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.

 

Extended Concept for Meaning Based Inferences. Version 1

ISSN 2567-6458, 30.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 DOCUMENT

TruthTheoryExtended-v1

The Simulator as a Learning Artificial Actor [LAA]. Version 1

ISSN 2567-6458, 23.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.

Abstract

The analysis of the main application scenario revealed that classical
logical inference concepts are insufficient for the assistance of human ac-
tors during shared planning. It turned out that the simulator has to be
understood as a real learning artificial actor which has to gain the required
knowledge during the process.

PDF DOCUMENT

LearningArtificialActor-v1 (last change: Aug 23, 2020)

CASE STUDIES

eJournal: uffmm.org
ISSN 2567-6458, 4.May  – 8.January 2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

In this section several case studies will  be presented. It will be shown, how the DAAI paradigm can be mapped into a  concept of Generative Cultural Anthropology [GCA] or Sociology. Then it will be shown how it is possible to implement the GCA concept or a Sociology concept in real world processes subsumed under the general concept of daily life. One popular perspective is that which deals with city planning and the participation of citizens in this process. The working title of the software project related to this is komega (later we will go back to an older concept already used in the past called OKSIMO (Open Knowledge Simulation Modeling)). Go back to the overall framework.

COLLECTION OF PAPERS

There exists only a loosely  order  between the  different papers due to the character of this elaboration process: generally this is an experimental philosophical process which will  partially be translated in software which can be used within experimental settings.

  1. From Men to Philosophy, to Empirical Sciences, to Real Systems. A Conceptual Network. (Last Change Nov 8, 2020)
  2. FROM DAAI to GCA. Turning Engineering into Generative Cultural Anthropology. This paper gives an outline how one can map the DAAI paradigm directly into the GCA paradigm (April-19,2020): case1-daai-gca-v1
  3. CASE STUDY 1. FROM DAAI to ACA. Transforming HMI into ACA (Applied Cultural Anthropology) (July 28, 2020)
  4. A first GCA open research project [GCA-OR No.1].  This paper outlines a first open research project using the GCA. This will be the framework for the first implementations (May-5, 2020): GCAOR-v0-1
  5. Engineering and Society. A Case Study for the DAAI Paradigm – Introduction. This paper illustrates important aspects of a cultural process looking to the acting actors  where  certain groups of people (experts of different kinds) can realize the generation, the exploration, and the testing of dynamical models as part of a surrounding society. Engineering is clearly  not  separated from society (April-9, 2020): case1-population-start-part0-v1
  6. Bootstrapping some Citizens. This  paper clarifies the set of general assumptions which can and which should be presupposed for every kind of a real world dynamical model (April-4, 2020): case1-population-start-v1-1
  7. Hybrid Simulation Game Environment [HSGE]. This paper outlines the simulation environment by combing a usual web-conference tool with an interactive web-page by our own  (23.May 2020): HSGE-v2 (May-5, 2020): HSGE-v0-1
  8. The Observer-World Framework. This paper describes the foundations of any kind of observer-based modeling or theory construction.(July 16, 2020)
  9. CASE STUDY – SIMULATION GAMES – PHASE 1 – Iterative Development of a Dynamic World Model (June 19.-30., 2020)
  10. KOMEGA REQUIREMENTS No.1. Basic Application Scenario (last change: August 11, 2020)
  11. KOMEGA REQUIREMENTS No.2. Actor Story Overview (last change: August 12, 2020)
  12. KOMEGA REQUIREMENTS No.3, Version 1. Basic Application Scenario – Editing S (last change: August 12, 2020)
  13. The Simulator as a Learning Artificial Actor [LAA]. Version 1 (last change: August 23, 2020)
  14. KOMEGA REQUIREMENTS No.4, Version 1 (last change: August 26, 2020)
  15. KOMEGA REQUIREMENTS No.4, Version 2. Basic Application Scenario (last change: August 28, 2020)
  16. Extended Concept for Meaning Based Inferences. Version 1 (last change: 30.April 2020)
  17. Extended Concept for Meaning Based Inferences – Part 2. Version 1 (last change: 1.September 2020)
  18. Extended Concept for Meaning Based Inferences – Part 2. Version 2 (last change: 2.September 2020)
  19. Actor Epistemology and Semiotics. Version 1 (last change: 3.September 2020)
  20. KOMEGA REQUIREMENTS No.4, Version 3. Basic Application Scenario (last change: 4.September 2020)
  21. KOMEGA REQUIREMENTS No.4, Version 4. Basic Application Scenario (last change: 10.September 2020)
  22. KOMEGA REQUIREMENTS No.4, Version 5. Basic Application Scenario (last change: 13.September 2020)
  23. KOMEGA REQUIREMENTS: From the minimal to the basic Version. An Overview (last change: Oct 18, 2020)
  24. KOMEGA REQUIREMENTS: Basic Version with optional on-demand Computations (last change: Nov 15,2020)
  25. KOMEGA REQUIREMENTS:Interactive Simulations (last change: Nov 12,2020)
  26. KOMEGA REQUIREMENTS: Multi-Group Management (last change: December 13, 2020)
  27. KOMEGA-REQUIREMENTS: Start with a Political Program. (last change: November 28, 2020)
  28. OKSIMO SW: Minimal Basic Requirements (last change: January 8, 2021)

