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Homo Sapiens: empirical and sustained-empirical theories, emotions, and machines. A sketch

Author: Gerd Doeben-Henisch

Email: info@uffmm.org

Aug 24, 2023 — Aug 29, 2023 (10:48h CET)

Attention: This text has been translated from a German source by using the software deepL for nearly 97 – 99% of the text! The diagrams of the German version have been left out.

CONTEXT

This text represents the outline of a talk given at the conference “AI – Text and Validity. How do AI text generators change scientific discourse?” (August 25/26, 2023, TU Darmstadt). [1] A publication of all lectures is planned by the publisher Walter de Gruyter by the end of 2023/beginning of 2024. This publication will be announced here then.

Start of the Lecture

Dear Auditorium,

This conference entitled “AI – Text and Validity. How do AI text generators change scientific discourses?” is centrally devoted to scientific discourses and the possible influence of AI text generators on these. However, the hot core ultimately remains the phenomenon of text itself, its validity.

In this conference many different views are presented that are possible on this topic.

TRANSDISCIPLINARY

My contribution to the topic tries to define the role of the so-called AI text generators by embedding the properties of ‘AI text generators’ in a ‘structural conceptual framework’ within a ‘transdisciplinary view’. This helps the specifics of scientific discourses to be highlighted. This can then result further in better ‘criteria for an extended assessment’ of AI text generators in their role for scientific discourses.

An additional aspect is the question of the structure of ‘collective intelligence’ using humans as an example, and how this can possibly unite with an ‘artificial intelligence’ in the context of scientific discourses.

‘Transdisciplinary’ in this context means to span a ‘meta-level’ from which it should be possible to describe today’s ‘diversity of text productions’ in a way that is expressive enough to distinguish ‘AI-based’ text production from ‘human’ text production.

HUMAN TEXT GENERATION

The formulation ‘scientific discourse’ is a special case of the more general concept ‘human text generation’.

This change of perspective is meta-theoretically necessary, since at first sight it is not the ‘text as such’ that decides about ‘validity and non-validity’, but the ‘actors’ who ‘produce and understand texts’. And with the occurrence of ‘different kinds of actors’ – here ‘humans’, there ‘machines’ – one cannot avoid addressing exactly those differences – if there are any – that play a weighty role in the ‘validity of texts’.

TEXT CAPABLE MACHINES

With the distinction in two different kinds of actors – here ‘humans’, there ‘machines’ – a first ‘fundamental asymmetry’ immediately strikes the eye: so-called ‘AI text generators’ are entities that have been ‘invented’ and ‘built’ by humans, it are furthermore humans who ‘use’ them, and the essential material used by so-called AI generators are again ‘texts’ that are considered a ‘human cultural property’.

In the case of so-called ‘AI-text-generators’, we shall first state only this much, that we are dealing with ‘machines’, which have ‘input’ and ‘output’, plus a minimal ‘learning ability’, and whose input and output can process ‘text-like objects’.

BIOLOGICAL — NON-BIOLOGICAL

On the meta-level, then, we are assumed to have, on the one hand, such actors which are minimally ‘text-capable machines’ – completely human products – and, on the other hand, actors we call ‘humans’. Humans, as a ‘homo-sapiens population’, belong to the set of ‘biological systems’, while ‘text-capable machines’ belong to the set of ‘non-biological systems’.

BLANK INTELLIGENCE TERM

The transformation of the term ‘AI text generator’ into the term ‘text capable machine’ undertaken here is intended to additionally illustrate that the widespread use of the term ‘AI’ for ‘artificial intelligence’ is rather misleading. So far, there exists today no general concept of ‘intelligence’ in any scientific discipline that can be applied and accepted beyond individual disciplines. There is no real justification for the almost inflationary use of the term AI today other than that the term has been so drained of meaning that it can be used anytime, anywhere, without saying anything wrong. Something that has no meaning can be neither true’ nor ‘false’.

PREREQUISITES FOR TEXT GENERATION

If now the homo-sapiens population is identified as the original actor for ‘text generation’ and ‘text comprehension’, it shall now first be examined which are ‘those special characteristics’ that enable a homo-sapiens population to generate and comprehend texts and to ‘use them successfully in the everyday life process’.

VALIDITY

A connecting point for the investigation of the special characteristics of a homo-sapiens text generation and a text understanding is the term ‘validity’, which occurs in the conference topic.

In the primary arena of biological life, in everyday processes, in everyday life, the ‘validity’ of a text has to do with ‘being correct’, being ‘appicable’. If a text is not planned from the beginning with a ‘fictional character’, but with a ‘reference to everyday events’, which everyone can ‘check’ in the context of his ‘perception of the world’, then ‘validity in everyday life’ has to do with the fact that the ‘correctness of a text’ can be checked. If the ‘statement of a text’ is ‘applicable’ in everyday life, if it is ‘correct’, then one also says that this statement is ‘valid’, one grants it ‘validity’, one also calls it ‘true’. Against this background, one might be inclined to continue and say: ‘If’ the statement of a text ‘does not apply’, then it has ‘no validity’; simplified to the formulation that the statement is ‘not true’ or simply ‘false’.

In ‘real everyday life’, however, the world is rarely ‘black’ and ‘white’: it is not uncommon that we are confronted with texts to which we are inclined to ascribe ‘a possible validity’ because of their ‘learned meaning’, although it may not be at all clear whether there is – or will be – a situation in everyday life in which the statement of the text actually applies. In such a case, the validity would then be ‘indeterminate’; the statement would be ‘neither true nor false’.

ASYMMETRY: APPLICABLE- NOT APPLICABLE

One can recognize a certain asymmetry here: The ‘applicability’ of a statement, its actual validity, is comparatively clear. The ‘not being applicable’, i.e. a ‘merely possible’ validity, on the other hand, is difficult to decide.

With this phenomenon of the ‘current non-decidability’ of a statement we touch both the problem of the ‘meaning’ of a statement — how far is at all clear what is meant? — as well as the problem of the ‘unfinishedness of our everyday life’, better known as ‘future’: whether a ‘current present’ continues as such, whether exactly like this, or whether completely different, depends on how we understand and estimate ‘future’ in general; what some take for granted as a possible future, can be simply ‘nonsense’ for others.

