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.

[] By Azote for Stockholm Resilience Centre, Stockholm University – https://www.stockholmresilience.org/research/research-news/2016-06-14-how-food-connects-all-the-sdgs.html, CC BY 4.0, https://commons.wikimedia.org/w/index.php?curid=112497386

[]  Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) in wkp-en, UTL: https://en.wikipedia.org/wiki/Intergovernmental_Science-Policy_Platform_on_Biodiversity_and_Ecosystem_Services

[] IPBES (2019): Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. E. S. Brondizio, J. Settele, S. Díaz, and H. T. Ngo (editors). IPBES secretariat, Bonn, Germany. 1148 pages. https://doi.org/10.5281/zenodo.3831673

[] Michaelis, L. & Lorek, S. (2004). “Consumption and the Environment in Europe: Trends and Futures.” Danish Environmental Protection Agency. Environmental Project No. 904.

[] Pezzey, John C. V.; Michael A., Toman (2002). “The Economics of Sustainability: A Review of Journal Articles” (PDF). . Archived from the original (PDF) on 8 April 2014. Retrieved 8 April 2014.

[] World Business Council for Sustainable Development (WBCSD)  in wkp-en: https://en.wikipedia.org/wiki/World_Business_Council_for_Sustainable_Development

[] Sierra Club in wkp-en: https://en.wikipedia.org/wiki/Sierra_Club

[] Herbert Bruderer, Where is the Cradle of the Computer?, June 20, 2022, URL: https://cacm.acm.org/blogs/blog-cacm/262034-where-is-the-cradle-of-the-computer/fulltext (accessed: July 20, 2022)

[] UN. Secretary-GeneralWorld Commission on Environment and Development, 1987, Report of the World Commission on Environment and Development : note / by the Secretary-General., https://digitallibrary.un.org/record/139811 (accessed: July 20, 2022) (A more readable format: https://sustainabledevelopment.un.org/content/documents/5987our-common-future.pdf )

/* Comment: Gro Harlem Brundtland (Norway) has been the main coordinator of this document */

[] Chaudhuri, S.,et al.Neurosymbolic programming. Foundations and Trends in Programming Languages 7, 158-243 (2021).

[] Noam Chomsky, “A Review of B. F. Skinner’s Verbal Behavior”, in Language, 35, No. 1 (1959), 26-58.(Online: https://chomsky.info/1967____/, accessed: July 21, 2022)

[] Churchman, C. West (December 1967). “Wicked Problems”Management Science. 14 (4): B-141–B-146. doi:10.1287/mnsc.14.4.B141.

[-] Yen-Chia Hsu, Illah Nourbakhsh, “When Human-Computer Interaction Meets Community Citizen Science“,Communications of the ACM, February 2020, Vol. 63 No. 2, Pages 31-34, 10.1145/3376892, https://cacm.acm.org/magazines/2020/2/242344-when-human-computer-interaction-meets-community-citizen-science/fulltext

[] Yen-Chia Hsu, Ting-Hao ‘Kenneth’ Huang, Himanshu Verma, Andrea Mauri, Illah Nourbakhsh, Alessandro Bozzon, Empowering local communities using artificial intelligence, DOI:https://doi.org/10.1016/j.patter.2022.100449, CellPress, Patterns, VOLUME 3, ISSUE 3, 100449, MARCH 11, 2022

[] Nello Cristianini, Teresa Scantamburlo, James Ladyman, The social turn of artificial intelligence, in: AI & SOCIETY, https://doi.org/10.1007/s00146-021-01289-8

[] Carl DiSalvo, Phoebe Sengers, and Hrönn Brynjarsdóttir, Mapping the landscape of sustainable hci, In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’10, page 1975–1984, New York, NY, USA, 2010. Association for Computing Machinery.

[] Claude Draude, Christian Gruhl, Gerrit Hornung, Jonathan Kropf, Jörn Lamla, Jan Marco Leimeister, Bernhard Sick, Gerd Stumme, Social Machines, in: Informatik Spektrum, https://doi.org/10.1007/s00287-021-01421-4

[] EU: High-Level Expert Group on AI (AI HLEG), A definition of AI: Main capabilities and scientific disciplines, European Commission communications published on 25 April 2018 (COM(2018) 237 final), 7 December 2018 (COM(2018) 795 final) and 8 April 2019 (COM(2019) 168 final). For our definition of Artificial Intelligence (AI), please refer to our document published on 8 April 2019: https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=56341

[] EU: High-Level Expert Group on AI (AI HLEG), Policy and investment recommendations for trustworthy Artificial Intelligence, 2019, https://digital-strategy.ec.europa.eu/en/library/policy-and-investment-recommendations-trustworthy-artificial-intelligence

[] European Union. Regulation 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC General Data Protection Regulation; http://eur-lex.europa.eu/eli/reg/2016/679/oj (Wirksam ab 25.Mai 2018) [26.2.2022]

[] C.S. Holling. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4(1):1–23, 1973

[] John P. van Gigch. 1991. System Design Modeling and Metamodeling. Springer US. DOI:https://doi.org/10.1007/978-1-4899-0676-2

[] Gudwin, R.R. (2002), Semiotic Synthesis and Semionic Networks, S.E.E.D. Journal (Semiotics, Energy, Evolution, Development), Volume 2, No.2, pp.55-83.