 

 

ACI – TWO DIFFERENT READINGS

eJournal: uffmm.org
ISSN 2567-6458, 11.-12.May 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de
Change: May-17, 2019 (Some Corrections, ACI associations)
Change: May-20, 2019 (Reframing ACI with AAI)
CONTEXT

This text is part of the larger text dealing with the Actor-Actor Interaction (AAI)  paradigm.

HCI – HMI – AAI ==> ACI ?

Who has followed the discussion in this blog remembers several different phases in the conceptual frameworks used here.

The first paradigm called Human-Computer Interface (HCI) has been only mentioned by historical reasons.  The next phase Human-Machine Interaction (HMI) was the main paradigm in the beginning of my lecturing in 2005. Later, somewhere 2011/2012, I switched to the paradigm Actor-Actor Interaction (AAI) because I tried to generalize over  the different participating machines, robots, smart interfaces, humans as well as animals. This worked quite nice and some time I thought that this is now the final formula. But reality is often different compared to  our thinking. Many occasions showed up where the generalization beyond the human actor seemed to hide the real processes which are going on, especially I got the impression that very important factors rooted in the special human actor became invisible although they are playing decisive role in many  processes. Another punch against the AAI view came from application scenarios during the last year when I started to deal with whole cities as actors. At the end  I got the feeling that the more specialized expressions like   Actor-Cognition Interaction (ACI) or  Augmented Collective Intelligence (ACI) can indeed help  to stress certain  special properties  better than the more abstract AAI acronym, but using structures like ACI  within general theories and within complex computing environments it became clear that the more abstract acronym AAI is in the end more versatile and simplifies the general structures. ACI became a special sub-case

HISTORY

To understand this oscillation between AAI and  ACI one has to look back into the history of Human Computer/ Machine Interaction, but not only until the end of the World War II, but into the more extended evolutionary history of mankind on this planet.

It is a widespread opinion under the researchers that the development of tools to help mastering material processes was one of the outstanding events which changed the path of  the evolution a lot.  A next step was the development of tools to support human cognition like scripture, numbers, mathematics, books, libraries etc. In this last case of cognitive tools the material of the cognitive  tools was not the primary subject the processes but the cognitive contents, structures, even processes encoded by the material structures of the tools.

Only slowly mankind understood how the cognitive abilities and capabilities are rooted in the body, in the brain, and that the brain represents a rather complex biological machinery which enables a huge amount of cognitive functions, often interacting with each other;  these cognitive functions show in the light of observable behavior clear limits with regard to the amount of features which can be processed in some time interval, with regard to precision, with regard to working interconnections, and more. And therefore it has been understood that the different kinds of cognitive tools are very important to support human thinking and to enforce it in some ways.