MEANING

This tension between ‘currently decidable’ and ‘currently not yet decidable’ additionally clarifies an ‘autonomous’ aspect of the phenomenon of meaning: if a certain knowledge has been formed in the brain and has been made usable as ‘meaning’ for a ‘language system’, then this ‘associated’ meaning gains its own ‘reality’ for the scope of knowledge: it is not the ‘reality beyond the brain’, but the ‘reality of one’s own thinking’, whereby this reality of thinking ‘seen from outside’ has something like ‘being virtual’.

If one wants to talk about this ‘special reality of meaning’ in the context of the ‘whole system’, then one has to resort to far-reaching assumptions in order to be able to install a ‘conceptual framework’ on the meta-level which is able to sufficiently describe the structure and function of meaning. For this, the following components are minimally assumed (‘knowledge’, ‘language’ as well as ‘meaning relation’):

KNOWLEDGE: There is the totality of ‘knowledge’ that ‘builds up’ in the homo-sapiens actor in the course of time in the brain: both due to continuous interactions of the ‘brain’ with the ‘environment of the body’, as well as due to interactions ‘with the body itself’, as well as due to interactions ‘of the brain with itself’.

LANGUAGE: To be distinguished from knowledge is the dynamic system of ‘potential means of expression’, here simplistically called ‘language’, which can unfold over time in interaction with ‘knowledge’.

MEANING RELATIONSHIP: Finally, there is the dynamic ‘meaning relation’, an interaction mechanism that can link any knowledge elements to any language means of expression at any time.

Each of these mentioned components ‘knowledge’, ‘language’ as well as ‘meaning relation’ is extremely complex; no less complex is their interaction.

FUTURE AND EMOTIONS

In addition to the phenomenon of meaning, it also became apparent in the phenomenon of being applicable that the decision of being applicable also depends on an ‘available everyday situation’ in which a current correspondence can be ‘concretely shown’ or not.

If, in addition to a ‘conceivable meaning’ in the mind, we do not currently have any everyday situation that sufficiently corresponds to this meaning in the mind, then there are always two possibilities: We can give the ‘status of a possible future’ to this imagined construct despite the lack of reality reference, or not.

If we would decide to assign the status of a possible future to a ‘meaning in the head’, then there arise usually two requirements: (i) Can it be made sufficiently plausible in the light of the available knowledge that the ‘imagined possible situation’ can be ‘transformed into a new real situation’ in the ‘foreseeable future’ starting from the current real situation? And (ii) Are there ‘sustainable reasons’ why one should ‘want and affirm’ this possible future?

The first requirement calls for a powerful ‘science’ that sheds light on whether it can work at all. The second demand goes beyond this and brings the seemingly ‘irrational’ aspect of ’emotionality’ into play under the garb of ‘sustainability’: it is not simply about ‘knowledge as such’, it is also not only about a ‘so-called sustainable knowledge’ that is supposed to contribute to supporting the survival of life on planet Earth — and beyond –, it is rather also about ‘finding something good, affirming something, and then also wanting to decide it’. These last aspects are so far rather located beyond ‘rationality’; they are assigned to the diffuse area of ’emotions’; which is strange, since any form of ‘usual rationality’ is exactly based on these ’emotions’.[2]

SCIENTIFIC DISCOURSE AND EVERYDAY SITUATIONS

In the context of ‘rationality’ and ’emotionality’ just indicated, it is not uninteresting that in the conference topic ‘scientific discourse’ is thematized as a point of reference to clarify the status of text-capable machines.

The question is to what extent a ‘scientific discourse’ can serve as a reference point for a successful text at all?

For this purpose it can help to be aware of the fact that life on this planet earth takes place at every moment in an inconceivably large amount of ‘everyday situations’, which all take place simultaneously. Each ‘everyday situation’ represents a ‘present’ for the actors. And in the heads of the actors there is an individually different knowledge about how a present ‘can change’ or will change in a possible future.

This ‘knowledge in the heads’ of the actors involved can generally be ‘transformed into texts’ which in different ways ‘linguistically represent’ some of the aspects of everyday life.

The crucial point is that it is not enough for everyone to produce a text ‘for himself’ alone, quite ‘individually’, but that everyone must produce a ‘common text’ together ‘with everyone else’ who is also affected by the everyday situation. A ‘collective’ performance is required.

Nor is it a question of ‘any’ text, but one that is such that it allows for the ‘generation of possible continuations in the future’, that is, what is traditionally expected of a ‘scientific text’.

From the extensive discussion — since the times of Aristotle — of what ‘scientific’ should mean, what a ‘theory’ is, what an ’empirical theory’ should be, I sketch what I call here the ‘minimal concept of an empirical theory’.

  1. The starting point is a ‘group of people’ (the ‘authors’) who want to create a ‘common text’.
  2. This text is supposed to have the property that it allows ‘justifiable predictions’ for possible ‘future situations’, to which then ‘sometime’ in the future a ‘validity can be assigned’.
  3. The authors are able to agree on a ‘starting situation’ which they transform by means of a ‘common language’ into a ‘source text’ [A].
  4. It is agreed that this initial text may contain only ‘such linguistic expressions’ which can be shown to be ‘true’ ‘in the initial situation’.
  5. In another text, the authors compile a set of ‘rules of change’ [V] that put into words ‘forms of change’ for a given situation.
  6. Also in this case it is considered as agreed that only ‘such rules of change’ may be written down, of which all authors know that they have proved to be ‘true’ in ‘preceding everyday situations’.
  7. The text with the rules of change V is on a ‘meta-level’ compared to the text A about the initial situation, which is on an ‘object-level’ relative to the text V.
  8. The ‘interaction’ between the text V with the change rules and the text A with the initial situation is described in a separate ‘application text’ [F]: Here it is described when and how one may apply a change rule (in V) to a source text A and how this changes the ‘source text A’ to a ‘subsequent text A*’.
  9. The application text F is thus on a next higher meta-level to the two texts A and V and can cause the application text to change the source text A.
  1. The moment a new subsequent text A* exists, the subsequent text A* becomes the new initial text A.
  2. If the new initial text A is such that a change rule from V can be applied again, then the generation of a new subsequent text A* is repeated.
  3. This ‘repeatability’ of the application can lead to the generation of many subsequent texts <A*1, …, A*n>.
  4. A series of many subsequent texts <A*1, …, A*n> is usually called a ‘simulation’.
  5. Depending on the nature of the source text A and the nature of the change rules in V, it may be that possible simulations ‘can go quite differently’. The set of possible scientific simulations thus represents ‘future’ not as a single, definite course, but as an ‘arbitrarily large set of possible courses’.
  6. The factors on which different courses depend are manifold. One factor are the authors themselves. Every author is, after all, with his corporeality completely himself part of that very empirical world which is to be described in a scientific theory. And, as is well known, any human actor can change his mind at any moment. He can literally in the next moment do exactly the opposite of what he thought before. And thus the world is already no longer the same as previously assumed in the scientific description.

