KOMEGA REQUIREMENTS: Start with a Political Program

Integrating Engineering and the Human Factor (info@uffmm.org) eJournal uffmm.org ISSN 2567-6458, Nov 23-28, 2020
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

As described in the uffmm eJournal  the wider context of this software project is a generative theory of cultural anthropology [GCA] which is an extension of the engineering theory called Distributed Actor-Actor Interaction [DAAI]. In  the section Case Studies of the uffmm eJournal there is also a section about Python co-learning – mainly
dealing with python programming – and a section about a web-server with
Dragon. This document is part of the Case Studies section.

CONTENT

Applying the original P-V-Pref Document structure to real cases it became clear that the everyday logic behind the classification of facts into problems [P] or  visions [V] follows a kind of logic hidden in the semantic space of the used expressions. This text explains this hidden logic and what this means for our application.

PDF DOCUMENT

VIDEO [DE]

REMARK

(After first presentations of this video)

(Last change: November 28, 2020)

Confusion by different meanings

While the general view of the whole process is quite clear there arose some hot debate about the everyday situation of the experts (here: citizens)  and the concepts ‘reality [R]‘, ‘vision [V] (imagination of a  state which is not yet real)’, ‘problem [P]‘, and ‘preference [Pref]‘. The members of my zevedi-working group (located at the INM (Frankfurt, Hessen, Germany) as well as a citizen from Dieburg (Hessen, Germany) associated with ‘reality’ also the different kinds of emotions being active in a person and they classified an imagination about a future state also as being real in a concrete person. With such a setting of the concepts it became difficult to motivate the logic illustrated in the video. The video — based on the preceding paper — talks about  a vision v, which can turn a reality r into a problem p, and thereby generating a preference Pref = (v,r). A preference can possibly become a trigger of  some change process.

Looking ahead

Before clarifying this discussion let as have a look ahead to the overall change process which constitutes the heart of the komega-software.  Beginning with October 18, 2020 the idea of this overall change process has been described in this blog. Having some given situation S, the komega software allows the construction of change rules X,  which can be applied onto a given situation S and a builtin simulator [sim] will generate a follow up situation S’ like sim(X,S)=S’ — or short: X(S) = S’ –, a process which can be repeated by using the output S’ as new input for a new cycle. At any time of this cyclic process one can ask whether the actual output S’ can be classified as successful. What is called ‘successful’ depends from the applied criteria. For the komega software at least two criteria are used. The most basic one looks to the ectual end state S’ of the simulation and computes the difference between the occurences of vision statements V in S’ and the occurrences of real statements R having been declared at the beginning as problems P as part of the  start situation S. Ideally the real statements classified as problems should have been disappeared and the vision statements should be present.  If the difference is bigger than some before agreed threshold theta  than the actual end state S’ will be classified as a success, as a goal state in the light of the visions of the preferences, which triggered the change process.

Vision statement

In the context of the whole change process a vision statement is an expression e associated with some everyday language L and which describes in the understanding of the experts a state, which is in our mindes conceivable, imaginable, which is not given as a real state, but can eventually  become a real state in some future. This disctinction presupposes that the expert can distinguish between an idea in his consciousness which is associated with some real state outside his consciousness — associated with a real state — and an idea, which is only inside his consciousness — associated with an imaginated state –.  Looking from a second person to the expert this second person can observe the body of the expert and the world surrounding the body and can speak of the real world and the real body of the expert, but the inner states of the expert are hidden for this second person. Thus from the point of view of this second person there are no real imaginations, no real future states. But the expert can utter some expression e which has a meaning describing some state, which as such is not yet real, but which possibly could become real if one would change the actual reality (the actual everyday life, the actual city …) accordingly.  Thus a vision statement is understood here as an expression e from the everyday language L uttered by some expert having a meaning which can be understood by the other persons describing some imginated state, which is not yet real but could eventually become real in some future ahead.

