COMMON SCIENCE as Sustainable Applied Empirical Theory, besides ENGINEERING, in a SOCIETY

eJournal: uffmm.org
ISSN 2567-6458, 19.Juni 2022 – 15.September 2022
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

This is work in progress:

  1. The whole text shows a dynamic, which induces many changes. Difficult to plan ‘in advance’.
  2. Perhaps, some time, it will look like a ‘book’, at least ‘for a moment’.

CONTEXT and INTRODUCTION

In a rather foundational paper about an idea, how one can generalize ‘systems engineering’ [*1] to the art of ‘theory engineering’ [1] a new conceptual framework has been outlined for a ‘sustainable applied empirical theory (SAET)’. Part of this new framework has been the idea that the classical recourse to groups of special experts (mostly ‘engineers’ in engineering) is too restrictive in the light of the new requirement of being sustainable: sustainability is primarily based on ‘diversity’ combined with the ‘ability to predict’ from this diversity probable future states which keep life alive. The aspect of diversity induces the challenge to see every citizen as a ‘natural expert’, because nobody can know in advance and from some non-existing absolut point of truth, which knowledge is really important. History shows that the ‘mainstream’ is usually to a large degree ‘biased’ [*1b].

With this assumption, that every citizen is a ‘natural expert’, science turns into a ‘general science’ where all citizens are ‘natural members’ of science. I will call this more general concept of science ‘sustainable citizen science (SCS)’ or ‘Citizen Science 2.0 (CS2)’. The important point here is that a sustainable citizen science is not necessarily an ‘arbitrary’ process. While the requirement of ‘diversity’ relates to possible contents, to possible ideas, to possible experiments, and the like, it follows from the other requirement of ‘predictability’/ of being able to make some useful ‘forecasts’, that the given knowledge has to be in a format, which allows in a transparent way the construction of some consequences, which ‘derive’ from the ‘given’ knowledge and enable some ‘new’ knowledge. This ability of forecasting has often been understood as the business of ‘logic’ providing an ‘inference concept’ given by ‘rules of deduction’ and a ‘practical pattern (on the meta level)’, which defines how these rules have to be applied to satisfy the inference concept. But, looking to real life, to everyday life or to modern engineering and economy, one can learn that ‘forecasting’ is a complex process including much more than only cognitive structures nicely fitting into some formulas. For this more realistic forecasting concept we will use here the wording ‘common logic’ and for the cognitive adventure where common logic is applied we will use the wording ‘common science’. ‘Common science’ is structurally not different from ‘usual science’, but it has a substantial wider scope and is using the whole of mankind as ‘experts’.

The following chapters/ sections try to illustrate this common science view by visiting different special views which all are only ‘parts of a whole’, a whole which we can ‘feel’ in every moment, but which we can not yet completely grasp with our theoretical concepts.

CONTENT

  1. Language (Main message: “The ordinary language is the ‘meta language’ to every special language. This can be used as a ‘hint’ to something really great: the mystery of the ‘self-creating’ power of the ordinary language which for most people is unknown although it happens every moment.”)
  2. Concrete Abstract Statements (Main message: “… you will probably detect, that nearly all words of a language are ‘abstract words’ activating ‘abstract meanings’. …If you cannot provide … ‘concrete situations’ the intended meaning of your abstract words will stay ‘unclear’: they can mean ‘nothing or all’, depending from the decoding of the hearer.”)
  3. True False Undefined (Main message: “… it reveals that ’empirical (observational) evidence’ is not necessarily an automatism: it presupposes appropriate meaning spaces embedded in sets of preferences, which are ‘observation friendly’.
  4. Beyond Now (Main message: “With the aid of … sequences revealing possible changes the NOW is turned into a ‘moment’ embedded in a ‘process’, which is becoming the more important reality. The NOW is something, but the PROCESS is more.“)
  5. Playing with the Future (Main message: “In this sense seems ‘language’ to be the master tool for every brain to mediate its dynamic meaning structures with symbolic fix points (= words, expressions) which as such do not change, but the meaning is ‘free to change’ in any direction. And this ‘built in ‘dynamics’ represents an ‘internal potential’ for uncountable many possible states, which could perhaps become ‘true’ in some ‘future state’. Thus ‘future’ can begin in these potentials, and thinking is the ‘playground’ for possible futures.(but see [18])”)
  6. Forecasting – Prediction: What? (This chapter explains the cognitive machinery behind forecasting/ predictions, how groups of human actors can elaborate shared descriptions, and how it is possible to start with sequences of singularities to built up a growing picture of the empirical world which appears as a radical infinite and indeterministic space. )
  7. !!! From here all the following chapters have to be re-written !!!
  8. THE LOGIC OF EVERYDAY THINKING. Lets try an Example (Will probably be re-written too)
  9. Boolean Logic (Explains what boolean logic is, how it enables the working of programmable machines, but that it is of nearly no help for the ‘heart’ of forecasting.)
  10. … more re-writing will probably happen …
  11. Everyday Language: German Example
  12. Everyday Language: English
  13. Natural Logic
  14. Predicate Logic
  15. True Statements
  16. Formal Logic Inference: Preserving Truth
  17. Ordinary Language Inference: Preserving and Creating Truth
  18. Hidden Ontologies: Cognitively Real and Empirically Real
  19. AN INFERENCE IS NOT AUTOMATICALLY A FORECAST
  20. EMPIRICAL THEORY
  21. Side Trip to Wikipedia
  22. SUSTAINABLE EMPIRICAL THEORY
  23. CITIZEN SCIENCE 2.0
  24. … ???

