OKSIMO MEETS POPPER. Popper’s Position

eJournal: uffmm.org
ISSN 2567-6458, 31.March – 31.March  2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

This text is part of a philosophy of science  analysis of the case of the oksimo software (oksimo.com). A specification of the oksimo software from an engineering point of view can be found in four consecutive  posts dedicated to the HMI-Analysis for  this software.

POPPERs POSITION IN THE CHAPTERS 1-17

In my reading of the chapters 1-17 of Popper’s The Logic of Scientific Discovery [1] I see the following three main concepts which are interrelated: (i) the concept of a scientific theory, (ii) the point of view of a meta-theory about scientific theories, and (iii) possible empirical interpretations of scientific theories.

Scientific Theory

A scientific theory is according to Popper a collection of universal statements AX, accompanied by a concept of logical inference , which allows the deduction of a certain theorem t  if one makes  some additional concrete assumptions H.

Example: Theory T1 = <AX1,>

AX1= {Birds can fly}

H1= {Peter is  a bird}

: Peter can fly

Because  there exists a concrete object which is classified as a bird and this concrete bird with the name ‘Peter’ can  fly one can infer that the universal statement could be verified by this concrete bird. But the question remains open whether all observable concrete objects classifiable as birds can fly.

One could continue with observations of several hundreds of concrete birds but according to Popper this would not prove the theory T1 completely true. Such a procedure can only support a numerical universality understood as a conjunction of finitely many observations about concrete birds   like ‘Peter can fly’ & ‘Mary can fly’ & …. &’AH2 can fly’.(cf. p.62)

The only procedure which is applicable to a universal theory according to Popper is to falsify a theory by only one observation like ‘Doxy is a bird’ and ‘Doxy cannot fly’. Then one could construct the following inference:

AX1= {Birds can fly}

H2= {Doxy is  a bird, Doxy cannot fly}

: ‘Doxy can fly’ & ~’Doxy can fly’

If a statement A can be inferred and simultaneously the negation ~A then this is called a logical contradiction:

{AX1, H2}  ‘Doxy can fly’ & ~’Doxy can fly’

In this case the set {AX1, H2} is called inconsistent.

If a set of statements is classified as inconsistent then you can derive from this set everything. In this case you cannot any more distinguish between true or false statements.

Thus while the increase of the number of confirmed observations can only increase the trust in the axioms of a scientific theory T without enabling an absolute proof  a falsification of a theory T can destroy the ability  of this  theory to distinguish between true and false statements.

Another idea associated with this structure of a scientific theory is that the universal statements using universal concepts are strictly speaking speculative ideas which deserve some faith that these concepts will be provable every time one will try  it.(cf. p.33, 63)

Meta Theory, Logic of Scientific Discovery, Philosophy of Science

Talking about scientific theories has at least two aspects: scientific theories as objects and those who talk about these objects.

Those who talk about are usually Philosophers of Science which are only a special kind of Philosophers, e.g. a person  like Popper.

Reading the text of Popper one can identify the following elements which seem to be important to describe scientific theories in a more broader framework:

A scientific theory from a point of  view of Philosophy of Science represents a structure like the following one (minimal version):

MT=<S, A[μ], E, L, AX, , ET, E+, E-, true, false, contradiction, inconsistent>

In a shared empirical situation S there are some human actors A as experts producing expressions E of some language L.  Based on their built-in adaptive meaning function μ the human actors A can relate  properties of the situation S with expressions E of L.  Those expressions E which are considered to be observable and classified to be true are called true expressions E+, others are called false expressions  E-. Both sets of expressions are true subsets of E: E+ ⊂ E  and E- ⊂ E. Additionally the experts can define some special  set of expressions called axioms  AX which are universal statements which allow the logical derivation of expressions called theorems of the theory T  ET which are called logically true. If one combines the set of axioms AX with some set of empirically true expressions E+ as {AX, E+} then one can logically derive either  only expressions which are logically true and as well empirically true, or one can derive logically true expressions which are empirically true and empirically false at the same time, see the example from the paragraph before:

{AX1, H2}  ‘Doxy can fly’ & ~’Doxy can fly’

Such a case of a logically derived contradiction A and ~A tells about the set of axioms AX unified with the empirical true expressions  that this unified set  confronted with the known true empirical expressions is becoming inconsistent: the axioms AX unified with true empirical expressions  can not  distinguish between true and false expressions.

Popper gives some general requirements for the axioms of a theory (cf. p.71):

  1. Axioms must be free from contradiction.
  2. The axioms  must be independent , i.e . they must not contain any axiom deducible from the remaining axioms.
  3. The axioms should be sufficient for the deduction of all statements belonging to the theory which is to be axiomatized.