Creating problems, composing preferences

If at least one vision statement v is known by some experts, then it can happen, that an expert does relate this vision with some given reality r as part of the everyday life or with some absent reality r. Example: if an expert classifies some part of the city as having too much traffic (r1) and he has the vision of changing this into a situation where the traffic is lowered down by X% (v1), then this vision statement v1 can help to understand other experts to interpret the reality r1 in the light of the visiin v1 as a problem v1(r1) = p1. Classifying some reality r1 into a problem p1 is understood in the context of the komega software as making the reality r1 a candidate for a possible change in the sense that r1 should be replaced by v1. Having taken this stance — seeing the reality r1 as a problem p1 by the vision v1 –, than the experts  have created a so-called preference Pref = (v1, p1) saying that the experts are preferring the imaginated possibly future state v1 more than the actual problem p1.

There is the special case, that an expert has uttered a vision statement v but there is no given reality which can be stated in a real statement r. Example: A company thinks that it can produce some vaccine against the  disease Y in two years from now, like  v2=’there is a vaccine against disease Y in yy’. Actually there exists no vaccine, but a disease is attacking the people. Because it is known, that the people can be made immune against the disease by an appropriate vaccine it makes sense to state r2=’There is no vaccine against the disease Y available’. Having the vision v2 this can turn the reality r2 into a problem p2 allowing the preference Pref=(v2,p2).

Triggering actions

If a group of experts generated a vision v — by several and different reaons (including emotions) –, having  associated this with some given eality r, and they decided to generate by v(r)=p  a preference Pr =(v,p),  then it can happen , that these experts decide to start a change process beginning now with the given problem p and ending up with a situation in some future where the problem p disappeared and the vision has become real.

Summing up

The komega software allows the planning and testing of change processes  if the acting experts have at least one preference Pref based on at least one  vision statement v and at least one real statement r.

BITS OF PHILOSOPHY

Shows the framework for the used concepts from the point of view of philosophy
Philosophical point of view

The above video (in German, DE) and the following  lengthy remark after the video how to understand the basic concepts vision statement [v],  real statement [r], problem statement [p], as well as preference [Pref] presuppose both a certain kind of philosophy. This philosophical point of view is outlined above in a simple drawing.

Basically there is a real human person (an actor) with a real brain embedded in some everyday world. The person can perceive parts of the every day world at every point of time. The most important reference point  in time is the actual moment called NOW.

Inside the brain the human person can generate some cognitive structure triggered by perception, by  memory and by some thinking.  Having learned some everyday language L the human person can map the cognitive structure into an expression E associated with the language L. If the cognitive structure correlates with some real situation outside the body then the meaning of the expression E is classified as being a real statement, here named E1.  But the brain can generate also cognitive structures and mapping these in expressions E without being actually correlated with some real situation outside. Such a statement is here called a vision statement, here named E2. A vision statement can eventually become correlated with some real situation outside in some future. In that case the vision statement transforms into a real statement E2, while the before mentioned real statement E1 can lose its correlation with a real situation.

FURTHER DISCUSSIONS

For further discussions have a look to this page too.

 

KOMEGA REQUIREMENTS No.1. Basic Application Scenario

KOMEGA REQUIREMENTS No.1. Basic Application Scenario

ISSN 2567-6458, 26.July – 11.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

As described in the uffmm eJournal  the wider context of this software project is a generative theory of cultural anthropology [GCA] which is an extension of the engineering theory called Distributed Actor-Actor Interaction [DAAI]. In  the section Case Studies of the uffmm eJournal there is also a section about Python co-learning – mainly
dealing with python programming – and a section about a web-server with
Dragon. This document will be part of the Case Studies section.

PDF TEXT:

requirements-no1-v3-11Aug2020 (published: Aug-11, 2020; this version replaces the version from 7.August 2020)

requirements-no1-v2-2-7Aug2020 (published: Aug-7, 2020; this version replaces the version from 6.August 2020)

requirements-no1-v2-6Aug2020 (published: Aug-6, 2020; this version replaces the version from 25.July 2020)

requirements-no1-25july2020-v1-pub (published: July-26, 2020)

ACTOR-ACTOR INTERACTION ANALYSIS – A rough Outline of the Blueprint

eJournal: uffmm.org,
ISSN 2567-6458, 13.February 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last corrections: 14.February 2019 (add some more keywords; added  emphasizes for central words)

Change: 5.May 2019 (adding the the aspect of simulation and gaming; extending the view of the driving actors)

CONTEXT

An overview to the enhanced AAI theory  version 2 you can find here.  In this post we talk about the blueprint  of the whole  AAI analysis process. Here I leave out the topic of actor models (AM); the aspect of  simulation and gaming is mentioned only shortly. For these topics see other posts.