Only in the 20th century mankind was able to built a cognitive tool called computer which could show   capabilities which resembled some human cognitive capabilities and which even surpassed human capabilities in some limited areas. Since then these machines have developed a lot (not by themselves but by the thinking and the engineering of humans!) and meanwhile the number and variety of capabilities where the computer seems to resemble a human person or surpasses human capabilities have extend in a way that it has become a common slang to talk about intelligent machines or smart devices.

While the original intention for the development of computers was to improve the cognitive tools with the intend  to support human beings one can  get today  the impression as if the computer has turned into a goal on its own: the intelligent and then — as supposed — the super-intelligent computer appears now as the primary goal and mankind appears as some old relic which has to be surpassed soon.

As will be shown later in this text this vision of the computer surpassing mankind has some assumptions which are

What seems possible and what seems to be a promising roadmap into the future is a continuous step-wise enhancement of the biological structure of mankind which absorbs the modern computing technology by new cognitive interfaces which in turn presuppose new types of physical interfaces.

To give a precise definition of these new upcoming structures and functions is not yet possible, but to identify the actual driving factors as well as the exciting combinations of factors seems possible.

COGNITION EMBEDDED IN MATTER
Actor-Cognition Interaction (ACI): A simple outline of the whole paradigm
Cognition within the Actor-Actor Interaction (AAI)  paradigm: A simple outline of the whole paradigm

The main idea is the shift of the focus away from the physical grounding of the interaction between actors looking instead more to the cognitive contents and processes, which shall be mediated  by the physical conditions. Clearly the analysis of the physical conditions as well as the optimal design of these physical conditions is still a challenge and a task, but without a clear knowledge manifested in a clear model about the intended cognitive contents and processes one has not enough knowledge for the design of the physical layout.

SOLVING A PROBLEM

Thus the starting point of an engineering process is a group of people (the stakeholders (SH)) which identify some problem (P) in their environment and which have some minimal idea of a possible solution (S) for this problem. This can be commented by some non-functional requirements (NFRs) articulating some more general properties which shall hold through the whole solution (e.g. ‘being save’, ‘being barrier-free’, ‘being real-time’ etc.). If the description of the problem with a first intended solution including the NFRs contains at least one task (T) to be solved, minimal intended users (U) (here called executive actors (eA)), minimal intended assistive actors (aA) to assist the user in doing the task, as well as a description of the environment of the task to do, then the minimal ACI-Check can be passed and the ACI analysis process can be started.

COGNITION AND AUGMENTED COLLECTIVE INTELLIGENCE

If we talk about cognition then we think usually about cognitive processes in an individual person.  But in the real world there is no cognition without an ongoing exchange between different individuals by communicative acts. Furthermore it has to be taken into account that the cognition of an individual person is in itself partitioned into two unequal parts: the unconscious part which covers about 99% of all the processes in the body and in the brain and about 1% which covers the conscious part. That an individual person can think somehow something this person has to trigger his own unconsciousness by stimuli to respond with some messages from his before unknown knowledge. Thus even an individual person alone has to organize a communication with his own unconsciousness to be able to have some conscious knowledge about its own unconscious knowledge. And because no individual person has at a certain point of time a clear knowledge of his unconscious knowledge  the person even does not really know what to look for — if there is no event, not perception, no question and the like which triggers the person to interact with its unconscious knowledge (and experience) to get some messages from this unconscious machinery, which — as it seems — is working all the time.

On account of this   logic of the individual internal communication with the individual cognition  an external communication with the world and the manifested cognition of other persons appears as a possible enrichment in the   interactions with the distributed knowledge in the different persons. While in the following approach it is assumed to represent the different knowledge responses in a common symbolic representation viewable (and hearable)  from all participating persons it is growing up a possible picture of something which is generally more rich, having more facets than a picture generated by an individual person alone. Furthermore can such a procedure help all participants to synchronize their different knowledge fragments in a bigger picture and use it further on as their own picture, which in turn can trigger even more aspects out of the distributed unconscious knowledge.

If one organizes this collective triggering of distributed unconscious knowledge within a communication process not only by static symbolic models but beyond this with dynamic rules for changes, which can be interactively simulated or even played with defined states of interest then the effect of expanding the explicit and shared knowledge will be boosted even more.