While the requirements (1) and (2) are purely logical and can be proved directly is the requirement (3) different: to know whether the theory covers all statements which are intended by the experts as the subject area is presupposing that all aspects of an empirical environment are already know. In the case of true empirical theories this seems not to be plausible. Rather we have to assume an open process which generates some hypothetical universal expressions which ideally will not be falsified but if so, then the theory has to be adapted to the new insights.

Empirical Interpretation(s)

Popper assumes that the universal statements  of scientific theories   are linguistic representations, and this means  they are systems of signs or symbols. (cf. p.60) Expressions as such have no meaning.  Meaning comes into play only if the human actors are using their built-in meaning function and set up a coordinated meaning function which allows all participating experts to map properties of the empirical situation S into the used expressions as E+ (expressions classified as being actually true),  or E- (expressions classified as being actually false) or AX (expressions having an abstract meaning space which can become true or false depending from the activated meaning function).

Examples:

  1. Two human actors in a situation S agree about the  fact, that there is ‘something’ which  they classify as a ‘bird’. Thus someone could say ‘There is something which is a bird’ or ‘There is  some bird’ or ‘There is a bird’. If there are two somethings which are ‘understood’ as being a bird then they could say ‘There are two birds’ or ‘There is a blue bird’ (If the one has the color ‘blue’) and ‘There is a red bird’ or ‘There are two birds. The one is blue and the other is red’. This shows that human actors can relate their ‘concrete perceptions’ with more abstract  concepts and can map these concepts into expressions. According to Popper in this way ‘bottom-up’ only numerical universal concepts can be constructed. But logically there are only two cases: concrete (one) or abstract (more than one).  To say that there is a ‘something’ or to say there is a ‘bird’ establishes a general concept which is independent from the number of its possible instances.
  2. These concrete somethings each classified as a ‘bird’ can ‘move’ from one position to another by ‘walking’ or by ‘flying’. While ‘walking’ they are changing the position connected to the ‘ground’ while during ‘flying’ they ‘go up in the air’.  If a human actor throws a stone up in the air the stone will come back to the ground. A bird which is going up in the air can stay there and move around in the air for a long while. Thus ‘flying’ is different to ‘throwing something’ up in the air.
  3. The  expression ‘A bird can fly’ understood as an expression which can be connected to the daily experience of bird-objects moving around in the air can be empirically interpreted, but only if there exists such a mapping called meaning function. Without a meaning function the expression ‘A bird can fly’ has no meaning as such.
  4. To use other expressions like ‘X can fly’ or ‘A bird can Y’ or ‘Y(X)’  they have the same fate: without a meaning function they have no meaning, but associated with a meaning function they can be interpreted. For instance saying the the form of the expression ‘Y(X)’ shall be interpreted as ‘Predicate(Object)’ and that a possible ‘instance’ for a predicate could be ‘Can Fly’ and for an object ‘a bird’ then we could get ‘Can Fly(a Bird)’ translated as ‘The object ‘a Bird’ has the property ‘can fly” or shortly ‘A Bird can fly’. This usually would be used as a possible candidate for the daily meaning function which relates this expression to those somethings which can move up in the air.
Axioms and Empirical Interpretations

The basic idea with a system of axioms AX is — according to Popper —  that the axioms as universal expressions represent  a system of equations where  the  general terms   should be able to be substituted by certain values. The set of admissible values is different from the set of  inadmissible values. The relation between those values which can be substituted for the terms  is called satisfaction: the values satisfy the terms with regard to the relations! And Popper introduces the term ‘model‘ for that set of admissible terms which can satisfy the equations.(cf. p.72f)

But Popper has difficulties with an axiomatic system interpreted as a system of equations  since it cannot be refuted by the falsification of its consequences ; for these too must be analytic.(cf. p.73) His main problem with axioms is,  that “the concepts which are to be used in the axiomatic system should be universal names, which cannot be defined by empirical indications, pointing, etc . They can be defined if at all only explicitly, with the help of other universal names; otherwise they can only be left undefined. That some universal names should remain undefined is therefore quite unavoidable; and herein lies the difficulty…” (p.74)

On the other hand Popper knows that “…it is usually possible for the primitive concepts of an axiomatic system such as geometry to be correlated with, or interpreted by, the concepts of another system , e.g . physics …. In such cases it may be possible to define the fundamental concepts of the new system with the help of concepts which were originally used in some of the old systems .”(p.75)

But the translation of the expressions of one system (geometry) in the expressions of another system (physics) does not necessarily solve his problem of the non-empirical character of universal terms. Especially physics is using also universal or abstract terms which as such have no meaning. To verify or falsify physical theories one has to show how the abstract terms of physics can be related to observable matters which can be decided to be true or not.