COMMENTS

wkp-en := Englisch Wikipedia

/* Often people argue against the usage of the wikipedia encyclopedia as not ‘scientific’ because the ‘content’ of an entry in this encyclopedia can ‘change’. This presupposes the ‘classical view’ of scientific texts to be ‘stable’, which presupposes further, that such a ‘stable text’ describes some ‘stable subject matter’. But this view of ‘steadiness’ as the major property of ‘true descriptions’ is in no correspondence with real scientific texts! The reality of empirical science — even as in some special disciplines like ‘physics’ — is ‘change’. Looking to Aristotle’s view of nature, to Galileo Galilei, to Newton, to Einstein and many others, you will not find a ‘single steady picture’ of nature and science, and physics is only a very simple strand of science compared to the live-sciences and many others. Thus wikipedia is a real scientific encyclopedia give you the breath of world knowledge with all its strengths and limits at once. For another, more general argument, see In Favour for Wikipedia */

[*1] Meaning operator ‘…’ : In this text (and in nearly all other texts of this author) the ‘inverted comma’ is used quite heavily. In everyday language this is not common. In some special languages (theory of formal languages or in programming languages or in meta-logic) the inverted comma is used in some special way. In this text, which is primarily a philosophical text, the inverted comma sign is used as a ‘meta-language operator’ to raise the intention of the reader to be aware, that the ‘meaning’ of the word enclosed in the inverted commas is ‘text specific’: in everyday language usage the speaker uses a word and assumes tacitly that his ‘intended meaning’ will be understood by the hearer of his utterance as ‘it is’. And the speaker will adhere to his assumption until some hearer signals, that her understanding is different. That such a difference is signaled is quite normal, because the ‘meaning’ which is associated with a language expression can be diverse, and a decision, which one of these multiple possible meanings is the ‘intended one’ in a certain context is often a bit ‘arbitrary’. Thus, it can be — but must not — a meta-language strategy, to comment to the hearer (or here: the reader), that a certain expression in a communication is ‘intended’ with a special meaning which perhaps is not the commonly assumed one. Nevertheless, because the ‘common meaning’ is no ‘clear and sharp subject’, a ‘meaning operator’ with the inverted commas has also not a very sharp meaning. But in the ‘game of language’ it is more than nothing 🙂

[*1b] That the main stream ‘is biased’ is not an accident, not a ‘strange state’, not a ‘failure’, it is the ‘normal state’ based on the deeper structure how human actors are ‘built’ and ‘genetically’ and ‘cultural’ ‘programmed’. Thus the challenge to ‘survive’ as part of the ‘whole biosphere’ is not a ‘partial task’ to solve a single problem, but to solve in some sense the problem how to ‘shape the whole biosphere’ in a way, which enables a live in the universe for the time beyond that point where the sun is turning into a ‘red giant’ whereby life will be impossible on the planet earth (some billion years ahead)[22]. A remarkable text supporting this ‘complex view of sustainability’ can be found in Clark and Harvey, summarized at the end of the text. [23]

[*2] The meaning of the expression ‘normal’ is comparable to a wicked problem. In a certain sense we act in our everyday world ‘as if there exists some standard’ for what is assumed to be ‘normal’. Look for instance to houses, buildings: to a certain degree parts of a house have a ‘standard format’ assuming ‘normal people’. The whole traffic system, most parts of our ‘daily life’ are following certain ‘standards’ making ‘planning’ possible. But there exists a certain percentage of human persons which are ‘different’ compared to these introduced standards. We say that they have a ‘handicap’ compared to this assumed ‘standard’, but this so-called ‘standard’ is neither 100% true nor is the ‘given real world’ in its properties a ‘100% subject’. We have learned that ‘properties of the real world’ are distributed in a rather ‘statistical manner’ with different probabilities of occurrences. To ‘find our way’ in these varying occurrences we try to ‘mark’ the main occurrences as ‘normal’ to enable a basic structure for expectations and planning. Thus, if in this text the expression ‘normal’ is used it refers to the ‘most common occurrences’.

[*3] Thus we have here a ‘threefold structure’ embracing ‘perception events, memory events, and expression events’. Perception events represent ‘concrete events’; memory events represent all kinds of abstract events but they all have a ‘handle’ which maps to subsets of concrete events; expression events are parts of an abstract language system, which as such is dynamically mapped onto the abstract events. The main source for our knowledge about perceptions, memory and expressions is experimental psychology enhanced by many other disciplines.

[*4] Characterizing language expressions by meaning – the fate of any grammar: the sentence ” … ‘words’ (= expressions) of a language which can activate such abstract meanings are understood as ‘abstract words’, ‘general words’, ‘category words’ or the like.” is pointing to a deep property of every ordinary language, which represents the real power of language but at the same time the great weakness too: expressions as such have no meaning. Hundreds, thousands, millions of words arranged in ‘texts’, ‘documents’ can show some statistical patterns’ and as such these patterns can give some hint which expressions occur ‘how often’ and in ‘which combinations’, but they never can give a clue to the associated meaning(s). During more than three-thousand years humans have tried to describe ordinary language in a more systematic way called ‘grammar’. Due to this radically gap between ‘expressions’ as ‘observable empirical facts’ and ‘meaning constructs’ hidden inside the brain it was all the time a difficult job to ‘classify’ expressions as representing a certain ‘type’ of expression like ‘nouns’, ‘predicates’, ‘adjectives’, ‘defining article’ and the like. Without regressing to the assumed associated meaning such a classification is not possible. On account of the fuzziness of every meaning ‘sharp definitions’ of such ‘word classes’ was never and is not yet possible. One of the last big — perhaps the biggest ever — project of a complete systematic grammar of a language was the grammar project of the ‘Akademie der Wissenschaften der DDR’ (‘Academy of Sciences of the GDR’) from 1981 with the title “Grundzüge einer Deutschen Grammatik” (“Basic features of a German grammar”). A huge team of scientists worked together using many modern methods. But in the preface you can read, that many important properties of the language are still not sufficiently well describable and explainable. See: Karl Erich Heidolph, Walter Flämig, Wolfgang Motsch et al.: Grundzüge einer deutschen Grammatik. Akademie, Berlin 1981, 1028 Seiten.