[] Gudwin, R.R. (2003), On a Computational Model of the Peircean Semiosis, IEEE KIMAS 2003 Proceedings

[] J.A. Jacko and A. Sears, Eds., The Human-Computer Interaction Handbook. Fundamentals, Evolving Technologies, and emerging Applications. 1st edition, 2003.

[] LeCun, Y., Bengio, Y., & Hinton, G. Deep learning. Nature 521, 436-444 (2015).

[] Lenat, D. What AI can learn from Romeo & Juliet.Forbes (2019)

[] Pierre Lévy, Collective Intelligence. mankind’s emerging world in cyberspace, Perseus books, Cambridge (M A), 1997 (translated from the French Edition 1994 by Robert Bonnono)

[] Lexikon der Nachhaltigkeit, ‘Starke Nachhaltigkeit‘, https://www.nachhaltigkeit.info/artikel/schwache_vs_starke_nachhaltigkeit_1687.htm (acessed: July 21, 2022)

[] Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven Sloman, Shannon Vallor, and Toby Walsh. “Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report.” Stanford University, Stanford, CA, September 2021. Doc: http://ai100.stanford.edu/2021-report.

[] Markus Luczak-Roesch, Kieron O’Hara, Ramine Tinati, Nigel Shadbolt, Socio-technical Computation, CSCW’15 Companion, March 14–18, 2015, Vancouver, BC, Canada, ACM 978-1-4503-2946-0/15/03, http://dx.doi.org/10.1145/2685553.2698991

[] Marcus, G.F., et al. Overregularization in language acquisition. Monographs of the Society for Research in Child Development 57 (1998).

[] Gary Marcus and Ernest Davis, Rebooting AI, Published by Pantheon,
Sep 10, 2019, 288 Pages

[] Gary Marcus, Deep Learning Is Hitting a Wall. What would it take for artificial intelligence to make real progress, March 10, 2022, URL: https://nautil.us/deep-learning-is-hitting-a-wall-14467/ (accessed: July 20, 2022)

[] Kathryn Merrick. Value systems for developmental cognitive robotics: A survey. Cognitive Systems Research, 41:38 – 55, 2017

[]  Illah Reza Nourbakhsh and Jennifer Keating, AI and Humanity, MIT Press, 2020 /* An examination of the implications for society of rapidly advancing artificial intelligence systems, combining a humanities perspective with technical analysis; includes exercises and discussion questions. */

[] Olazaran, M. , A sociological history of the neural network controversy. Advances in Computers 37, 335-425 (1993).

[] Friedrich August Hayek (1945), The use of knowledge in society. The American Economic Review 35, 4 (1945), 519–530

[] Karl Popper, „A World of Propensities“, in: Karl Popper, „A World of Propensities“, Thoemmes Press, Bristol, (Vortrag 1988, leicht erweitert neu abgedruckt 1990, repr. 1995)

[] Karl Popper, „Towards an Evolutionary Theory of Knowledge“, in: Karl Popper, „A World of Propensities“, Thoemmes Press, Bristol, (Vortrag 1989, ab gedruckt in 1990, repr. 1995)

[] Karl Popper, „All Life is Problem Solving“, Artikel, ursprünglich ein Vortrag 1991 auf Deutsch, erstmalig publiziert in dem Buch (auf Deutsch) „Alles Leben ist Problemlösen“ (1994), dann in dem Buch (auf Englisch) „All Life is Problem Solving“, 1999, Routledge, Taylor & Francis Group, London – New York

[] Rittel, Horst W.J.; Webber, Melvin M. (1973). “Dilemmas in a General Theory of Planning” (PDF). Policy Sciences. 4 (2): 155–169. doi:10.1007/bf01405730S2CID 18634229. Archived from the original (PDF) on 30 September 2007. [Reprinted in Cross, N., ed. (1984). Developments in Design Methodology. Chichester, England: John Wiley & Sons. pp. 135–144.]