Even this simple example shows that the emotionality of ‘finding good, wanting, and deciding’ lies ahead of the rationality of scientific theories. This continues in the so-called ‘sustainability discussion’.

SUSTAINABLE EMPIRICAL THEORY

With the ‘minimal concept of an empirical theory (ET)’ just introduced, a ‘minimal concept of a sustainable empirical theory (NET)’ can also be introduced directly.

While an empirical theory can span an arbitrarily large space of grounded simulations that make visible the space of many possible futures, everyday actors are left with the question of what they want to have as ‘their future’ out of all this? In the present we experience the situation that mankind gives the impression that it agrees to destroy the life beyond the human population more and more sustainably with the expected effect of ‘self-destruction’.

However, this self-destruction effect, which can be predicted in outline, is only one variant in the space of possible futures. Empirical science can indicate it in outline. To distinguish this variant before others, to accept it as ‘good’, to ‘want’ it, to ‘decide’ for this variant, lies in that so far hardly explored area of emotionality as root of all rationality.[2]

If everyday actors have decided in favor of a certain rationally lightened variant of possible future, then they can evaluate at any time with a suitable ‘evaluation procedure (EVAL)’ how much ‘percent (%) of the properties of the target state Z’ have been achieved so far, provided that the favored target state is transformed into a suitable text Z.

In other words, the moment we have transformed everyday scenarios into a rationally tangible state via suitable texts, things take on a certain clarity and thereby become — in a sense — simple. That we make such transformations and on which aspects of a real or possible state we then focus is, however, antecedent to text-based rationality as an emotional dimension.[2]

MAN-MACHINE

After these preliminary considerations, the final question is whether and how the main question of this conference, “How do AI text generators change scientific discourse?” can be answered in any way?

My previous remarks have attempted to show what it means for humans to collectively generate texts that meet the criteria for scientific discourse that also meets the requirements for empirical or even sustained empirical theories.

In doing so, it becomes apparent that both in the generation of a collective scientific text and in its application in everyday life, a close interrelation with both the shared experiential world and the dynamic knowledge and meaning components in each actor play a role.

The aspect of ‘validity’ is part of a dynamic world reference whose assessment as ‘true’ is constantly in flux; while one actor may tend to say “Yes, can be true”, another actor may just tend to the opposite. While some may tend to favor possible future option X, others may prefer future option Y. Rational arguments are absent; emotions speak. While one group has just decided to ‘believe’ and ‘implement’ plan Z, the others turn away, reject plan Z, and do something completely different.

This unsteady, uncertain character of future-interpretation and future-action accompanies the Homo Sapiens population from the very beginning. The not understood emotional complex constantly accompanies everyday life like a shadow.

Where and how can ‘text-enabled machines’ make a constructive contribution in this situation?

Assuming that there is a source text A, a change text V and an instruction F, today’s algorithms could calculate all possible simulations faster than humans could.

Assuming that there is also a target text Z, today’s algorithms could also compute an evaluation of the relationship between a current situation as A and the target text Z.

In other words: if an empirical or a sustainable-empirical theory would be formulated with its necessary texts, then a present algorithm could automatically compute all possible simulations and the degree of target fulfillment faster than any human alone.

But what about the (i) elaboration of a theory or (ii) the pre-rational decision for a certain empirical or even sustainable-empirical theory ?

A clear answer to both questions seems hardly possible to me at the present time, since we humans still understand too little how we ourselves collectively form, select, check, compare and also reject theories in everyday life.

My working hypothesis on the subject is: that we will very well need machines capable of learning in order to be able to fulfill the task of developing useful sustainable empirical theories for our common everyday life in the future. But when this will happen in reality and to what extent seems largely unclear to me at this point in time.[2]

COMMENTS

[1] https://zevedi.de/en/topics/ki-text-2/

[2] Talking about ’emotions’ in the sense of ‘factors in us’ that move us to go from the state ‘before the text’ to the state ‘written text’, that hints at very many aspects. In a small exploratory text “State Change from Non-Writing to Writing. Working with chatGPT4 in parallel” ( https://www.uffmm.org/2023/08/28/state-change-from-non-writing-to-writing-working-with-chatgpt4-in-parallel/ ) the author has tried to address some of these aspects. While writing it becomes clear that very many ‘individually subjective’ aspects play a role here, which of course do not appear ‘isolated’, but always flash up a reference to concrete contexts, which are linked to the topic. Nevertheless, it is not the ‘objective context’ that forms the core statement, but the ‘individually subjective’ component that appears in the process of ‘putting into words’. This individual subjective component is tentatively used here as a criterion for ‘authentic texts’ in comparison to ‘automated texts’ like those that can be generated by all kinds of bots. In order to make this difference more tangible, the author decided to create an ‘automated text’ with the same topic at the same time as the quoted authentic text. For this purpose he used chatGBT4 from openAI. This is the beginning of a philosophical-literary experiment, perhaps to make the possible difference more visible in this way. For purely theoretical reasons, it is clear that a text generated by chatGBT4 can never generate ‘authentic texts’ in origin, unless it uses as a template an authentic text that it can modify. But then this is a clear ‘fake document’. To prevent such an abuse, the author writes the authentic text first and then asks chatGBT4 to write something about the given topic without chatGBT4 knowing the authentic text, because it has not yet found its way into the database of chatGBT4 via the Internet.

body-external world, change, change - repeated, change - schema, change rules, difference description, forecasting, Knowledge, meaning, meaning space, measurement, measurement procedure, naive thinking, observational approach, perception, phenomenal space, prediction, rule-compression, sensor signal, time slice

FORECASTING – PREDICTION: What?

eJournal: uffmm.org
ISSN 2567-6458, 19.August 2022 – 25 August 2022, 14:26h
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

This text is part of the subject COMMON SCIENCE as Sustainable Applied Empirical Theory, besides ENGINEERING, in a SOCIETY. It is a preliminary version, which is intended to become part of a book.