[*5] Differing opinions about a given situation manifested in uttered expressions are a very common phenomenon in everyday communication. In some sense this is ‘natural’, can happen, and it should be no substantial problem to ‘solve the riddle of being different’. But as you can experience, the ability of people to solve the occurrence of different opinions is often quite weak. Culture is suffering by this as a whole.

[1] Gerd Doeben-Henisch, 2022, From SYSTEMS Engineering to THEORYEngineering, see: https://www.uffmm.org/2022/05/26/from-systems-engineering-to-theory-engineering/(Remark: At the time of citation this post was not yet finished, because there are other posts ‘corresponding’ with that post, which are too not finished. Knowledge is a dynamic network of interwoven views …).

[1d] ‘usual science’ is the game of science without having a sustainable format like in citizen science 2.0.

[2] Science, see e.g. wkp-en: https://en.wikipedia.org/wiki/Science

Citation = “Science is a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe.[1][2]

Citation = “In modern science, the term “theory” refers to scientific theories, a well-confirmed type of explanation of nature, made in a way consistent with the scientific method, and fulfilling the criteria required by modern science. Such theories are described in such a way that scientific tests should be able to provide empirical support for it, or empirical contradiction (“falsify“) of it. Scientific theories are the most reliable, rigorous, and comprehensive form of scientific knowledge,[1] in contrast to more common uses of the word “theory” that imply that something is unproven or speculative (which in formal terms is better characterized by the word hypothesis).[2] Scientific theories are distinguished from hypotheses, which are individual empirically testable conjectures, and from scientific laws, which are descriptive accounts of the way nature behaves under certain conditions.”

Citation = “New knowledge in science is advanced by research from scientists who are motivated by curiosity about the world and a desire to solve problems.[27][28] Contemporary scientific research is highly collaborative and is usually done by teams in academic and research institutions,[29] government agencies, and companies.[30][31] The practical impact of their work has led to the emergence of science policies that seek to influence the scientific enterprise by prioritizing the ethical and moral development of commercial productsarmamentshealth carepublic infrastructure, and environmental protection.”

[2b] History of science in wkp-en: https://en.wikipedia.org/wiki/History_of_science#Scientific_Revolution_and_birth_of_New_Science

[3] Theory, see wkp-en: https://en.wikipedia.org/wiki/Theory#:~:text=A%20theory%20is%20a%20rational,or%20no%20discipline%20at%20all.

Citation = “A theory is a rational type of abstract thinking about a phenomenon, or the results of such thinking. The process of contemplative and rational thinking is often associated with such processes as observational study or research. Theories may be scientific, belong to a non-scientific discipline, or no discipline at all. Depending on the context, a theory’s assertions might, for example, include generalized explanations of how nature works. The word has its roots in ancient Greek, but in modern use it has taken on several related meanings.”

[4] Scientific theory, see: wkp-en: https://en.wikipedia.org/wiki/Scientific_theory

Citation = “In modern science, the term “theory” refers to scientific theories, a well-confirmed type of explanation of nature, made in a way consistent with the scientific method, and fulfilling the criteria required by modern science. Such theories are described in such a way that scientific tests should be able to provide empirical support for it, or empirical contradiction (“falsify“) of it. Scientific theories are the most reliable, rigorous, and comprehensive form of scientific knowledge,[1] in contrast to more common uses of the word “theory” that imply that something is unproven or speculative (which in formal terms is better characterized by the word hypothesis).[2] Scientific theories are distinguished from hypotheses, which are individual empirically testable conjectures, and from scientific laws, which are descriptive accounts of the way nature behaves under certain conditions.”

[4b] Empiricism in wkp-en: https://en.wikipedia.org/wiki/Empiricism

[4c] Scientific method in wkp-en: https://en.wikipedia.org/wiki/Scientific_method

Citation =”The scientific method is an empirical method of acquiring knowledge that has characterized the development of science since at least the 17th century (with notable practitioners in previous centuries). It involves careful observation, applying rigorous skepticism about what is observed, given that cognitive assumptions can distort how one interprets the observation. It involves formulating hypotheses, via induction, based on such observations; experimental and measurement-based statistical testing of deductions drawn from the hypotheses; and refinement (or elimination) of the hypotheses based on the experimental findings. These are principles of the scientific method, as distinguished from a definitive series of steps applicable to all scientific enterprises.[1][2][3] [4c]

and

Citation = “The purpose of an experiment is to determine whether observations[A][a][b] agree with or conflict with the expectations deduced from a hypothesis.[6]: Book I, [6.54] pp.372, 408 [b] Experiments can take place anywhere from a garage to a remote mountaintop to CERN’s Large Hadron Collider. There are difficulties in a formulaic statement of method, however. Though the scientific method is often presented as a fixed sequence of steps, it represents rather a set of general principles.[7] Not all steps take place in every scientific inquiry (nor to the same degree), and they are not always in the same order.[8][9]