[] Ritchey, Tom (2013) [2005]. “Wicked Problems: Modelling Social Messes with Morphological Analysis”Acta Morphologica Generalis2 (1). ISSN 2001-2241. Retrieved 7 October 2017.

[] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th US ed., 2021, URL: http://aima.cs.berkeley.edu/index.html (accessed: July 20, 2022)

[] A. Sears and J.A. Jacko, Eds., The Human-Computer Interaction Handbook. Fundamentals, Evolving Technologies, and emerging Applications. 2nd edition, 2008.

[] Skaburskis, Andrejs (19 December 2008). “The origin of “wicked problems””. Planning Theory & Practice9 (2): 277-280. doi:10.1080/14649350802041654. At the end of Rittel’s presentation, West Churchman responded with that pensive but expressive movement of voice that some may well remember, ‘Hmm, those sound like “wicked problems.”‘

[] Tonkinwise, Cameron (4 April 2015). “Design for Transitions – from and to what?”Academia.edu. Retrieved 9 November 2017.

[] Thoppilan, R., et al. LaMDA: Language models for dialog applications. arXiv 2201.08239 (2022).

[] Wurm, Daniel; Zielinski, Oliver; Lübben, Neeske; Jansen, Maike; Ramesohl,
Stephan (2021) : Wege in eine ökologische Machine Economy: Wir brauchen eine ‘Grüne Governance der Machine Economy’, um das Zusammenspiel von Internet of Things, Künstlicher Intelligenz und Distributed Ledger Technology ökologisch zu gestalten, Wuppertal Report, No. 22, Wuppertal Institut für Klima, Umwelt, Energie, Wuppertal, https://doi.org/10.48506/opus-7828

[] Aimee van Wynsberghe, Sustainable AI: AI for sustainability and the sustainability of AI, in: AI and Ethics (2021) 1:213–218, see: https://doi.org/10.1007/s43681

[-] Sarah West, Rachel Pateman, 2017, “How could citizen science support the Sustainable Development Goals?“, SEI Stockholm Environment Institut , 2017, see: https://mediamanager.sei.org/documents/Publications/SEI-2017-PB-citizen-science-sdgs.pdf

[] R. I. Damper (2000), Editorial for the special issue on ‘Emergent Properties of Complex Systems’: Emergence and levels of abstraction. International Journal of Systems Science 31, 7 (2000), 811–818. DOI:https://doi.org/10.1080/002077200406543

[] Gerd Doeben-Henisch (2004), The Planet Earth Simulator Project – A Case Study in Computational Semiotics, IEEE AFRICON 2004, pp.417 – 422

[] Boder, A. (2006), “Collective intelligence: a keystone in knowledge management”, Journal of Knowledge Management, Vol. 10 No. 1, pp. 81-93. https://doi.org/10.1108/13673270610650120

[] Wikipedia, ‘Weak and strong sustainability’, https://en.wikipedia.org/wiki/Weak_and_strong_sustainability (accessed: July 21, 2022)

[] Florence Maraninchi, Let us Not Put All Our Eggs in One Basket. Towards new research directions in computer Science, CACM Communications of the ACM, September 2022, Vol.65, No.9, pp.35-37, https://dl.acm.org/doi/10.1145/3528088

[] AYA H. KIMURA and ABBY KINCHY, “Citizen Science: Probing the Virtues and Contexts of Participatory Research”, Engaging Science, Technology, and Society 2 (2016), 331-361, DOI:10.17351/ests2016.099

[] Eric Bonabeau (2009), Decisions 2.0: The power of collective intelligence. MIT Sloan Management Review 50, 2 (Winter 2009), 45-52.

[] Jim Giles (2005), Internet encyclopaedias go head to head. Nature 438, 7070 (Dec. 2005), 900–901. DOI:https://doi.org/10.1038/438900a

[] T. Bosse, C. M. Jonker, M. C. Schut, and J. Treur (2006), Collective representational content for shared extended mind. Cognitive Systems Research 7, 2-3 (2006), pp.151-174, DOI:https://doi.org/10.1016/j.cogsys.2005.11.007

[] Romina Cachia, Ramón Compañó, and Olivier Da Costa (2007), Grasping the potential of online social networks for foresight. Technological Forecasting and Social Change 74, 8 (2007), oo.1179-1203. DOI:https://doi.org/10.1016/j.techfore.2007.05.006

[] Tom Gruber (2008), Collective knowledge systems: Where the social web meets the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web 6, 1 (2008), 4–13. DOI:https://doi.org/10.1016/j.websem.2007.11.011

[] Luca Iandoli, Mark Klein, and Giuseppe Zollo (2009), Enabling on-line deliberation and collective decision-making through large-scale argumentation. International Journal of Decision Support System Technology 1, 1 (Jan. 2009), 69–92. DOI:https://doi.org/10.4018/jdsst.2009010105