THE AAI ANALYSIS BLUEPRINT

Blueprint of the whole AAI analysis process including the epistemological assumptions. Not shown here is the whole topic of actor models (AM) and as well simulation.
Blueprint of the whole AAI analysis process including the epistemological assumptions. Not shown here is the whole topic of actor models (AM) and as well simulation.

The Actor-Actor Interaction (AAI) analysis is understood here as part of an  embracing  systems engineering process (SEP), which starts with the statement of a problem (P) which includes a vision (V) of an improved alternative situation. It has then to be analyzed how such a new improved situation S+ looks like; how one can realize certain tasks (T)  in an improved way.

DRIVING ACTORS

The driving actors for such an AAI analysis are at least one  stakeholder (STH) which communicates a problem P and an envisioned solution (ES) to an  expert (EXPaai) with a sufficient AAI experience. This expert will take   the lead in the process of transforming the problem and the envisioned  solution into a working solution (WS).

In the classical industrial case the stakeholder can be a group of managers from some company and the expert is also represented by a whole team of experts from different disciplines, including the AAI perspective as leading perspective.

In another case which  I will call here the  communal case — e.g. a whole city —      the stakeholder as well as the experts are members of the communal entity.   As   in the before mentioned cases there is some commonly accepted problem P combined  with a first envisioned solution ES, which shall be analyzed: what is needed to make it working? Can it work at all? What are costs? And many other questions can arise. The challenge to include all relevant experience and knowledge from all participants is at the center of the communication and to transform this available knowledge into some working solution which satisfies all stated requirements for all participants is a central  condition for the success of the project.

EPISTEMOLOGY

It has to be taken into account that the driving actors are able to do this job because they  have in their bodies brains (BRs) which in turn include  some consciousness (CNS). The processes and states beyond the consciousness are here called ‘unconscious‘ and the set of all these unconscious processes is called ‘the Unconsciousness’ (UCNS).

For more details to the cognitive processes see the post to the philosophical framework as well as the post bottom-up process. Both posts shall be integrated into one coherent view in the future.

SEMIOTIC SUBSYSTEM

An important set of substructures of the unconsciousness are those which enable symbolic language systems with so-called expressions (L) on one side and so-called non-expressions (~L) on the other. Embedded in a meaning relation (MNR) does the set of non-expressions ~L  function as the meaning (MEAN) of the expressions L, written as a mapping MNR: L <—> ~L. Depending from the involved sensors the expressions L can occur either as acoustic events L_spk, or as visual patterns written L_txt or visual patterns as pictures L_pict or even in other formats, which will not discussed here. The non-expressions can occur in every format which the brain can handle.

While written (symbolic) expressions L are only associated with the intended meaning through encoded mappings in the brain,  the spoken expressions L_spk as well as the pictorial ones L_pict can show some similarities with the intended meaning. Within acoustic  expressions one can ‘imitate‘ some sounds which are part of a meaning; even more can the pictorial expressions ‘imitate‘ the visual experience of the intended meaning to a high degree, but clearly not every kind of meaning.

DEFINING THE MAIN POINT OF REFERENCE

Because the space of possible problems and visions it nearly infinite large one has to define for a certain process the problem of the actual process together with the vision of a ‘better state of the affairs’. This is realized by a description of he problem in a problem document D_p as well as in a vision statement D_v. Because usually a vision is not without a given context one has to add all the constraints (C) which have to be taken into account for the possible solution.  Examples of constraints are ‘non-functional requirements’ (NFRs) like “safety” or “real time” or “without barriers” (for handicapped people). Part of the non-functional requirements are also definitions of win-lose states as part of a game.

AAI ANALYSIS – BASIC PROCEDURE

If the AAI check has been successful and there is at least one task T to be done in an assumed environment ENV and there are at least one executing actor A_exec in this task as well as an assisting actor A_ass then the AAI analysis can start.