Thus the argument goes back to the primary problem of Popper that universal names cannot not be directly be interpreted in an empirically decidable way.

As the preceding examples (1) – (4) do show for human actors it is no principal problem to relate any kind of abstract expressions to some concrete real matters. The solution to the problem is given by the fact that expressions E  of some language L never will be used in isolation! The usage of expressions is always connected to human actors using expressions as part of a language L which consists  together with the set of possible expressions E also with the built-in meaning function μ which can map expressions into internal structures IS which are related to perceptions of the surrounding empirical situation S. Although these internal structures are processed internally in highly complex manners and  are — as we know today — no 1-to-1 mappings of the surrounding empirical situation S, they are related to S and therefore every kind of expressions — even those with so-called abstract or universal concepts — can be mapped into something real if the human actors agree about such mappings!

Example:

Lets us have a look to another  example.

If we take the system of axioms AX as the following schema:  AX= {a+b=c}. This schema as such has no clear meaning. But if the experts interpret it as an operation ‘+’ with some arguments as part of a math theory then one can construct a simple (partial) model m  as follows: m={<1,2,3>, <2,3,5>}. The values are again given as  a set of symbols which as such must not ave a meaning but in common usage they will be interpreted as sets of numbers   which can satisfy the general concept of the equation.  In this secondary interpretation m is becoming  a logically true (partial) model for the axiom Ax, whose empirical meaning is still unclear.

It is conceivable that one is using this formalism to describe empirical facts like the description of a group of humans collecting some objects. Different people are bringing  objects; the individual contributions will be  reported on a sheet of paper and at the same time they put their objects in some box. Sometimes someone is looking to the box and he will count the objects of the box. If it has been noted that A brought 1 egg and B brought 2 eggs then there should according to the theory be 3 eggs in the box. But perhaps only 2 could be found. Then there would be a difference between the logically derived forecast of the theory 1+2 = 3  and the empirically measured value 1+2 = 2. If one would  define all examples of measurement a+b=c’ as contradiction in that case where we assume a+b=c as theoretically given and c’ ≠ c, then we would have with  ‘1+2 = 3′ & ~’1+2 = 3’ a logically derived contradiction which leads to the inconsistency of the assumed system. But in reality the usual reaction of the counting person would not be to declare the system inconsistent but rather to suggest that some unknown actor has taken against the agreed rules one egg from the box. To prove his suggestion he had to find this unknown actor and to show that he has taken the egg … perhaps not a simple task … But what will the next authority do: will the authority belief  the suggestion of the counting person or will the authority blame the counter that eventually he himself has taken the missing egg? But would this make sense? Why should the counter write the notes how many eggs have been delivered to make a difference visible? …

Thus to interpret some abstract expression with regard to some observable reality is not a principal problem, but it can eventually be unsolvable by purely practical reasons, leaving questions of empirical soundness open.

SOURCES

[1] Karl Popper, The Logic of Scientific Discovery, First published 1935 in German as Logik der Forschung, then 1959 in English by  Basic Books, New York (more editions have been published  later; I am using the eBook version of Routledge (2002))

 

 

HMI Analysis for the CM:MI paradigm. Part 1

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, February 25, 2021
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de
Last change: March 16, 2021 (Some minor corrections)
HISTORY

As described in the uffmm eJournal  the wider context of this software project is an integrated  engineering theory called Distributed Actor-Actor Interaction [DAAI] further extended to the Collective Man-Machine Intelligence [CM:MI] paradigm.  This document is part of the Case Studies section.

HMI ANALYSIS, Part 1
Introduction

Since January 2021 an intense series of posts has been published how the new ideas manifested in the new software published in this journal  can adequately be reflected in the DAAI theoretical framework. Because these ideas included in the beginning parts of philosophy, philosophy of science, philosophy of engineering, these posts have been first published in the German Blog of the author (cognitiveagent.org). This series of posts started with an online lecture for students of the University of Leipzig together with students of the ‘Hochschule für Technik, Wirtschaft und Kultur (HTWK)’ January 12, 2021.  Here is the complete list of posts:

In what follows in this text is an English version of the following 5 posts. This is not a 1-to-1 translation but rather a new version:

HMI Analysis as Part of Systems Engineering
HMI analysis as pat of systems engineering illustrated with the oksimo software
HMI analysis for the CM:MI paradigm illustrated with the oksimo software concept

As described in the original DAAI theory paper the whole topic of HMI is here understood as a job within the systems engineering paradigm.