From this background it makes some sense to turn the wording Actor-Cognition Interaction into the wording Augmented Collective Intelligence where Intelligence is the component of dynamic cognition in a system — here a human person –, Collective means that different individual person are sharing their unconscious knowledge by communicative interactions, and Augmented can be interpreted that one enhances, extends this sharing of knowledge by using new tools of modeling, simulation and gaming, which expands and intensifies the individual learning as well as the commonly shared opinions. For nearly all problems today this appears to be  absolutely necessary.

ACI ANALYSIS PROCESS

Here it will be assumed that there exists a group of ACI experts which can supervise  other actors (stakeholders, domain experts, …) in a process to analyze the problem P with the explicit goal of finding a satisfying solution (S+).

For the whole ACI analysis process an appropriate ACI software should be available to support the ACI experts as well as all the other domain experts.

In this ACI analysis process one can distinguish two main phases: (1) Construct an actor story (AS) which describes all intended states and intended changes within the actor story. (2) Make several tests of the actor story to exploit their explanatory power.

ACTOR STORY (AS)

The actor story describes all possible states (S) of the tasks (T) to be realized to reach intended goal states (S+). A mapping from one state to a follow-up state will be described by a change rule (X). Thus having start state (S0) and appropriate change rules one can construct the follow-up states from the actual state (S*)  with the aid of the change rules. Formally this computation of the follow-up state (S’) will be computed by a simulator function (σ), written as: σ: S* x X  —> S.

SEVERAL TESTS

With the aid of an explicit actor story (AS) one can define the non-functional requirements (NFRs) in a way that it will become decidable whether  a NFR is valid with regard to an actor story or not. In this case this test of being valid can be done as an automated verification process (AVP). Part of this test paradigm is the so-called oracle function (OF) where one can pose a question to the system and the system will answer the question with regard to all theoretically possible states without the necessity to run a (passive) simulation.

If the size of the group is large and it is important that all members of the group have a sufficient similar knowledge about the problem(s) in question (as it is the usual case in a city with different kinds of citizens) then is can be very helpful to enable interactive simulations or even games, which allow a more direct experience of the possible states and changes. Furthermore, because the participants can act according to their individual reflections and goals the process becomes highly uncertain and nearly unpredictable. Especially for these highly unpredictable processes can interactive simulations (and games) help to improve a common understanding of the involved factors and their effects. The difference between a normal interactive simulation and a game is given in the fact that a game has explicit win-states whereas the interactive simulations doesn’t. Explicit win-states can improve learning a lot.

The other interesting question is whether an actor story AS with a certain idea for an assistive actor (aA) is usable for the executive actors. This requires explicit measurements of the usability which in turn requires a clear norm of reference with which the behavior of an executive actor (eA) during a process can be compared. Usually is the actor Story as such the norm of reference with which the observable behavior of the executing actors will be compared. Thus for the measurement one needs real executive actors which represent the intended executive actors and one needs a physical realization of the intended assistive actors called mock-up. A mock-up is not yet  the final implementation of the intended assistive actor but a physical entity which can show all important physical properties of the intended assistive actor in a way which allows a real test run. While in the past it has been assumed to be sufficient to test a test person only once it is here assumed that a test person has to be tested at least three times. This follows from the assumption that every executive (biological) actor is inherently a learning system. This implies that the test person will behave differently in different tests. The degree of changes can be a hint of the easiness and the learnability of the assistive actor.

COLLECTIVE MEMORY

If an appropriate ACI software is available then one can consider an actor story as a simple theory (ST) embracing a model (M) and a collection of rules (R) — ST(x) iff x = <M,R> –which can be used as a kind of a     building block which in turn can be combined with other such building blocks resulting in a complex network of simple theories. If these simple theories are stored in a  public available data base (like a library of theories) then one can built up in time a large knowledge base on their own.

 

 

AAI-THEORY V2 – BLUEPRINT: Bottom-up

eJournal: uffmm.org,
ISSN 2567-6458, 27.February 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: 28.February 2019 (Several corrections)

CONTEXT

An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the special topic how to proceed in a bottom-up approach.