ACTOR STORY (AS)

The main task is to elaborate a complete description of a process which includes a start state S* and a goal state S+, where  the participating executive actors A_exec can reach the goal state S+ by doing some actions. While the imagined process p_v  is a virtual (= cognitive/ mental) model of an intended real process p_e, this intended virtual model p_e can only be communicated by a symbolic expressions L embedded in a meaning relation. Thus the elaboration/ construction of the intended process will be realized by using appropriate expressions L embedded in a meaning relation. This can be understood as a basic mapping of sensor based perceptions of the supposed real world into some abstract virtual structures automatically (unconsciously) computed by the brain. A special kind of this mapping is the case of measurement.

In this text especially three types of symbolic expressions L will be used: (i) pictorial expressions L_pict, (ii) textual expressions of a natural language L_txt, and (iii) textual expressions of a mathematical language L_math. The meaning part of these symbolic expressions as well as the expressions itself will be called here an actor story (AS) with the different modes  pictorial AS (PAS), textual AS (TAS), as well as mathematical AS (MAS).

The basic elements of an  actor story (AS) are states which represent sets of facts. A fact is an expression of some defined language L which can be decided as being true in a real situation or not (the past and the future are special cases for such truth clarifications). Facts can be identified as actors which can act by their own. The transformation from one state to a follow up state has to be described with sets of change rules. The combination of states and change rules defines mathematically a directed graph (G).

Based on such a graph it is possible to derive an automaton (A) which can be used as a simulator. A simulator allows simulations. A concrete simulation takes a start state S0 as the actual state S* and computes with the aid of the change rules one follow up state S1. This follow up state becomes then the new actual state S*. Thus the simulation constitutes a continuous process which generally can be infinite. To make the simulation finite one has to define some stop criteria (C*). A simulation can be passive without any interruption or interactive. The interactive mode allows different external actors to select certain real values for the available variables of the actual state.

If in the problem definition certain win-lose states have been defined then one can turn an interactive simulation into a game where the external actors can try to manipulate the process in a way as to reach one of the defined win-states. As soon as someone (which can be a team) has reached a win-state the responsible actor (or team) has won. Such games can be repeated to allow accumulation of wins (or loses).

Gaming allows a far better experience of the advantages or disadvantages of some actor story as a rather lose simulation. Therefore the probability to detect aspects of an actor story with their given constraints is by gaming quite high and increases the probability to improve the whole concept.

Based on an actor story with a simulator it is possible to increase the cognitive power of exploring the future even more.  There exists the possibility to define an oracle algorithm as well as different kinds of intelligent algorithms to support the human actor further. This has to be described in other posts.

TAR AND AAR

If the actor story is completed (in a certain version v_i) then one can extract from the story the input-output profiles of every participating actor. This list represents the task-induced actor requirements (TAR).  If one is looking for concrete real persons for doing the job of an executing actor the TAR can be used as a benchmark for assessing candidates for this job. The profiles of the real persons are called here actor-actor induced requirements (AAR), that is the real profile compared with the ideal profile of the TAR. If the ‘distance’ between AAR and TAR is below some threshold then the candidate has either to be rejected or one can offer some training to improve his AAR; the other option is to  change the conditions of the TAR in a way that the TAR is more closer to the AARs.

The TAR is valid for the executive actors as well as for the assisting actors A_ass.

CONSTRAINTS CHECK

If the actor story has in some version V_i a certain completion one has to check whether the different constraints which accompany the vision document are satisfied through the story: AS_vi |- C.

Such an evaluation is only possible if the constraints can be interpreted with regard to the actor story AS in version vi in a way, that the constraints can be decided.

For many constraints it can happen that the constraints can not or not completely be decided on the level of the actor story but only in a later phase of the systems engineering process, when the actor story will be implemented in software and hardware.

MEASURING OF USABILITY

Using the actor story as a benchmark one can test the quality of the usability of the whole process by doing usability tests.

 

 

 

 

 

 

 

 

 

 

 

AAI THEORY V2 – DEFINING THE PROBLEM

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.