[5] Gerd Doeben-Henisch, “Is Mathematics a Fake? No! Discussing N.Bourbaki, Theory of Sets (1968) – Introduction”, 2022, https://www.uffmm.org/2022/06/06/n-bourbaki-theory-of-sets-1968-introduction/

[6] Logic, see wkp-en: https://en.wikipedia.org/wiki/Logic

[7] W. C. Kneale, The Development of Logic, Oxford University Press (1962)

[8] Set theory, in wkp-en: https://en.wikipedia.org/wiki/Set_theory

[9] N.Bourbaki, Theory of Sets , 1968, with a chapter about structures, see: https://en.wikipedia.org/wiki/%C3%89l%C3%A9ments_de_math%C3%A9matique

[10] = [5]

[11] Ludwig Josef Johann Wittgenstein ( 1889 – 1951): https://en.wikipedia.org/wiki/Ludwig_Wittgenstein

[12] Ludwig Wittgenstein, 1953: Philosophische Untersuchungen [PU], 1953: Philosophical Investigations [PI], translated by G. E. M. Anscombe /* For more details see: https://en.wikipedia.org/wiki/Philosophical_Investigations */

[13] Wikipedia EN, Speech acts: https://en.wikipedia.org/wiki/Speech_act

[14] While the world view constructed in a brain is ‘virtual’ compared to the ‘real word’ outside the brain (where the body outside the brain is also functioning as ‘real world’ in relation to the brain), does the ‘virtual world’ in the brain function for the brain mostly ‘as if it is the real world’. Only under certain conditions can the brain realize a ‘difference’ between the triggering outside real world and the ‘virtual substitute for the real world’: You want to use your bicycle ‘as usual’ and then suddenly you have to notice that it is not at that place where is ‘should be’. …

[15] Propositional Calculus, see wkp-en: https://en.wikipedia.org/wiki/Propositional_calculus#:~:text=Propositional%20calculus%20is%20a%20branch,of%20arguments%20based%20on%20them.

[16] Boolean algebra, see wkp-en: https://en.wikipedia.org/wiki/Boolean_algebra

[17] Boolean (or propositional) Logic: As one can see in the mentioned articles of the English wikipedia, the term ‘boolean logic’ is not common. The more logic-oriented authors prefer the term ‘boolean calculus’ [15] and the more math-oriented authors prefer the term ‘boolean algebra’ [16]. In the view of this author the general view is that of ‘language use’ with ‘logic inference’ as leading idea. Therefore the main topic is ‘logic’, in the case of propositional logic reduced to a simple calculus whose similarity with ‘normal language’ is widely ‘reduced’ to a play with abstract names and operators. Recommended: the historical comments in [15].

[18] Clearly, thinking alone can not necessarily induce a possible state which along the time line will become a ‘real state’. There are numerous factors ‘outside’ the individual thinking which are ‘driving forces’ to push real states to change. But thinking can in principle synchronize with other individual thinking and — in some cases — can get a ‘grip’ on real factors causing real changes.

[19] This kind of knowledge is not delivered by brain science alone but primarily from experimental (cognitive) psychology which examines observable behavior and ‘interprets’ this behavior with functional models within an empirical theory.

[20] Predicate Logic or First-Order Logic or … see: wkp-en: https://en.wikipedia.org/wiki/First-order_logic#:~:text=First%2Dorder%20logic%E2%80%94also%20known,%2C%20linguistics%2C%20and%20computer%20science.

[21] Gerd Doeben-Henisch, In Favour of Wikipedia, https://www.uffmm.org/2022/07/31/in-favour-of-wikipedia/, 31 July 2022

[22] The sun, see wkp-ed https://en.wikipedia.org/wiki/Sun (accessed 8 Aug 2022)

[23] By Clark, William C., and Alicia G. Harley – https://doi.org/10.1146/annurev-environ-012420-043621, Clark, William C., and Alicia G. Harley. 2020. “Sustainability Science: Toward a Synthesis.” Annual Review of Environment and Resources 45 (1): 331–86, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=109026069

[24] Sustainability in wkp-en: https://en.wikipedia.org/wiki/Sustainability#Dimensions_of_sustainability

[25] Sustainable Development in wkp-en: https://en.wikipedia.org/wiki/Sustainable_development

[26] Marope, P.T.M; Chakroun, B.; Holmes, K.P. (2015). Unleashing the Potential: Transforming Technical and Vocational Education and Training (PDF). UNESCO. pp. 9, 23, 25–26. ISBN978-92-3-100091-1.

[27] SDG 4 in wkp-en: https://en.wikipedia.org/wiki/Sustainable_Development_Goal_4

[28] Thomas Rid, Rise of the Machines. A Cybernetic History, W.W.Norton & Company, 2016, New York – London

[29] Doeben-Henisch, G., 2006, Reducing Negative Complexity by a Semiotic System In: Gudwin, R., & Queiroz, J., (Eds). Semiotics and Intelligent Systems Development. Hershey et al: Idea Group Publishing, 2006, pp.330-342

[30] Döben-Henisch, G.,  Reinforcing the global heartbeat: Introducing the planet earth simulator project, In M. Faßler & C. Terkowsky (Eds.), URBAN FICTIONS. Die Zukunft des Städtischen. München, Germany: Wilhelm Fink Verlag, 2006, pp.251-263

[29] The idea that individual disciplines are not good enough for the ‘whole of knowledge’ is expressed in a clear way in a video of the theoretical physicist and philosopher Carlo Rovell: Carlo Rovelli on physics and philosophy, June 1, 2022, Video from the Perimeter Institute for Theoretical Physics. Theoretical physicist, philosopher, and international bestselling author Carlo Rovelli joins Lauren and Colin for a conversation about the quest for quantum gravity, the importance of unlearning outdated ideas, and a very unique way to get out of a speeding ticket.