BOTTOM-UP: THE GENERAL BLUEPRINT
Outine of the process how to generate an AS
Figure 1: Outline of the process how to generate an AS with a bottom-up approach

As the introductory figure shows it is assumed here that there is a collection of citizens and experts which offer their individual knowledge, experiences, and skills to ‘put them on the table’ challenged by a given problem P.

This knowledge is in the beginning not structured. The first step in the direction of an actor story (AS) is to analyze the different contributions in a way which shows distinguishable elements with properties and relations. Such a set of first ‘objects’ and ‘relations’ characterizes a set of facts which define a ‘situation’ or a ‘state’ as a collection of ‘facts’. Such a situation/ state can also be understood as a first simple ‘model‘ as response to a given problem. A model is as such ‘static‘; it describes what ‘is’ at a certain point of ‘time’.

In a next step the group has to identify possible ‘changes‘ which can be associated with at least one fact. There can be many possible changes which eventually  need different durations to come into effect. These effects can happen  as ‘exclusive alternatives’ or in ‘parallel’. Apply the possible changes to a  situation  generates   ‘successors’ to the actual situation. A sequence of situations generated by applied changes is  usually called a ‘simulation‘.

If one allows the interaction between real actors with a simulation by associating  a real actor to one of the actors ‘inside the simulation’ one is turning the simulation into an ‘interactive simulation‘ which represents basically a ‘computer game‘ (short: ‘egame‘).

One can use interactive simulations e.g. to (i) learn about the dynamics of a model, to (ii) test the assumptions of a model, to (iii) test the knowledge and skills of the real actors.

Making new experiences with a  simulation allows a continuous improvement of the model and its change rules.

Additionally one can include more citizens and experts into this process and one can use available knowledge from databases and libraries.

EPISTEMOLOGY OF CONCEPTS
Epistemology of concepts used in an AAI Analysis rprocess
Fig.2: Epistemology of concepts used in an AAI Analysis process

As outlined in the preceding section about the blueprint of a bottom-up process there will be a heavy   usage of concepts to describe state of affairs.

The literature about this topic in philosophy as well as many scientific disciplines is overwhelmingly and therefore this small text here can only be a ‘pointer’ into a complex topic. Nevertheless I will use exactly this pointer to explore this topic further.

While the literature is mainly dealing with  more or less specific partial models, I am trying here to point out a very general framework which fits to a more genera philosophical — especially epistemological — view as well as gives respect to many results of scientific disciplines.

The main dimensions here are (i) the outside external empirical world, which connects via sensors to the (ii) internal body, especially the brain,  which works largely ‘unconscious‘, and then (iii) the ‘conscious‘ part of he brain.

The most important relationship between the ‘conscious’ and the ‘unconscious’ part of the brain is the ability of the unconscious brain to transform automatically incoming concrete sens-experiences into more   ‘abstract’ structures, which have at least three sub-dimensions: (i) different concrete material, (ii) a sub-set of extracted common properties, (iii) different sets of occurring contexts associated with the different subsets. This enables the brain to extract only a ‘few’ abstract structures (= abstract concepts)  to deal with ‘many’  concrete events. Thus the abstract concept ‘chair’ can cover many different concrete chairs which have only a few properties in common. Additionally the chairs can occur in different ‘contexts’ associating them with different ‘relations’ which can  specify  possible different ‘usages’   of  the concept ‘chair’.

Thus, if the actor perceives something which ‘matches’ some ‘known’ concept then the actor is  not only conscious about the empirical concrete phenomenon but also simultaneously about the abstract concept which will automatically be activated. ‘Immediately’ the actor ‘knows’ that this empirical something is e.g. a ‘chair’. Concrete: this concrete something is matching an abstract concept ‘chair’ which can as such cover many other concrete things too which can be as concrete somethings partially different from another concrete something.