FORECASTING – PREDICTION: What?

optimal prediction

In the introduction of the main text it has been underlined that within a sustainable empirical theory it is not only necessary to widen the scope with a maximum of diversity, but at the same time it is also necessary to enable the capability for an optimal prediction about the ‘possible states of a possible future’.

the meaning machinery

In the text after this introduction it has been outlined that between human actors the most powerful tool for the clarification of the given situation — the NOW — is the everyday language with a ‘built in’ potential in every human actor for infinite meanings. This individual internal meaning space as part of the individual cognitive structure is equipped with an ‘abstract – concrete’ meaning structure with the ability to distinguish between ‘true’ and ‘not true’, and furthermore equipped with the ability to ‘play around’ with meanings in a ‘new way’.

COORDINATION

Thus every human actor can generate within his cognitive dimension some states or situations accompanied with potential new processes leading to new states. To share this ‘internal meanings’ with other brains to ‘compare’ properties of the ‘own’ thinking with properties of the thinking of ‘others’ the only chance is to communicate with other human actors mediated by the shared everyday language. If this communication is successful it arises the possibility to ‘coordinate’ the own thinking about states and possible actions with others. A ‘joint undertaking’ is becoming possible.

shared thinking

To simplify the process of communication it is possible, that a human actor does not ‘wait’ until some point in the future to communicate the content of the thinking, but even ‘while the thinking process is going on’ a human actor can ‘translate his thinking’ in language expressions which ‘fit the processed meanings’ as good as possible. Doing this another human actor can observe the language activity, can try to ‘understand’, and can try to ‘respond’ to the observations with his language expressions. Such an ‘interplay’ of expressions in the context of multiple thinking processes can show directly either a ‘congruence’ or a ‘difference’. This can help each participant in the communication to clarify the own thinking. At the same time an exchange of language expressions associated with possible meanings inside the different brains can ‘stimulate’ different kinds of memory and thinking processes and through this the space of shared meanings can be ‘enlarged’.

phenomenal space 1 and 2

Human actors with their ability to construct meaning spaces and the ability to share parts of the meaning space by language communication are embedded with their bodies in a ‘body-external environment’ usual called ‘external world’ or ‘nature’ associated with the property to be ‘real’.

Equipped with a body with multiple different kinds of ‘sensors’ some of the environmental properties can stimulate these sensors which in turn send neuronal signals to the embedded brain. The first stage of the ‘processing of sensor signals’ is usually called ‘perception’. Perception is not a passive 1-to-1 mapping of signals into the brain but it is already a highly sophisticated processing where the ‘raw signals’ of the sensors — which already are doing some processing on their own — are ‘transformed’ into more complex signals which the human actor in its perception does perceive as ‘features’, ‘properties’, ‘figures’, ‘patterns’ etc. which usually are called ‘phenomena’. They all together are called ‘phenomenal space’. In a ‘naive thinking’ this phenomenal space is taken ‘as the external world directly’. During life a human actor can learn — this must not happen! –, that the ‘phenomenal space’ is a ‘derived space’ triggered by an ‘assumed outside world’ which ’causes’ by its properties the sensors to react in a certain way. But the ‘actual nature’ of the outside world is not really known. Let us call the unknown outside world of properties ‘phenomenal space 1’ and the derived phenomenal space inside the body-brain ‘phenomenal space 2’.

TIMELY ORDERING

Due to the availability of the phenomenal space 2 the different human actors can try to ‘explore’ the ‘unknown assumed real world’ based on the available phenomena.

If one takes a wider look to the working of the brain of a human actor one can detect that the processing of the brain of the phenomenal space is using additional mechanisms:

  1. The phenomenal space is organized in ‘time slices’ of a certain fixed duration. The ‘content’ of a time slice during the time window (t,t’) will be ‘overwritten’ during the next time slice (t’,t”) by those phenomena, which are then ‘actual’, which are then constituting the NOW. The phenomena from the time window before (t’,t”) can become ‘stored’ in some other parts of the brain usually called ‘memory’.
  2. The ‘storing’ of phenomena in parts of the brain called ‘memory’ happens in a highly sophisticated way enabling ‘abstract structures’ with an ‘interface’ for ‘concrete properties’ typical for the phenomenal space, and which can become associated with other ‘content’ of the memory.
  3. It is an astonishing ability of the memory to enable an ‘ordering’ of memory contents related to situations as having occurred ‘before’ or ‘after’ some other property. Therefore the ‘content of the memory’ can represent collections of ‘stored NOWs’, which can be ‘ordered’ in a ‘sequence of NOWs’, and thereby the ‘dimension of time’ appears as a ‘framing property’ of ‘remembered phenomena’.
  4. Based on this capability to organize remembered phenomena in ‘sequences of states’ representing a so-called ‘timely order’ the brain can ‘operate’ on such sequences in various ways. It can e.g. ‘compare’ two states in such a sequence whether these are ‘the same’ or whether they are ‘different’. A difference points to a ‘change’ in the phenomenal space. Longer sequences — even including changes — can perhaps show up as ‘repetitions’ compared to ‘earlier’ sequences. Such ‘repeating sequences’ can perhaps represent a ‘pattern’ pointing to some ‘hidden factors’ responsible for the pattern.

formal representations [1]

Basic outline of human actor as part of an external world with an internal phenomenal space 2, including a memory and the capability to elaborate cognitive meta-levels using the dimension of time. There is a limited exchange medium between different brains realized by language communication. Formal models are an instrument to represent recognized timely sequences of sets of properties with typical changes.

Based on a rather sophisticated internal processing structure every human actor has the capability to compose language descriptions which can ‘represent’ with the aid of sets of language expressions different kinds of local situations. Every expression can represent some ‘meaning’ which is encoded in every human actor in an individual manner. Such a language encoding can partially becoming ‘standardized’ by shared language learning in typical everyday living situations. To that extend as language encodings (the assumed meaning) is shared between different human actors they can use this common meaning space to communicate their experience.