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THE OKSIMO CASE as SUBJECT FOR PHILOSOPHY OF SCIENCE. Part 1

eJournal: uffmm.org
ISSN 2567-6458, 22.March – 23.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.

THE OKSIMO EVENT SPACE

The characterization of the oksimo software paradigm starts with an informal characterization  of the oksimo software event space.

EVENT SPACE

An event space is a space which can be filled up by observable events fitting to the species-specific internal processed environment representations [1], [2] here called internal environments [ENVint]. Thus the same external environment [ENV] can be represented in the presence of  10 different species  in 10 different internal formats. Thus the expression ‘environment’ [ENV] is an abstract concept assuming an objective reality which is common to all living species but indeed it is processed by every species in a species-specific way.

In a human culture the usual point of view [ENVhum] is simultaneous with all the other points of views [ENVa] of all the other other species a.

In the ideal case it would be possible to translate all species-specific views ENVa into a symbolic representation which in turn could then be translated into the human point of view ENVhum. Then — in the ideal case — we could define the term environment [ENV] as the sum of all the different species-specific views translated in a human specific language: ∑ENVa = ENV.

But, because such a generalized view of the environment is until today not really possible by  practical reasons we will use here for the beginning only expressions related to the human specific point of view [ENVhum] using as language an ordinary language [L], here  the English language [LEN]. Every scientific language — e.g. the language of physics — is understood here as a sub language of the ordinary language.

EVENTS

An event [EV] within an event space [ENVa] is a change [X] which can be observed at least from the  members of that species [SP] a which is part of that environment ENV which enables  a species-specific event space [ENVa]. Possibly there can be other actors around in the environment ENV from different species with their specific event space [ENVa] where the content of the different event spaces  can possible   overlap with regard to  certain events.

A behavior is some observable movement of the body of some actor.

Changes X can be associated with certain behavior of certain actors or with non-actor conditions.

Thus when there are some human or non-human  actors in an environment which are moving than they show a behavior which can eventually be associated with some observable changes.

CHANGE

Besides being   associated with observable events in the (species specific) environment the expression  change is understood here as a kind of inner state in an actor which can compare past (stored) states Spast with an actual state SnowIf the past and actual state differ in some observable aspect Diff(Spast, Snow) ≠ 0, then there exists some change X, or Diff(Spast, Snow) = X. Usually the actor perceiving a change X will assume that this internal structure represents something external to the brain, but this must not necessarily be the case. It is of help if there are other human actors which confirm such a change perception although even this does not guarantee that there really is a  change occurring. In the real world it is possible that a whole group of human actors can have a wrong interpretation.

SYMBOLIC COMMUNICATION AND MEANING

It is a specialty of human actors — to some degree shared by other non-human biological actors — that they not only can built up internal representations ENVint of the reality external to the  brain (the body itself or the world beyond the body) which are mostly unconscious, partially conscious, but also they can built up structures of expressions of an internal language Lint which can be mimicked to a high degree by expressions in the body-external environment ENV called expressions of an ordinary language L.

For this to work one  has  to assume that there exists an internal mapping from internal representations ENVint into the expressions of the internal language   Lint as

meaning : ENVint <—> Lint.

and

speaking: Lint —> L

hearing: Lint <— L

Thus human actors can use their ordinary language L to activate internal encodings/ decodings with regard to the internal representations ENVint  gained so far. This is called here symbolic communication.

NO SPEECH ACTS

To classify the occurrences of symbolic expressions during a symbolic communication  is a nearly infinite undertaking. First impressions of the unsolvability of such a classification task can be gained if one reads the Philosophical Investigations of Ludwig Wittgenstein. [5] Later trials from different philosophers and scientists  — e.g. under the heading of speech acts [4] — can  not fully convince until today.

Instead of assuming here a complete scientific framework to classify  occurrences of symbolic expressions of an ordinary language L we will only look to some examples and discuss these.

KINDS OF EXPRESSIONS

In what follows we will look to some selected examples of symbolic expressions and discuss these.

(Decidable) Concrete Expressions [(D)CE]

It is assumed here that two human actors A and B  speaking the same ordinary language L  are capable in a concrete situation S to describe objects  OBJ and properties PROP of this situation in a way, that the hearer of a concrete expression E can decide whether the encoded meaning of that expression produced by the speaker is part of the observable situation S or not.

Thus, if A and B are together in a room with a wooden  white table and there is a enough light for an observation then   B can understand what A is saying if he states ‘There is a white wooden table.

To understand means here that both human actors are able to perceive the wooden white table as an object with properties, their brains will transform these external signals into internal neural signals forming an inner — not 1-to-1 — representation ENVint which can further be mapped by the learned meaning function into expressions of the inner language Lint and mapped further — by the speaker — into the external expressions of the learned ordinary language L and if the hearer can hear these spoken expressions he can translate the external expressions into the internal expressions which can be mapped onto the learned internal representations ENVint. In everyday situations there exists a high probability that the hearer then can respond with a spoken ‘Yes, that’s true’.