[] Shuangling Luo, Haoxiang Xia, Taketoshi Yoshida, and Zhongtuo Wang (2009), Toward collective intelligence of online communities: A primitive conceptual model. Journal of Systems Science and Systems Engineering 18, 2 (01 June 2009), 203–221. DOI:https://doi.org/10.1007/s11518-009-5095-0

[] Dawn G. Gregg (2010), Designing for collective intelligence. Communications of the ACM 53, 4 (April 2010), 134–138. DOI:https://doi.org/10.1145/1721654.1721691

[] Rolf Pfeifer, Jan Henrik Sieg, Thierry Bücheler, and Rudolf Marcel Füchslin. 2010. Crowdsourcing, open innovation and collective intelligence in the scientific method: A research agenda and operational framework. (2010). DOI:https://doi.org/10.21256/zhaw-4094

[] Martijn C. Schut. 2010. On model design for simulation of collective intelligence. Information Sciences 180, 1 (2010), 132–155. DOI:https://doi.org/10.1016/j.ins.2009.08.006 Special Issue on Collective Intelligence

[] Dimitrios J. Vergados, Ioanna Lykourentzou, and Epaminondas Kapetanios (2010), A resource allocation framework for collective intelligence system engineering. In Proceedings of the International Conference on Management of Emergent Digital EcoSystems (MEDES’10). ACM, New York, NY, 182–188. DOI:https://doi.org/10.1145/1936254.1936285

[] Anita Williams Woolley, Christopher F. Chabris, Alex Pentland, Nada Hashmi, and Thomas W. Malone (2010), Evidence for a collective intelligence factor in the performance of human groups. Science 330, 6004 (2010), 686–688. DOI:https://doi.org/10.1126/science.1193147

[] Michael A. Woodley and Edward Bell (2011), Is collective intelligence (mostly) the General Factor of Personality? A comment on Woolley, Chabris, Pentland, Hashmi and Malone (2010). Intelligence 39, 2 (2011), 79–81. DOI:https://doi.org/10.1016/j.intell.2011.01.004

[] Joshua Introne, Robert Laubacher, Gary Olson, and Thomas Malone (2011), The climate CoLab: Large scale model-based collaborative planning. In Proceedings of the 2011 International Conference on Collaboration Technologies and Systems (CTS’11). 40–47. DOI:https://doi.org/10.1109/CTS.2011.5928663

[] Miguel de Castro Neto and Ana Espírtio Santo (2012), Emerging collective intelligence business models. In MCIS 2012 Proceedings. Mediterranean Conference on Information Systems. https://aisel.aisnet.org/mcis2012/14

[] Peng Liu, Zhizhong Li (2012), Task complexity: A review and conceptualization framework, International Journal of Industrial Ergonomics 42 (2012), pp. 553 – 568

[] Sean Wise, Robert A. Paton, and Thomas Gegenhuber. (2012), Value co-creation through collective intelligence in the public sector: A review of US and European initiatives. VINE 42, 2 (2012), 251–276. DOI:https://doi.org/10.1108/03055721211227273

[] Antonietta Grasso and Gregorio Convertino (2012), Collective intelligence in organizations: Tools and studies. Computer Supported Cooperative Work (CSCW) 21, 4 (01 Oct 2012), 357–369. DOI:https://doi.org/10.1007/s10606-012-9165-3

[] Sandro Georgi and Reinhard Jung (2012), Collective intelligence model: How to describe collective intelligence. In Advances in Intelligent and Soft Computing. Vol. 113. Springer, 53–64. DOI:https://doi.org/10.1007/978-3-642-25321-8_5

[] H. Santos, L. Ayres, C. Caminha, and V. Furtado (2012), Open government and citizen participation in law enforcement via crowd mapping. IEEE Intelligent Systems 27 (2012), 63–69. DOI:https://doi.org/10.1109/MIS.2012.80

[] Jörg Schatzmann & René Schäfer & Frederik Eichelbaum (2013), Foresight 2.0 – Definition, overview & evaluation, Eur J Futures Res (2013) 1:15
DOI 10.1007/s40309-013-0015-4

[] Sylvia Ann Hewlett, Melinda Marshall, and Laura Sherbin (2013), How diversity can drive innovation. Harvard Business Review 91, 12 (2013), 30–30

[] Tony Diggle (2013), Water: How collective intelligence initiatives can address this challenge. Foresight 15, 5 (2013), 342–353. DOI:https://doi.org/10.1108/FS-05-2012-0032