Based on the built-in property of abstract knowledge to have an interface to ‘more concrete’ meanings, which finally can be related to ‘concrete perceptual phenomena’ available in the sensual perceptions, every human actor can ‘check’ whether an actual meaning seems to have an ‘actual correspondence’ to some properties in the ‘real environment’. If this phenomenal setting in the phenomenal space 2 with a correspondence to the sensual perceptions is encoded in a language expression E then usually it is told that the ‘meaning of E’ is true; otherwise not.

Because the perceptual interface to an assumed real world is common to all human actors they can ‘synchronize’ their perceptions by sharing the related encoded language expressions.

If a group of human actors sharing a real situation agrees about a ‘set of language expressions’ in the sens that each expression is assumed to be ‘true’, then one can assume, that every expression ‘represents’ some encoded meanings which are related to the shared empirical situation, and therefore the expressions represent ‘properties of the assumed real world’. Such kinds of ‘meaning constructions’ can be further ‘supported’ by the usage of ‘standardized procedures’ called ‘measurement procedures’.

Under this assumption one can infer, that a ‘change in the realm of real world properties’ has to be encoded in a ‘new language expression’ associated with the ‘new real world properties’ and has to be included in the set of expressions describing an actual situation. At the same time it can happen, that an expression of the actual set of expressions is becoming ‘outdated’ because the properties, this expression has encoded, are gone. Thus, the overall ‘dynamics of a set of expressions representing an actual set of real world properties’ can be realized as follows:

  1. Agree on a first set of expression to be a ‘true’ description of a given set of real world properties.
  2. After an agreed period of time one has to check whether (i) the encoded meaning of an expression is gone or (ii) whether a new real world property has appeared which seems to be ‘important’ but is not yet encoded in a language expression of the set. Depending from this check either (i) one has to delete those expressions which are no longer ‘true’ or (ii) one has to introduce new expressions for the new real world properties.

In a strictly ‘observational approach’ the human actors are only observing the course of events after some — regular or spontaneous –time span, making their observations (‘measurements’) and compare these observations with their last ‘true description’ of the actual situation. Following this pattern of behavior they can deduce from the series of true descriptions <D1, D2, …, Dn> for every pair of descriptions (Di,Di+1) a ‘difference description’ as a ‘rule’ in the following way: (i) IF x is a subset of expressions in Di+1, which are not yet members of the set of expressions in Di, THEN ‘add’ these expressions to the set of expressions in Di. (ii) IF y is a subset of expressions in Di, which are no more members of the set of expressions in Di+1, THEN ‘delete’ these expressions from the set of expressions in Di. (iii) Construct a ‘condition-set’ of expressions as subset of Di, which has to be fulfilled to apply (i) and (ii).

Doing this for every pair of descriptions then one is getting a set of ‘change rules’ X which can be used, to ‘generate’ — starting with the first description D0 — all the follow-up descriptions only by ‘applying a change rule Xi‘ to the last generated description.

This first purely observational approach works, but every change rule Xi in this set of change rules X can be very ‘singular’ pointing to a true singularity in the mathematical sense, because there is not ‘common rule’ to predict this singularity.

It would be desirable to ‘dig into possible hidden factors’ which are responsible for the observed changes but they would allow to ‘reduce the number’ of individual change rules of X. But for such a ‘rule-compression’ there exists from the outset no usable knowledge. Such a reduction will only be possible if a certain amount of research work will be done hopefully to discover the hidden factors.

All the change rules which could be found through such observational processes can in the future be re-used to explore possible outcomes for selected situations.

COMMENTS

[1] For the final format of this section I have got important suggestions from René Thom by reading the introduction of his book “Structural Stability and Morphogenesis: An Outline of a General Theory of Models” (1972, 1989). See my review post HERE : https://www.uffmm.org/2022/08/22/rene-thom-structural-stability-and-morphogenesis-an-outline-of-a-general-theory-of-models-original-french-edition-1972-updated-by-the-author-and-translated-into-english-by-d-h-fowler-1989/

change rules, everyday expert, everyday language, expert, forecast, given situation, inference, inference concept, inference engine, inference rule, Knowledge, mathematical language, meaning, meaning function, mental model, natural expert, oksimo, oksimo application, Simulation, simulator, symbolic representation, vision, vision statement

OKSIMO APPLICATIONS – Simple Examples – Citizens of a County

eJournal: uffmm.org ISSN 2567-6458

27.March 2022 – 27.March 2022
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

BLOG-CONTEXT

This post is part of the Oksimo Application theme which is part of the uffmm blog.

PREFACE

This post shows a simple simulation example with the beta-version of the new Version 2 of the oksimo programming environment. This example shall illustrate the concept of an ‘Everyday Empirical Theory‘ as described in this blog 11 days before. It is intentionally as ‘simple as possible’. Probably some more examples will be shown.

FROM THEORY TO AN APPLICATION

To apply a theory concept in an everyday world there are many formats possible. In this text it will be shown how such an application would look like if one is applying the oksimo programming environment. Until now there exists only a German Blog (oksimo.org) describing the oksimo paradigm a little bit. But the examples there are written with oksimo version 1, which didn’t allow to use math. In version 2 this is possible, accompanied by some visual graph features.

Everyday Experts – Basic Ideas

This figure shows a simple outline of the basic assumptions of the oksimo programming environment constituting the oksimo paradigm: (i) Every human person is assumed to be a ‘natural expert’ being member of a bigger population which shares the same ‘everyday language’ including basic math. (ii) An actor is embedded in some empirical environment including the own body and other human actors. (iii) Human actors are capable of elaborating as inner states different kinds of ‘mental (cognitive) models’ based on their experience of the environment and their own body. (iv) Human actors are further capable to use symbolic languages to ‘represent’ properties of these mental models encoded in symbolic expressions. Such symbolic encoding presupposes an ‘inner meaning function’ which has to be learned. (v) In the oksimo programming environment one needs for the description of a ‘given state’ two kinds of symbolic expressions: (v.1) Language expressions to describe general properties and relations which are assumed to be ‘given’ (= ‘valid by experience’); (v.2) Language expressions to name concrete quantitative properties (simple math expressions).