If this happens that some human actor is uttering a symbolic expression with regard to some observable property of the external environment  and the other human actor does respond with a confirmation then such an utterance is called here a decidable symbolic expression of the ordinary language L. In this case one can classify such an expression  as being true. Otherwise the expression  is classified as being not true.

The case of being not true is not a simple case. Being not true can mean: (i) it is actually simply not given; (ii) it is conceivable that the meaning could become true if the external situation would be  different; (iii) it is — in the light of the accessible knowledge — not conceivable that the meaning could become true in any situation; (iv) the meaning is to fuzzy to decided which case (i) – (iii) fits.

Cognitive Abstraction Processes

Before we talk about (Undecidable) Universal Expressions [(U)UE] it has to clarified that the internal mappings in a human actor are not only non-1-to-1 mappings but they are additionally automatic transformation processes of the kind that concrete perceptions of concrete environmental matters are automatically transformed by the brain into different kinds of states which are abstracted states using the concrete incoming signals as a  trigger either to start a new abstracted state or to modify an existing abstracted state. Given such abstracted states there exist a multitude of other neural processes to process these abstracted states further embedded  in numerous  different relationships.

Thus the assumed internal language Lint does not map the neural processes  which are processing the concrete events as such but the processed abstracted states! Language expressions as such can never be related directly to concrete material because this concrete material  has no direct  neural basis.  What works — completely unconsciously — is that the brain can detect that an actual neural pattern nn has some similarity with a  given abstracted structure NN  and that then this concrete pattern nn  is internally classified as an instance of NN. That means we can recognize that a perceived concrete matter nn is in ‘the light of’ our available (unconscious) knowledge an NN, but we cannot argue explicitly why. The decision has been processed automatically (unconsciously), but we can become aware of the result of this unconscious process.

Universal (Undecidable) Expressions [U(U)E]

Let us repeat the expression ‘There is a white wooden table‘ which has been used before as an example of a concrete decidable expression.

If one looks to the different parts of this expression then the partial expressions ‘white’, ‘wooden’, ‘table’ can be mapped by a learned meaning function φ into abstracted structures which are the result of internal processing. This means there can be countable infinite many concrete instances in the external environment ENV which can be understood as being white. The same holds for the expressions ‘wooden’ and ‘table’. Thus the expressions ‘white’, ‘wooden’, ‘table’ are all related to abstracted structures and therefor they have to be classified as universal expressions which as such are — strictly speaking —  not decidable because they can be true in many concrete situations with different concrete matters. Or take it otherwise: an expression with a meaning function φ pointing to an abstracted structure is asymmetric: one expression can be related to many different perceivable concrete matters but certain members of  a set of different perceived concrete matters can be related to one and the same abstracted structure on account of similarities based on properties embedded in the perceived concrete matter and being part of the abstracted structure.

In a cognitive point of view one can describe these matters such that the expression — like ‘table’ — which is pointing to a cognitive  abstracted structure ‘T’ includes a set of properties Π and every concrete perceived structure ‘t’ (caused e.g. by some concrete matter in our environment which we would classify as a ‘table’) must have a ‘certain amount’ of properties Π* that one can say that the properties  Π* are entailed in the set of properties Π of the abstracted structure T, thus Π* ⊆ Π. In what circumstances some speaker-hearer will say that something perceived concrete ‘is’ a table or ‘is not’ a table will depend from the learning history of this speaker-hearer. A child in the beginning of learning a language L can perhaps call something   a ‘chair’ and the parents will correct the child and will perhaps  say ‘no, this is table’.

Thus the expression ‘There is a white wooden table‘ as such is not true or false because it is not clear which set of concrete perceptions shall be derived from the possible internal meaning mappings, but if a concrete situation S is given with a concrete object with concrete properties then a speaker can ‘translate’ his/ her concrete perceptions with his learned meaning function φ into a composed expression using universal expressions.  In such a situation where the speaker is  part of  the real situation S he/ she  can recognize that the given situation is an  instance of the abstracted structures encoded in the used expression. And recognizing this being an instance interprets the universal expression in a way  that makes the universal expression fitting to a real given situation. And thereby the universal expression is transformed by interpretation with φ into a concrete decidable expression.

SUMMING UP

Thus the decisive moment of turning undecidable universal expressions U(U)E into decidable concrete expressions (D)CE is a human actor A behaving as a speaker-hearer of the used  language L. Without a speaker-hearer every universal expressions is undefined and neither true nor false.

makedecidable :  S x Ahum x E —> E x {true, false}

This reads as follows: If you want to know whether an expression E is concrete and as being concrete is  ‘true’ or ‘false’ then ask  a human actor Ahum which is part of a concrete situation S and the human actor shall  answer whether the expression E can be interpreted such that E can be classified being either ‘true’ or ‘false’.

The function ‘makedecidable()’ is therefore  the description (like a ‘recipe’) of a real process in the real world with real actors. The important factors in this description are the meaning functions inside the participating human actors. Although it is not possible to describe these meaning functions directly one can check their behavior and one can define an abstract model which describes the observable behavior of speaker-hearer of the language L. This is an empirical model and represents the typical case of behavioral models used in psychology, biology, sociology etc.