[] Hélène Landemore and Jon Elster. 2012. Collective Wisdom: Principles and Mechanisms. Cambridge University Press. DOI:https://doi.org/10.1017/CBO9780511846427

[] Jerome C. Glenn (2013), Collective intelligence and an application by the millennium project. World Futures Review 5, 3 (2013), 235–243. DOI:https://doi.org/10.1177/1946756713497331

[] Detlef Schoder, Peter A. Gloor, and Panagiotis Takis Metaxas (2013), Social media and collective intelligence—Ongoing and future research streams. KI – Künstliche Intelligenz 27, 1 (1 Feb. 2013), 9–15. DOI:https://doi.org/10.1007/s13218-012-0228-x

[] V. Singh, G. Singh, and S. Pande (2013), Emergence, self-organization and collective intelligence—Modeling the dynamics of complex collectives in social and organizational settings. In 2013 UKSim 15th International Conference on Computer Modelling and Simulation. 182–189. DOI:https://doi.org/10.1109/UKSim.2013.77

[] A. Kornrumpf and U. Baumöl (2014), A design science approach to collective intelligence systems. In 2014 47th Hawaii International Conference on System Sciences. 361–370. DOI:https://doi.org/10.1109/HICSS.2014.53

[] Michael A. Peters and Richard Heraud. 2015. Toward a political theory of social innovation: Collective intelligence and the co-creation of social goods. 3, 3 (2015), 7–23. https://researchcommons.waikato.ac.nz/handle/10289/9569

[] Juho Salminen. 2015. The Role of Collective Intelligence in Crowdsourcing Innovation. PhD dissertation. Lappeenranta University of Technology

[] Aelita Skarzauskiene and Monika Maciuliene (2015), Modelling the index of collective intelligence in online community projects. In International Conference on Cyber Warfare and Security. Academic Conferences International Limited, 313

[] AYA H. KIMURA and ABBY KINCHY (2016), Citizen Science: Probing the Virtues and Contexts of Participatory Research, Engaging Science, Technology, and Society 2 (2016), 331-361, DOI:10.17351/ests2016.099

[] Philip Tetlow, Dinesh Garg, Leigh Chase, Mark Mattingley-Scott, Nicholas Bronn, Kugendran Naidoo†, Emil Reinert (2022), Towards a Semantic Information Theory (Introducing Quantum Corollas), arXiv:2201.05478v1 [cs.IT] 14 Jan 2022, 28 pages



Is Mathematics a Fake? No! Discussing N.Bourbaki, Theory of Sets (1968) – Introduction

eJournal: uffmm.org, ISSN 2567-6458,
6.June 2022 – 13.June 2022, 10:30h
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

SCOPE

In the uffmm review section the different papers and books are discussed from the point of view of the oksimo paradigm, which is embedded in the general view of a generalized ‘citizen science’ as a ‘computer aided sustainable applied empirical theory’ (CSAET). In the following text the author discusses the introduction of the book “Theory of Sets” from the series “Elements of Mathematics” by N.Bourbaki (1968) [1b]

CONTEXT

In the foundational post with the title “From SYSTEMS Engineering to THEORY Engineering” [3] the assumptions of the whole formalization approach in logic, mathematics and science are questioned as to narrow to allow a modern sustainable theory of science dealing explicitly with the future. To sharpen some of the arguments in that post it seems to be helpful to discuss one of the cornerstones of modern (formalized) mathematics substantiated in the book ‘Theory of sets’ from the Bourbaki group.[1a] It has to be mentioned that the question of the insufficiency of formalization has been discussed in the uffmm blog in several posts before. (cf. e.g. [2])

Formalization

preface

In the introduction to the ‘Set Theory Book’ the bourbaki group reveals a little bit of their meta-mathematical point of view, which finally belongs to the perspective of philosophy. At the one hand they try to be ‘radically formal’, but doing this they notice themselves that this is — by several reasons — only a ‘regulative idea’, somehow important for our thinking, but not completely feasible. This ‘practical impossibility’ is not necessarily a problem as long as one is conscious about this. The Bourbaki group is conscious about this problem, but different to their ‘rigor’ with the specialized formalization of mathematical ideas, they leave it widely ‘undefined’ what follows from the practical impossibility of being ‘completely rigorous’. In the following text it will be tried to describe the Bourbaki position with both dimensions: the idea of ‘formalization’ and the reality of ‘non-formalized realities’ which give the ‘ground’ for everything, even for the formalization. Doing this it will — hopefully — become clear that the idea of formalization was a great achievement in the philosophical and scientific thinking but it did not really solve our problems of understanding the world. The most important aspects of knowledge are ‘outside’ of this formalization approach, and many ‘problems’ which seem to bother our actual thinking are perhaps only ‘artifacts’ of this simplified formalization approach (somehow similar to the problems which have been induced by the metaphysical thinking of the older philosophy). To say it flatly: to introduce new names for old problems does not necessarily solve problems. It enables new ways of speaking and perhaps some new kinds of knowledge, but it does not really solve the big problems of knowledge. And the biggest problem of knowledge is — perhaps — the primary ‘knowledge machine’ itself: the biological actors which have brains to transform ‘reality’ in ‘virtual models’ in their brains and communication tools to ‘communicate’ these virtual models to enable a ‘collective intelligence’ as well as a ‘collective cooperation’. As long as we do not understand this we do not really understand the ‘process of knowing’.