This figure shows the idea how to change a given state (situation) by so-called ‘change rules’. A change rule encodes experience from the everyday world under which conditions some properties of a given situation S can be ‘changed’ in a way, that a ‘new situation’ S* comes into being. Generally a given state can change if either language expression is ‘deleted’ from the description or ‘contributed’. Another possibility is realized if one of the given quantitative expressions changes its value. In the above simple situation the only change happens by changing the number of citizens by some growth effect. But, as other examples will demonstrate, everything is possible what is possible in the empirical world.

SOME MORE FEATURES

The basic schema of the oksimo paradigm assumes the following components:

  1. The description of a ‘given situation’ as a ‘start state’.
  2. The description of a ‘vision’ functioning as a ‘goal’ which allows a basic ‘Benchmarking’.
  3. A list of ‘change rules’ which describe the assumed possible changes
  4. An ‘inference engine’ called ‘simulator’: Depending from the number of wanted ‘simulation cycles’ (‘inferences’) the simulator applies the change rules onto a given state S and thereby it is producing a ‘follow up state’ S*, which becomes the new given state. The series of generated states represents the ‘history’ of a simulation. Every follow up state is an ‘inference’ and by definition also a ‘forecast’.

All these features (1) – (4) together constitute a full empirical theory in the sense of the mentioned theory post before.

Let us look to a real simulation.

A REAL SIMULATION

The following example has been run with Oksimo v2.0 (Pre-Release) (353e5). Hopefully we can finish the pre-release to a full release the next few weeks.

A VISION

Name: v2026

Expressions:

The Main-Kinzig County exists.

Math expressions:

YEAR>2025 and YEAR<2027

This simple goal assumes the existence of the Main-Kinzig County for the year 2026.

GIVEN START STATE

Name: StartSimple1

Expressions:

The Main-Kinzig County exists.

The number of citizens is known.

Comparing the number of different years one has computed a growth rate.

Math expressions:

YEAR=2018Number

CITIZENS=418950Amount

GROWTH=0.0023Percentage

The start state makes some simple statements which are assumed to be ‘valid’ in a ‘real given situation’ by the participating natural experts.

CHANGE RULES

In this example there is only one change rules (In principle there can be as many change rules as wanted).

Rule name: Growth1

Probability: 1.0

Conditions:

The Main-Kinzig County exists.

Math conditions:

CITIZENS < 450000

Effects plus:

Effects minus:

Effects math:

CITIZENS=CITIZENS+(CITIZENS*GROWTH)

YEAR=YEAR+1

This change rules is rather simple. It looks only to the fact whether the Main-Kinzig County exists and wether the number of citizens is still below 450000. If this is the case, then the year will be incremented and the number of citizens will be incremented according to an extremely simple formula.

For every named quantity in this simulation (YEAR, GROWTH, CITIZENS) the values are collected for every simulation cycle and therefore can be used for evaluations. In this simple case only the quantities of YEAR and CITIZENS have changes:

Simple linear graph for the quantity named YEAR
Simple linear graph for the quantity named CITIZENS

Here the quick log of simulation cycle round 7 – 9:

Round 7

State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2025Number
CITIZENS: 425741.8149741673Amount
GROWTH: 0.0023Percentage

50.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
None

Round 8

State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2026Number
CITIZENS: 426721.0211486079Amount
GROWTH: 0.0023Percentage

100.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
YEAR>2025 and YEAR<2027,

Round 9

State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2027Number
CITIZENS: 427702.4794972497Amount
GROWTH: 0.0023Percentage

50.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
None

In round 8 one can see, that the simulation announces:

“100.00 percent of your vision was achieved by reaching the following states: The Main-Kinzig County exists., And the following math visions: YEAR>2025 and YEAR<2027“

From this the natural expert can conclude that his requirements given in the vision are ‘fulfilled’/’satisfied’.

WHAT COMES NEXT?

In a loosely order more examples will follow. Here you find the next one.

change rules, derivation, Engineering, expression [E], false, formal logic, future state, oksimo theory, Probability, probability factor, Simulation, simulator, true, validated oksimo theory

THE OKSIMO CASE as SUBJECT FOR PHILOSOPHY OF SCIENCE. Part 5. Oksimo as Theory?

eJournal: uffmm.org
ISSN 2567-6458, 24.March – 24.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.

DERIVATION

In formal logic exists the concept of logical derivation ‘⊢’ written as

E ⊢X e

saying that one can get the expression e out of the set of expressions E by applying the rules X.

In the oksimo case we have sets of expressions ES to represent either a given starting state S or to represent as EV a given vision V. Furthermore  we have change rules X operating on sets of expressions and we can derive sequences of states of expressions <E1, E2, …, En> by applying the change rules X with the aid of a simulator Σ onto these expressions written as

ES ⊢Σ,X <E1, E2, …, En>

Thus given an initial set of expressions ES one can derive a whole sequence of expression sets Ei by applying the change rules.

While all individual expressions of the start set ES are by assumption classified as true it holds for the derived sets of expressions Ei  that these expressions are correct with regard to the used change rules X but whether these sets of expressions are also true with regard to a given  situation Si considered as a possible future state Sfuti has to be proved separately! The reason for this unclear status results from the fact that the change rules X represent changes which the authoring experts consider as possible changes which they want to apply but they cannot guarantee the empirical validity for all upcoming times   only by thinking. This implicit uncertainty can be handled a little bit with the probability factor π of an individual change rule. The different degrees of certainty in the application of a change rule can give an approximation of this uncertainty. Thus as longer the chain of derivations is becoming as lower the assumed probability will develop.

SIMPLE OKSIMO THEORY [TOKSIMO]

Thus if we have some human actors Ahum, an environment ENV, some starting situation S as part of the environment ENV, a first set of expressions ES representing only true expressions with regard to the starting situation S, a set of elaborated change rules X, and a simulator Σ then one can  define a simple  oksimo-like theory Toksimo as follows:

TOKSIMO(x) iff x = <ENV, S, Ahum, ES, X, Σ, ⊢Σ,X, speakL(), makedecidable()>

The human actors can describe a given situation S as part of an environment ENV as a set of expressions ES which can be proved with makedecidable() as true. By defining a set of change rules X and a simulator Σ one can define  a formal derivation relation ⊢Σ,X which allows the derivation of a sequence of sets of expressions <E1, E2, …, En> written as

ES ⊢T,Σ,X <E1, E2, …, En>

While the truth of the first set of expressions ES has been proved in the beginning, the truth of the derived sets of expressions has to be shown explicitly for each set Ei separately. Given is only the formal correctness of the derived expressions according to the change rules X and the working of the simulator.