SOURCES

[1] Jakob Johann Freiherr von Uexküll (German: [ˈʏkskʏl])(1864 – 1944) https://en.wikipedia.org/wiki/Jakob_Johann_von_Uexk%C3%BCll

[2] Jakob von Uexküll, 1909, Umwelt und Innenwelt der Tiere. Berlin: J. Springer. (Download: https://ia802708.us.archive.org/13/items/umweltundinnenwe00uexk/umweltundinnenwe00uexk.pdf )

[3] Wikipedia EN, Speech acts: https://en.wikipedia.org/wiki/Speech_act

[4] Ludwig Josef Johann Wittgenstein ( 1889 – 1951): https://en.wikipedia.org/wiki/Ludwig_Wittgenstein

[5] Ludwig Wittgenstein, 1953: Philosophische Untersuchungen [PU], 1953: Philosophical Investigations [PI], translated by G. E. M. Anscombe /* For more details see: https://en.wikipedia.org/wiki/Philosophical_Investigations */

CASE STUDY – SIMULATION GAMES – PHASE 1 – Iterative Development of a Dynamic World Model

ISSN 2567-6458, 19.-30.June 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

To work within the Generative Cultural Anthropology [GCA] Theory one needs a practical tool which allows the construction of dynamic world models, the storage of these models, their usage within a simulation game environment together with an evaluation tool.  To prepare a simulation game within a Hybrid Simulation Game Environment [HSGE] one needs an
iterative development process which is described below.

CASE STUDY – SIMULATION GAMES – PHASE 1: Iterative Development of a Dynamic World Model – Part of the Generative Cultural Anthropology [GCA] Theory

Contents
1 Overview of the Whole Development Process
2 Cognitive Aspects of Symbolic Expressions
3 Symbolic Representations and Transformations
4 Abstract-Concrete Concepts
5 Implicit Structures Embedded in Experience
5.1 Example 1

daai-analysis-simgame-development-v3 (June-30, 2020)

daai-analysis-simgame-development-v2 (June-20, 2020)

daai-analysis-simgame-development-v1 (June-19,2020)

Going back to the section Case Studies.

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)

 

 

REVIEWS

eJournal: uffmm.org,
ISSN 2567-6458, 18 June 2019 – 24 August 2022
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

This post is part of the uffmm science blog and collects reviews of books related to the uffmm subject.

COLLECTION OF REVIEWS

The most recent review is on top:

  1. René 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) (Last change: 24 August 2022)
  2. Is Mathematics a Fake? No! Discussing N.Bourbaki, Theory of Sets (1968), – Introduction (Last change: June 13, 2022)
  3. Pierre Lévy : Collective Intelligence (fr.1994, en.1997) – Chapter 8, Anthropological Space (Last change: May 15, 2022)
  4. Pierre Lévy : Collective Intelligence (fr.1994, en.1997) – Chapter 7, The Four Spaces (Last change: 1.April 2022)
  5. Pierre Lévy : Collective Intelligence (fr.1994, en.1997) – Chapter 1, Introduction (Last change: 22.March 2022)
  6. Pierre Lévy: Footnote ‘Knowledge Tree’, in: Collective Intelligence (1979) (Last change: 18.March 2022)
  7. Comments on Thomas Rid (2016), Rise of the machines. A cybernetics History. W.W.Norton & Company, Independent Publishers Since 1923 (New York – London). /* The German edition: maschinen dämmerung. eine kurze geschichte der kybernetik published 2016 by the Publisher Propyläen, owned by Ullstein Buchverlag GmbH (Berlin) */ (Last change: Sept 29, 2021)
  8. LOGIC. The Theory Of Inquiry (1938) by John Dewey – An oksimo Review – Part 1 (Last change: Aug 18, 2021)
  9. LOGIC. The Theory Of Inquiry (1938) by John Dewey – An oksimo Review – Part 2 (Last change: Aug 18, 2021)
  10. LOGIC. The Theory Of Inquiry (1938) by John Dewey – An oksimo Review – Part 3 (Last change: Aug 20, 2021)
  11. Review of the book Why the World Needs Anthropologists edited by Dan Podjed, Meta Gorup, Pavel Borecký & Carla Guerrón Montero, 2021 (already distributed November 2020), Publisher: Routledge (Landon – New York)(Last change: December 1, 2020)
  12. Review of Tarski (1936) On the concept of logical consequence, (1936) The establishment of scientific semantics, in one paper. (published 8.August 2020)
  13. Review of Maslow (1966) The Psychology of Science.(Part I: June-1, 2020, Part II: 21.Juni 2020)
  14. Review of EU’s trustworthy AI Ethic with Denning & Denning (2020)  and other authors from the point of view of GCA theory (May-11, 2020).
  15. Review of Tsu and Nourbakhsh (2020), When Human-Computer Interaction Meets Community Citizen Science. Empowering communities through citizen science. In the Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM 2017: review-Tsu-et-2020-acm-CommunitySciences (April-6, 2020)
  16. Review of Nancy Leveson (2020), Are you sure your software will not kill anyone?, Communications of the ACM, February 2020, Vol.63, No.2, pp.25-28: review-leveson-2020-acm-yourSWwillNotKill
  17. Review of Miller & Page (2007), Complex Adaptive Systems. An Introduction to Computational Models of Social Life, example No.1 from Chapter 7: review-santa-fe-2-miller-page-2007-example-c7-no1c (PDF, Febr 5, 2020)
  18. Review of Miller & Page (2007), Complex Adaptive Systems. An Introduction to Computational Models of Social Life, Chapters 1-7,final: review-santa-fe-1-miller-page-2007-cc1-7-final (PDF, final, Febr 1,2020)
  19. Review of Cathy Stein Greenblat (1988), DESIGNING GAMES and SIMULATIONS, Complete review-greenblat-1988-1-2
  20. Review of Alan Newell and Herbert A.Simon (1972), Human Problem Solving (Last update: Oct 9, 2019):  review-newell-simon-1972-V1-4 Comment: This document will be replaced several times by the next extended version with the discussion of the text. One document spans in the end one complete chapter.
  21. Review of Peter Gärdenfors (2014), Geometry of Meaning. Semantics Based on Conceptual Spaces, Part 1, A Review from a Philosophical Point of View: review-gaerdenfors2014-c1-2
  22. Review of Charles R.Gallistel, (1990), The Organization of Learning. Part 1, A Review from a Philosophical Point of View: review-gallistel-part1-C1

Remark: There have been many more reviews before this review section but these have been written in German and are located in the philosophy blog of G.Doeben Henisch.