before formalization

With the advent of the homo sapiens population on the planet earth about 300.000 years ago [4] it became possible that biological systems could transform their perceptions of the reality around their brains into ‘internal’, ‘virtual’ models, which enabled ‘reference points’ for ‘acting’ and a ‘cooperation’ which was synchronized by a ‘symbolic communication’. Those properties of the internal virtual models which have no clear ‘correspondence’ to the ‘reality between the brains’ are difficult to communicate.

Everyday symbolic communication refers to parts of the reality by certain types of expressions, which are ‘combined’ in manners which encode different types of ‘relations’ or even ‘changes’. Expressions which ‘refer’ to ‘concrete’ properties can be ‘overloaded’ by expressions which refer to other expressions, which in turn refer either again to expressions or to ‘concrete meanings’. Those objects which are the targets of a referring relation — concrete objects or other expressions — are here called ‘the meaning’ of the expressions. Thus the ‘meaning space’ is populated by either expressions related to ‘concrete’ properties or by ‘expressions pointing forward’ to other expressions and these ‘pointing-forward’ expressions are here called ‘abstract meaning’. While concrete meanings are usually ‘decidable’ in the everyday world situations as being ‘given’ (being ‘true’) or as ‘not being given’ (‘being false’), abstract meanings are as expressions ‘undefined’: they can lead to some concrete property which in turn perhaps can be decided or not.

The availability of ‘abstract expressions’ in ordinary language can be seen as a ‘problem’ or as a ‘blessing’. Being able to generate and use abstract terms manifests a great flexibility in talking — and thinking! — about possible realities which allow to overcome the dictatorship of the ‘now’ and the ‘individual single’. Without abstraction thinking would indeed be impossible. Thus if one understands that ‘thinking’ is a real process with sequences of different states which reveal eventually more abstract classes, structures, and changes, then abstraction is the ‘opener’ for more reality, the ‘enabler’ for a more broader and flexible knowledge. Only by ‘transcending’ the eternal ‘Now’ we get an access to phenomena like time, changes, all kinds of dynamics, and only thereby are ‘pictures of some possible future’ feasible!

Clearly, the potential of abstraction can also be a source of ‘non-real’ ideas, of ‘fantastic’ pictures, of ‘fake news’ and the like.

But these possible ‘failures’ — if they will be ‘recognized’ as failures! — are inevitable if one wants to dig out some ‘truth’ in the nearly infinite space of the unknown. Before the ‘knowledge that something is true’ one has to master a ‘path of trial and error’ consuming ‘time’ and ‘resources’.

This process of creating new abstract ideas to guide a search in the space of the unknown is the only key to find besides ‘errors’ sometimes some ‘truth’.

Thus the ‘problem’ with abstract ideas is an unavoidable condition to find necessary ‘truths’. Stepping back in the face of possible problems is no option to survive in the unknown future.

the formal view of the world according to bourbaki

Figure 1: Graphical interpretation of N.Bourbaki, Set Theory (1968), Introduction, ‘liberal version’
Language, object language, meta language

Talking about mathematical objects with their properties within an ordinary language is not simple because the expressions of an ordinary language are as such usually part of a network of meanings, which can overlap, which can be fuzzy, which are giving space for many interpretations. Additionally, that which is called a ‘mathematical object’ is not a kind of an object wich is given in the everyday world experience. What can be done in such a situation?

Bourbaki proposes to introduce a ‘specialized language’ constructed out of a finite set of elements constituting the ‘alphabet’ of a new language, together with ‘syntactical rules’, which describe how to construct with the elements of the alphabet chains of elements called ‘(well formed) expressions’, which constitute the ‘language’ LO, which shall be used to talk about mathematical objects.