VALIDADED SIMPLE OKSIMO THEORY [TOKSIMO.V]

One can extend the simple oksimo theory TOKSIMO to a biased  oksimo theory TOKSIMO.V if one includes in the theory a set of vision expressions EV. Vision expressions can describe a possible situation in the future Sfut which is declared as a goal to be reached. With a given vision document EV the simulator can check for every new derived set of expressions Ei to which degree the individual expressions e of the set of vision expressions EV are already reached.

FROM THEORY TO ENGINEERING

But one has to keep in mind that the purely formal achievement of a given vision document EV does not imply that the corresponding situation Sfut    is a real situation.  The corresponding situation Sfut  is first of all only an idea in the mind of the experts.  To transfer this idea into the real environment as a real situation is a process on its own known as engineering.

 

change, change - internally perceived, change - repeated, change - schema, change expression, change rule - application, change rules, expression - concrete, expression - decidable, Simulation, simulator

THE OKSIMO CASE as SUBJECT FOR PHILOSOPHY OF SCIENCE. Part 4. Describing Change

eJournal: uffmm.org
ISSN 2567-6458, 24.March – 24.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.

CHANGE

AS described in part 1 of the philosophy of science analysis of the oksimo behavior space it is here assumed — following  the ideas of  von Uexküll — that every biological species SP embedded in a real environment ENV transforms this environment  in its species specific internal representation  ENVSP which is no 1-to-1 mapping. Furthermore we know from modern Biology and brain research that the human brain cuts its sensory perceptions P into time-slices P1, P2, … which have durations between about 50 – 700 milliseconds and which are organized as multi-modal structures for further processing. The results of this processing are different kinds of abstracted structures which represent — not in a 1-to-1 fashion — different aspects of a given situation S which   in the moment of being processed and then being stored is not any longer actual, ‘not now’, but ‘gone‘, ‘past‘.

Thus if we as human actors are speaking about change then we are primarily speaking about the difference which our brain can compute comparing the actual situation S being kept in an actual time-slice P0 and those abstracted structures A(P) coming out of preceding time slices interacting in many various ways with other available abstracted structures:  Diff(A(P0), A(P)) = Δint. Usually we assume automatically that the perceived internal change Δint corresponds to a change in the actual situation S leading to a follow-up situation S’ which differs with regard to the species specific perception represented in Δint as Δext = Diff(S, S’). As psychological tests can  reveal  this automatic (unconscious) assumption that a perceived change Δint corresponds to a real external change Δext must not be the case. There is a real difference between Δint, Δext and on account of this difference there exists the possibility that we can detect an error  comparing our ideas with the real world environment. Otherwise — in the absence of an error —  a congruence can be interpreted as a confirmation of our ideas.

EXPRESSIONS CAN FOLLOW REAL PROPERTIES

As described in the preceding posts about a decidable start state S and a vision V  it is possible to map a perceived actual situation S in a set of expressions ES={e1, e2, …, en }. This general assumption is valid for all real states S, which results in the fact that a series of real states S1, S2, …, Sn is conceivable where every such real state Si can be associated with a set of expressions Ei which contain individual expressions ei which represent according to the presupposed meaning function φ certain aspects/ properties Pi of the corresponding real situation Si.  Thus, if two consecutive real states Si, Si+1 are include perceived  differences  indicated by some properties then it is possible to express these differences by corresponding expressions ei as part of the whole set of expressions Ei and Ei+1. If e.g. in the successor of Si one property px expressed by ex  is missing which is present in Si then the corresponding set Ei+1 should not include the expression ex. Or if the successor state Si+1 contains a property py expressed by the expression ey which is not yet given in Si then this fact too indicates a difference. Thus the differing pair (Si, Si+1)  could correspond to the pair (Ei, Ei+1) with ex as part of Ei but not any more in Ei+1 and the expression ey not part of Ei but then in Ei+1.

The general schema could be described as:

Si+1 = Si -{px} + {py} (the real dimension)

Ei+1 = Ei – {ex} + {ey} (the symbolic dimension)

Between the real dimension and the symbolic dimension is the body with the brain offering all the neural processing which is necessary to enable such complex mappings. This can bne expressed by the following pragmatic recipe:

symbolicarticulation: S x body[brain] —> E

symbolicarticulation(S,body[brain]) = E

Having a body with a brain embedded in an actual (real) situation S the body (with the brain) can produce symbolic expressions corresponding to certain properties of the situation S.

DESCRIBING CHANGE

Assuming that symbolic articulation is possible and that there is some regular mapping between an actual situation S and a set of expressions E it is conceivable to describe the generation of two successive actual states S, S’  as follows:

Apply a Change Rule ξ of X
  • We have a given actual situation S.
  • We have a group of human actors Ahum which are using a language L.
  • The group generates a decidable description of S as a set of expressions ELS following the rules of language L.
  • Thus we have symbolicarticulation(S, Ahum) = ELS
  • The group of human actors defines a finite set of change rules X which describe which expressions Eminus should be removed from ES and which expressions Eplus should be added to ES to get the successor state  ES‘ represented in a symbolic space:
  • ES‘ = ES – Eminus + Eplus . An individual change rule ξ of X has the format:
  • IF COND THEN with probability π REMOVE Eminus and ADD Eplus.
  • COND is a set of expressions which shall be a subset of the given set ES saying: COND ⊆ ES. If this condition is satisfied (fulfilled) then the rule can be applied following probability  π.
  • Thus applying a change rule ξ to a given state S means to operate on the corresponding set of expressions ES of  S as follows:
  • applychange: S x ES x {ξ}    —> ES‘
  • There can be more than only one change rule ξ as a finite set X = {ξ1, ξ2, …, ξn}. They have all to be applied in a random order. Thus we get:
  • applychange: S x ES x X   —> ES‘ or applychange(S,ES,X) = ES‘
Simulation

If one has a given actual state S with a finite set of change rules X we know now how to apply this finite set of change rules X to a given state description  ES. But if we would enlarge the set of change rules X in a way that this set X* not only contains rules for the given actual state description ES but also for a finite number of other possible state descriptions ES* then one could repeat the application of the change rules X* several times by using the last outcome desribing ES‘ to make ES‘ to the new actual state description ES. Proceeding in this way we can generate a whole sequence of state decriptions: <ES.0, ES.1, …, ES.n> where for each pair (ES.i, ES.i+1) it holds that  applychange(Si,ES.i,X) = ES.i+1

Such a repetitive application of the applychange() rule we call here a simulation: S x ES x X   —> <ES.0, ES.1, …, ES.n> with the condition  for each pair (ES.i, ES.i+1) that it holds that  applychange(Si,ES.i,X) = ES.i+1also written as: simulation(S , ES, X) = <ES.0, ES.1, …, ES.n>.