From this follows an interesting side effect: while an actor can easily decide, whether a concrete something is there  (“it is the case, that” = “it is true”) or not (“it is not the case, that” = “it isnot true” = “it is false”), an actor can not directly decide whether an abstract concept like ‘chair’ as such is ‘true’ in the sense, that the concept ‘as a whole’ corresponds to concrete empirical occurrences. This depends from the fact that an abstract concept like ‘chair’ can match with a  nearly infinite set of possible concrete somethings which are called ‘possible instances’ of the abstract concept. But a human actor can directly   ‘check’ only a ‘few’ concrete somethings. Therefore the usage of abstract concepts like ‘chair’, ‘house’, ‘bottle’ etc. implies  inherently an ‘open set’ of ‘possible’ concrete  exemplars and therefor is the usage of such concepts necessarily a ‘hypothetical’ usage.  Because we can ‘in principle’ check the real extensions of these abstract concepts   in everyday life as long there is the ‘freedom’ to do  such checks,  we are losing the ‘truth’ of our concepts and thereby the basis for a  realistic cooperation, if this ‘freedom of checking’ is not possible.

If some incoming perception is ‘not yet known’,  because nothing given in the unconsciousness does ‘match’,  it is in a basic sens ‘new’ and the brain will automatically generate a ‘new concept’.

THE DIMENSION OF MEANING

In Figure 2 one can find two other components: the ‘meaning relation’ which maps concepts into ‘language expression’.

Language expressions inside the brain correspond to a diversity of visual, auditory, tactile or other empirical event sequences, which are in use for communicative acts.

These language expressions are usually not ‘isolated structures’ but are embedded in relations which map the expression structures to conceptual structures including  the different substantiations of the abstract concepts and the associated contexts. By these relations the expressions are attached to the conceptual structures which are called the ‘meaning‘ of the expressions and vice versa the expressions are called the ‘language articulation’ of the meaning structures.

As far as conceptual structures are related via meaning relations to language expressions then  a perception can automatically cause the ‘activation’ of the associated language expressions, which in turn can be uttered in some way. But conceptual structures   can exist  (especially with children) without an available  meaning relation.

When language expressions are used within a communicative act then  their usage can activate in all participants of the communication the ‘learned’ concepts as their intended meanings. Heaving the meaning activated in someones ‘consciousness’ this is a real phenomenon for that actor. But from the occurrence of  concepts alone does not automatically follow, that a  concept is ‘backed up’ by some ‘real matter’ in the external world. Someone can utter that it is raining, in the hearer of this utterance the intended concepts can become activated, but in the outside external world no rain is happening. In this case one has to state that the utterance of the language expressions “Look, its raining” has no counterpart in the real world, therefore we call the utterance in this case ‘false‘ or  ‘not true‘.

THE DIMENSION OF TIME
The dimension of time based on past experience and combinatoric thinking
Fig.3: The dimension of time based on past experience and combinatoric thinking

The preceding figure 2 of the conceptual space is not yet complete. There is another important dimension based on the ability of the unconscious brain to ‘store’ certain structures in a ‘timely order’ which enables an actor — under certain conditions ! — to decide whether a certain structure X occurred in the consciousness ‘before’ or ‘after’ or ‘at the same time’ as another structure Y.

Evidently the unconscious brain is able do exactly this:  (i) it can arrange the different structures under certain conditions in a ‘timely order’;  (ii)  it can detect ‘differences‘ between timely succeeding structures;  the brain (iii) can conceptualize these changes as ‘change concepts‘ (‘rules of change’), and it can  can classify different kinds of change like ‘deterministic’, ‘non-deterministic’ with different kinds of probabilities, as well as ‘arbitrary’ as in the case of ‘free learning systems‘. Free learning systems are able to behave in a ‘deterministic-like manner’, but they can also change their patterns on account of internal learning and decision processes in nearly any direction.

Based on memories of conceptual structures and derived change concepts (rules of change) the unconscious brain is able to generate different kinds of ‘possible configurations’, whose quality is  depending from the degree of dependencies within the  ‘generating  criteria’: (i) no special restrictions; (ii) empirical restrictions; (iii) empirical restrictions for ‘upcoming states’ (if all drinkable water would be consumed, then one cannot plan any further with drinkable water).