But because mathematics is not restricted to ‘static objects’ but deals also with ‘transformations’ (‘changes’) of objects, one needs ‘successions of objects’ (‘sequences’), which are related by ‘operations with mathematical objects’. In this case the operations are also represented by ‘expressions’ but these expressions are expressions of a ‘higher order’ which have as referenced subject those expressions which are representing objects . Thus, Bourbaki needs right from the beginning two languages: an ‘object language’ (expressions of a language LO representing mathematical objects) and a ‘meta language’ LL (expressions referring to expressions of the object language LO including certain ‘types of changes’ occurring with the object language expressions). Thus a mathematical language Lm consists in the combination of an object language LO with a meta language LL (Lm = (LO,LL)).

And, what becomes clear by this procedure, to introduce such a kind of mathematical language Lm one needs another language talking about the mathematical language Lm, and this is either the everyday (normal) language L, which is assumed to be a language which everybody can ‘understand’ and ‘apply correctly’, or it is a third specialized language LLL, which can talk with special expressions about the mathematical language Lm. Independent of the decision which solution one prefers, finally the ordinary language L will become the meta language for all other thinkable meta languages.

Translating(?) math objects into formal expressions

If the formalized expressions of the mathematical language (Lm = (LO,LL)) would be the mathematical objects themselves, then mathematics would consist only of those expressions. And, because there would be no other criteria available, whatever expressions one would introduce, every expression would claim to be a relevant mathematical expression. This situation would be a ‘maximum of non-sense’ construct: nothing could be ‘false’.

Thus, the introduction of formal expressions of some language alone seems to be not enough to establish a language which is called a ‘mathematical’ language Lm different from other languages which talk about other kinds of objects. But what could it be which relates to ‘specific math objects’ which are not yet the expressions used to ‘refer’ to these specific math objects?

Everybody knows that the main reason for to ‘speak’ (or ‘write’) about math specific objects are humans which are claiming to be ‘mathematicians’ and which are claiming to have some ‘knowledge’ about specific objects called ‘math objects’ which are the ‘content’ which they ‘translate’ into the expressions of a certain language call ‘mathematical language’.[5] Thus, if the ‘math objects’ are not the used expressions themselves then these ‘math objects’ have to be located ‘inside of these talking humans’. According to modern science one would specify this ‘inside’ as ‘brain’, which is connected in complex ways to a body which in turn is connected to the ‘outside world of the body’. Until today it is not possible to ‘observe’ directly math objects assumed to be in the brain of the body of someone which claims to be a mathematician. Thus one mathematician A can not decide what another mathematician B has ‘available in his brain’ at some point of time.

Bourbaki is using some formulations in his introduction which gives some ‘flavor’ of this ‘being not able to translate it into a formalized mathematical language’. Thus at one position in the text Bourbaki is recurring to the “common sense” of the mathematicians [6] or to the “reader’s intuition”. [7] Other phrases refer to the “modes of reasoning” which cannot be formalized [8], or simply to the “experience” on which “the opinion rests”. [9] Expressions like ‘common sense’, ‘intuition’, ‘modes of reasoning’, and ‘experience’ are difficult to interpret. All these expressions describe something ‘inside’ the brains which cannot be observed directly. Thus, how can mathematician A know what mathematician B ‘means’ if he is uttering some statement or writes it down? Does it make a difference whether a mathematician is a man or a woman or is belonging to some other kind of a ‘gender’? Does it make a difference which ‘age’ the mathematician has? How ‘big’ he is? Which ‘weight’ he has?

Thus, from a philosophical point of view the question to the specific criteria which classify a language as a ‘mathematical language’ and not some other language leads us into a completely unsatisfying situation: there are no ‘hard facts’ which can give us a hint what ‘mathematical objects’ could be. What did we ‘overlook’ here? What is the key to the specific mathematical objects which inspired the brains of many many thousand people through centuries and centuries? Is mathematics a ‘big fake’ or is there more than this?

A mathematician as an ‘actor’?
Figure 2: Graphical interpretation of N.Bourbaki, Set Theory (1968), Introduction, ‘Actor view’

This last question “Is mathematics a ‘big fake’ or is there more than this?” can lead o the assumption, that it is not enough to talk about ‘mathematics’ by not including the mathematician itself. Only the mathematician is that ‘mysterious source’ of knowledge, which seems to trigger the production of ‘mathematical expressions’ in speaking or writing (or drawing). Thus a meta-mathematical — and thereby philosophical’ — ‘description’ of mathematics should have at least the ‘components’ (MA, LO,LL,L) with ‘MA’ as abbreviation for the set of actors where each element of the set MA is a mathematician, and — this is decisive ! — it is this math actor MA which is in possession of those ‘criteria’ which decide whether an expression E belongs the ‘mathematical language Lm‘ or not.

The phrase of the ‘mathematician’ as a ‘mysterious source of knowledge’ is justified by an ’empirical observational point of view’: nobody can directly look into the brain of a mathematician. Thus the question of what an expression called ‘mathematical expression’ can ‘mean’ is in such an empirical view not decidable and appears to be a ‘mystery’.