A device which can operate a simulation is called a simulator ∑. A simulator is either a human actor or a computer with an appropriate algorithm.

 

change rules, evaluation, evaluation module, oksimo reloaded software, oksimo software, requirements, Simulation, simulator, simulator module, states module, vision, vision module

OKSIMO SW – Minimal Basic Requirements

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, January 8, 2021
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

As described in the uffmm eJournal  the wider context of this software project is an integrated  engineering theory called Distributed Actor-Actor Interaction [DAAI]. This includes Human Machine Intelligence [HMIntelligence]  as part of Human Machine Interaction [HMI]. 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

In the long way of making the theory  as well as the software [SW] more concrete we have reached January 5, 2021 a first published version on [www.]oksimo.com.  This version contains a sub-part of the whole concept which I call here the Minimal Basic Version [MBV] of the osimo SW. This minimal basic will be tested until the end of february 2021. Then we will add stepwise all the other intended features.

THE MINIMAL BASIC VERSION

oksimo SW Minimal Basic Version Jan 3, 2021
oksimo SW Minimal Basic Version Jan 3, 2021

If one compares this figure with the figure of the Multi-Group Management from Dec 5, 2020 one can easily detect simplifications for the first modul now called Vision [V] as well as for the last modul called Evaluation [EVAL].

While the basic modules States [S], Change Rules [X] and Simulator [SIM] stayed the same the mentioned first and last module have slightly changed in the sense that they have become simplified.

During the first tests with the oksimo reloaded SW it became clear that for a simulation unified with evaluation  it is sufficient to have at least one vision V to be compared with an actual state S whether parts of the vision V are also part of the state S. This induced the requirement that a vision V has to be understood as a collection of statements where earch statement describes some aspect of a vision as a whole.

Example 1: Thus a global vision of a city to have a ‘Kindergarten’ could be extended with facts like ‘It is free for all children’, ‘I is constructed in an ecological acceptable manner’, …

Example 2: A global vision to have a system interface [SI] for the oksimo reloaded SW could include statements (facts) like: ‘The basic mode is text input in an everyday language’, ‘In an advanced mode you can use speech-recognition tools to enter a text into the system’, ‘The basic mode of the simulation output is text-based’, ‘In an advanced mode you can use text-to-speech SW to allow audio-output of the simulation’, ….

Vision V – Statement S: The citizen which will work with the oksimo reloaded SW has now only to distinguish between the vision V which points into some — as such — unknown future and the given situation S describing some part of the everyday world. The vision with all its possible different partial views (statements, facts) can then be used to a evaluate a given state S whether the vision is already part of it or not. If during a simulation a state S* has been reached and the global vision ‘The city has a Kindergarten’ is part of S*  but not the partial aspects ‘It is free for all children’, ‘I is constructed in an ecological acceptable manner’,  then only one third of the vision has been fulfilled: eval(V,S*)= 33,3 … %. As one can see the amount of vision facts determines the fineness of the evaluation.

Requirements Point of View: In Software Engineering [SWE] and — more general — in Human-Machine Interaction [HMI] as part of System Engineering [SE] the analysis phase is characterized by a list of functional and non-functional requirements [FR, NFR]. Both concepts are in the oksimo SW parts of the vision modul. Everything you think of  to be important for your vision you can write down as some aspect of the vision.  And if you want to structure your vision into several parts you can edit different vision documents which for a simulation can be united to one document again.

Change Rules [X]: In the minimal basic version only three components of a change rule X will be considered: The condition [COND] part which checks whether an actual state S satisfies (fulfills)  the condition; the Eplus part which contains facts which shall be added to the actual state S for the next turn; the Eminus part which contains facts which shall be removed from the actual state S für the next turn. Other components like Probability [PROB] or Model [MODEL] will be added in the future.

change, change expression, change rules, condition, duration, Eminus, Eplus, evaluation modes, evaluation state, expert, multiple states, preferences, Probability, problem document, resources, Simulation, simulation end state, simulation state, state, state description, vision statement

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)

abstract concept, abstract term, ACA Applied Cultural Anthropology, applied, autopoietic, autopoietic learning, change expression, change rules, decidable interpretation, descriptive, empirical science, empirical theory, empirically sound, inference concept, interactive simulation, interpretation, meaning, meaning function, passive simulation, Simulation, simulation game, static state, theory, triggering event, working group

CASE STUDY 1. FROM DAAI to ACA. Transforming HMI into ACA (Applied Cultural Anthropology)

eJournal: uffmm.org
ISSN 2567-6458, 28.July 2020
Email: info@uffmm.org

Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Abstract

The collection of papers in the Case Studies Section deals with the
possible applications of the general concept of a GCA Generative Cul-
tural Anthropology to all kinds of cultural processes. The GCA paradigm
has been derived from the formalized DAAI Distributed Actor-Actor In-
teraction theory, which in turn is a development based on the common
HMI Human Machine Interaction paradigm reformulated within the Sys-
tems Engineering paradigm. The GCA is a very general and strong theory
paradigm, but, saying this, it is for most people difficult to understand,
because it is highly interdisciplinary, and it needs some formal technical
skills, which are not too common. During the work in the last three
months it became clear, that the original HMI and DAAI approach can
also be understood as the case of something which one could call ACA
Applied Cultural Anthropology as part of an GCA. The concept of ACA
is more or less directly understandable for most people.

case1-daai-aca-v1