ACI – TWO DIFFERENT READINGS

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

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

HCI – HMI – AAI ==> ACI ?

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

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

HISTORY

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

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

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

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

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

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

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

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

COGNITION EMBEDDED IN MATTER

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

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

SOLVING A PROBLEM

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

COGNITION AND AUGMENTED COLLECTIVE INTELLIGENCE

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

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

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

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

ACI ANALYSIS PROCESS

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

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

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

ACTOR STORY (AS)

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

SEVERAL TESTS

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

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

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

COLLECTIVE MEMORY

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

 

 

AAI THEORY V2 – AS AND REAL WORLD MODELING

eJournal: uffmm.org,
ISSN 2567-6458, 2.February 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 special topic how the actor story (AS) can be used for a modeling of the real world (RW).

AS AND REAL WORLD MODELING

In the preceding post you find a rough description how an actor story can be generated challenged by a problem P. Here I shall address the question, how this procedure can be used to model certain aspects of the real world and not some abstract ideas only.

There are two main elements of the actor story which can be related to the real world: (i)  The start state of the actor story and the list of possible change expressions.

FACTS

A start state is a finite set of facts which in turn are — in the case of the mathematical language — constituted by names of objects associated with properties or relations. Primarily   the possible meaning of these expressions is  located in the cognitive structures of the actors. These cognitive structures are as such not empirical entities and are partially available in a state called consciousness. If some element of meaning is conscious and simultaneously part of the inter-subjective space between different actors in a way that all participating actors can perceive these elements, then these elements are called empirical by everyday experience, if these facts can be decided between the participants of the situation.  If there exist further explicit measurement procedures associating an inter-subjective property with inter-subjective measurement data then these elements are called genuine empirical data.

Thus the collection of facts constituting a state of an actor story can be realized as a set of empirical facts, at least in the format of empirical by everyday experience.

CHANGES

While a state represents only static facts, one needs an additional element to be able to model the dynamic aspect of the real world. This is realized by change expressions X. 

The general idea of a change is that at least one fact f of an actual state (= NOW), is changed either by complete disappearance or by changing some of its properties or by the creation of a new fact f1. An object called ‘B1’ with the property being ‘red’ — written as ‘RED(B1)’ — perhaps changes its property from being ‘red’ to become ‘blue’ — written as ‘BLUE(B1)’ –. Then the set of facts of the actual state S0= {RED(B1)} will change to a successor state S1={BLUE(B1)}. In this case the old fact ‘RED(B1)’ has been deleted and the new fact ‘BLUE(B1)’ has been created.  Another example:  the object ‘B1’ has also a ‘weight’ measured in kg which changes too. Then the actual state was S0={RED(B1), WEIGHT(B1,kg,2.4)} and this state changed to the successor state S1= {BLUE(B1), WEIGHT(B1,kg,3.4)}.

The possible cause of a change can be either an object or the ‘whole state‘ representing the world.

The mapping from a given state s into a successor state s’ by subtracting facts f- and joining facts f+ is here called an action: S –> S-(f-) u (f+) or action(s) = s’ = s-(f-) u (f+) with s , s’ in S

Because an action has an actor as a carrier one can write action: S x A –>  S-(f-) u (f+) or action_a(s) = s’.

The defining properties of such an action are given in the sets of facts to be deleted — written as ‘d:{f-}’ — and the sets of facts to be created — written ‘c:{f+}’ –.

A full change expression amounts then to the following format: <s,s’, obj-name, action-name, d:{…}, c:{…}>.

But this is not yet the whole story.  A change can be deterministic or indeterministic.

The deterministic change is cause by a deterministic actor or by a deterministic world.

The indeterministic change can have several formats:e.g.  classical probability or quantum-like probability or the an actor as cause, whose behavior is not completely deterministic.

Additionally there can be interactions between different objects which can cause a change and these changes   happen in parallel, simultaneously. Depending from the assumed environment (= world) and some laws describing the behavior of this world it can happen, that different local actions can hinder each other or change the effect of the changes.

Independent of the different kinds of changes it can be required that all used change-expressions should be of that kind that one can state that they are   empirical by everyday experience.

TIME

And there is even more to tell. A change has in everyday life a duration measured with certain time units generated by a technical device called a clock.

To improve the empirical precision of change expressions one has to add the duration of the change between the actual state s and the final state s’ showing all the deletes (f-) and creates (f+) which are caused by this change-expression. This can only be done if a standard clock is included in the facts represented by the actual time stamp of this clock. Thus with regard to such a standard time one can realize a change with duration (t,t’)  exactly in coherence with the standard time. A special case is given when a change-expression describes the effects of its actions in a distributed  manner by giving more than one time point (t,t1, …, tn) and associating different deletes and creates with different points of time.  Those distributed effects can make an actor story rather complex and difficult to understand by human brains.