The specification process is a kind of a ‘test’ whether the DAAI format of the HMI analysis works with this new  application too.

To remember, the main points of the integrated engineering concept are the following ones:

  1. A philosophical  framework (Philosophy of Science, Philosophy of Engineering, …), which gives the fundamentals for such a process.
  2. The engineering process as such where managers and engineers start the whole process and do it.
  3. After the clarification of the problem to be solved and a minimal vision, where to go, it is the job of the HMI analysis to clarify which requirements have to be fulfilled, to find an optimal solution for the intended product/ service. In modern versions of the HMI analysis substantial parts of the context, i.e. substantial parts of the surrounding society, have to be included in the analysis.
  4. Based on the HMI analysis  in  the logical design phase a mathematical structure has to be identified, which integrates all requirements sufficiently well. This mathematical structure has to be ‘map-able’ into a set of algorithms written in  appropriate programming languages running on  an appropriate platform (the mentioned phases Problem, Vision, HMI analysis, Logical Design are in reality highly iterative).
  5. During the implementation phase the algorithms will be translated into a real working system.
Which Kinds of Experts?

While the original version of the DAAI paper is assuming as ‘experts’ only the typical manager and engineers of an engineering process including all the typical settings, the new extended version under the label CM:MI (Collective Man-Machine Intelligence) has been generalized to any kind of human person as an expert, which allows a maximum of diversity. No one is the ‘absolute expert’.

Collective Intelligence

As ‘intelligence’ is understood here the whole of knowledge, experience, and motivations which can be the moving momentum inside of a human person. As ‘collective’  is meant  the situation, where more than one person is communicating with other persons to share it’s intelligence.

Man-Machine Symbiosis

Today there are discussions going around  about the future of man and (intelligent) machines. Most of these discussions are very weak because they are lacking clear concepts of intelligent machines as well of what is a human person. In the CM:MI paradigm the human person (together with all other biological systems)  is seen at the center of the future  (by  reasons based on modern theories of biological evolution) and the  intelligent machines are seen as supporting devices (although it is assumed here to use ‘strong’ intelligence compared to the actual ‘weak’ machine intelligence today).

CM:MI by Design

Although we know, that groups of many people are ‘in principal’ capable of sharing intelligence to define problems, visions, constructing solutions, testing the solutions etc., we know too, that the practical limits of the brains and the communication are quite narrow. For special tasks a computer can be much, much better. Thus the CM:MI paradigm provides an environment for groups of people to do the shared planning and testing in a new way, only using normal language. Thus the software is designed to enable new kinds of shared knowledge about shared common modes of future worlds. Only with such a truly general framework the vision of a sustainable society as pointed out by the United Nations since 1992 can become real.

Continuation

Look here.

KOMEGA REQUIREMENTS: From the minimal to the basic version

ISSN 2567-6458, 18.October  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 is part of the Case Studies section.

CONTENT

Here we present the ideas how to extend the minimal version to a first basic version. At least two more advanced levels will follow.

VIDEO (EN)

(Last change: Oct 17, 2020)

VIDEO(DE)

(last change: Oct 18, 2020)

CASE STUDIES

eJournal: uffmm.org
ISSN 2567-6458, 4.May  – 16.March   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 applied to many different contexts . Since the original version of the DAAI-Theory in Jan 18, 2020 the concept has been further developed centering around the concept of a Collective Man-Machine Intelligence [CM:MI] to address now any kinds of experts for any kind of simulation-based development, testing and gaming. Additionally the concept  now can be associated with any kind of embedded algorithmic intelligence [EAI]  (different to the mainstream concept ‘artificial intelligence’). The new concept can be used with every normal language; no need for any special programming language! 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. HMI Analysis applied for the CM:MI paradigm.

 

JANUARY 2021 – OCTOBER 2021

  1. HMI Analysis for the CM:MI paradigm. Part 1 (Febr. 25, 2021)(Last change: March 16, 2021)
  2. HMI Analysis for the CM:MI paradigm. Part 2. Problem and Vision (Febr. 27, 2021)
  3. HMI Analysis for the CM:MI paradigm. Part 3. Actor Story and Theories (March 2, 2021)
  4. HMI Analysis for the CM:MI paradigm. Part 4. Tool Based Development with Testing and Gaming (March 3-4, 2021, 16:15h)

APRIL 2020 – JANUARY 2021

  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)

 

 

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 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.