaboration which are mixing these two idealized cases. The top-down paradigm is very common although it produces many problems, especially in communal projects. A bottom-up process including the topic of ‘participation’ in communities and cities is today highly demanded, but not well specified.

In this book  the  bottom-up paradigm will be discussed explicitly.  This requires that the AAI experts collaborate from the beginning with a group of common users from the application domain. To do this they will (i) extract the knowledge which is distributed in the different users, then (ii) they will start some modeling from these different facts to (iii) enable some basic simulations. These simple simulations (iv) will be enhanced to   interactive simulations which allow serious gaming either (iv.a) to test the model or to enable the users (iv.b) to learn the space of possible states. The test case will (v) generate some data which can be used to evaluate the model with regard to pre-defined goals. Depending from these findings (vi) one can try to improve the model further.

EXAMPLE

The mayor of a city has the identified problem P that there exists a certain road which has a to high load of traffic. He wants to find a new configuration S which minimizes this problem without creating a new problem P’.

He decides to attack this problem not by delegating it to a group of experts only but to a group of experts collaborating with all the citizens which think to be affected by this problem and a possible solution. Thus the mayor opts for a bottom-up approach. This poses the challenge to find a procedure which enables the inclusion of the citizens in the overall process.

 

 

AAI THEORY V2 – DEFINING THE CONTEXT

eJournal: uffmm.org,
ISSN 2567-6458, 24.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 second chapter where you have to define the context of the problem, which should be analyzed.

DEFINING THE CONTEXT OF PROBLEM P

  1. A defined problem P identifies at least one property associated with  a configuration which has a lower level x than a value y inferred by an accepted standard E.
  2. The property P is always part of some environment ENV which interacts with the problem P.
  3. To approach an improved configuration S measured by  some standard E starting with a  problem P one  needs a process characterized by a set of necessary states Q which are connected by necessary changes X.
  4. Such a process can be described by an actor story AS.
  5. All properties which belong to the whole actor story and therefore have to be satisfied by every state q of the actor story  are called  non-functional process requirements (NFPRs). If required properties are are associate with only one state but for the whole state, then these requirements are called non-functional state requirements (NFSRs).
  6. An actor story can include many different sequences, where every sequence is called a path PTH.  A finite set of paths can represent a task T which has to be fulfilled. Within the environment of the defined problem P it mus be possible to identify at least one task T to be realized from some start state to some goal state. The realization of a task T is assumed to be ‘driven’ by input-output-systems which are called actors A.
  7. Additionally it mus be possible to identify at least one executing actor A_exec doing a  task and at least one actor assisting A_ass the executing actor to fulfill the task.
  8. A state q represents all needed actors as part of the associated environment ENV. Therefore a  state q can be analyzed as a network of elements interacting with each other. But this is only one possible structure for an analysis besides others.
  9. For the   analysis of a possible solution one can distinguish at least two overall strategies:
    1. Top-down: There exists a group of experts EXPs which will analyze a possible solution, will test these, and then will propose these as a solution for others.
    2. Bottom-up: There exists a group of experts EXPs too but additionally there exists a group of customers CTMs which will be guided by the experts to use their own experience to find a possible solution.

EXAMPLE

The mayor of a city has identified as a  problem the relationship between the actual population number POP,    the amount of actual available  living space LSP0, and the  amount of recommended living space LSPr by some standard E.  The population of his city is steadily interacting with populations in the environment: citizens are moving into the environment MIGR- and citizens from the environment are arriving MIGR+. The population,  the city as well as the environment can be characterized by a set of parameters <P1, …, Pn> called a configuration which represents a certain state q at a certain point of time t. To convert the actual configuration called a start state q0 to a new configuration S called a goal state q+ with better values requires the application of a defined set of changes Xs which change the start state q0 stepwise into a sequence of states qi which finally will end up in the desired goal state q+. A description of all these states necessary for the conversion of the start state q0 into the goal state q+ is called here an actor story AS. Because a democratic elected  mayor of the city wants to be ‘liked’ by his citizens he will require that this conversion process should end up in a goal state which is ‘not harmful’ for his citizens, which should support a ‘secure’ and ‘safety’ environment, ‘good transportation’ and things like that. This illustrates non-functional state requirements (NFSRs). Because the mayor wants also not to much trouble during the conversion process he will also require some limits for the whole conversion process, this is for the whole actor story. This illustrates non-functional process requirements (NFPRs). To realize the intended conversion process the mayor needs several executing actors which are doing the job and several other assistive actors helping the executing actors. To be able to use the available time and resources ‘effectively’ the executing actors need defined tasks which have to be realized to come from one state to the next. Often there are more than one sequences of states possible either alternatively or in parallel. A certain state at a certain point of time t can be viewed as a network where all participating actors are in many ways connected with each other, interacting in several ways and thereby influencing each other. This realizes different kinds of communications with different kinds of contents and allows the exchange of material and can imply the change of the environment. Until today the mayors of cities use as their preferred strategy to realize conversion processes selected small teams of experts doing their job in a top-down manner leaving the citizens more or less untouched, at least without a serious participation in the whole process. From now on it is possible and desirable to twist the strategy from top-down to bottom up. This implies that the selected experts enable a broad communication with potentially all citizens which are touched by a conversion and including  the knowledge, experience, skills, visions etc. of these citizens  by applying new methods possible in the new digital age.