But in the reality of everyday life we can observe, that every human actor — not only mathematicians — seems to be able to use expressions of the everyday language with referring to ‘internal states’ of the brain in a way which seems to ‘work’. If we are talking about ‘pain with my teeth’ or about ‘being hungry or thirsty’ or ‘having an idea’ etc. then usually other human actors seem to ‘understand’ what one is uttering. The ‘evidence’ of a ‘working understanding’ is growing up by the ‘confirmation of expectations’: if oneself is hungry, then one has a certain kind of ‘feeling’ and usually this feeling leads — depending from the cultural patterns one is living in — to a certain kind of ‘behavior’, which has — usually — the ‘felt effect’ of being again ‘not hungry’. This functional relation of ‘feeling to be hungry’, ‘behavior of consuming something’, ‘feeling of being not hungry again’ is an association between ‘bodily functions’ common to all human actors and additionally it is a certain kind of observable behavior, which is common to all human actors too. And it seems to work that human actors are able to associate ‘common internal states’ with ‘common observable behavior’ and associate this observable behavior with the ‘presupposed internal states’ with certain kinds of ‘expressions’. Thus although the internal states are directly not observable, they can become ‘communicated by expressions’ because these internal states are — usually — ‘common to the internal experience of every human actor’.

From this follows the ‘assumption’ that we should extend the necessary elements for ‘inter-actor communication’ with the factor of ‘common human actor HA experience’ abbreviated as ‘HAX‘ (HA, HAX, MA, LO,LL,L), which is usually accompanied by certain kinds of observable behavior ‘BX‘, which can be used as point of reference for certain expressions ‘LX‘, which ‘point to’ the associated intern al states HAX, which are not directly observable. This yields the structure (HA, HAX, MA, BX, LO,LL,LX,L).

Having reached this state of assumptions, there arises an other assumption regarding the relationship between ‘expressions of a language’ — like (LO,LL,LX,L) — and those properties which are constituting the ‘meaning’ of these expressions. In this context ‘meaning’ is not a kind of an ‘object’ but a ‘relation’ between two different things, the expressions at one side and the properties ‘referred to’ on the other side. Moreover this ‘meaning relation’ seems not to be a ‘static’ relation but a ‘dynamic’ one, associating two different kinds of properties one to another. This reminds to that what mathematicians call a ‘mapping, a function’, and the engineers a ‘process, an operation’. If we abbreviate this ‘dynamic meaning relation’ with the sign ‘μ’, then we could agree to the convention ‘μX : LX <—> (BX,HAX)’ saying that there exists a meaning function μX which maps the special expressions of LX to the special internal experience HAX, which in turn is associated with the special behavior BX. Thus, we extend our hypothetical structure to the format (HA, HAX, MA, BX, LO,LL,LX,L,μX).

With these assumptions we are getting a first idea how human actors in general can communicate about internal, directly not observable states, with other human actors by using external language expressions. We have to notice that the assumed dynamic meaning relation μX itself has to be located ‘inside’ the body, inside’ the brain. This triggers the further assumption to have ‘internal counterparts’ of the external observable behavior as well as external expressions. From this follows the further assumption that there must exists some ‘translation/ transformation’ ‘τ’ which ‘maps’ the internal ‘counterparts’ of the observable behavior and the observable expressions into the external behavior.(cf. figure 2) Thus, we are reaching the further extended format: (HA, HAX, MA, BX, LO,LL,LX,L,μX,τ).

Mathematical objects

Accepting the basic assumptions about an internal meaning function μX as well an internal translation function τ narrows the space of possible answers about the nature of ‘math objects’ a little bit, but as such this is possibly not yet a satisfying answer. Or, have we nevertheless yet to assume that ‘math objects’ and related ‘modes of reasoning’ are also rooted in internal properties and dynamics of the brain which are ‘common to all humans’?

If one sees that every aspect of the human world view is encoded in some internal states of the brain, and that what we call ‘world’ is only given as a constructed virtual structure in the brains of bodies including all the different kinds of ‘memories’, then there is no real alternative to the assumption that ‘math objects’ and related ‘modes of reasoning’ have to be located in these — yet not completely decoded — inner structures and dynamics of the brain.

From the everyday experience — additionally enlightened by different scientific disciplines, e.g. experimental (neuro) psychology — we know that the brain is — completely automatic — producing all kinds of ‘abstractions’ from concrete ‘perceptions’, can produce any kinds of ‘abstractions of abstractions’, can ‘associate’ abstractions with other abstractions, can arrange many different kinds of memories to ‘arrangements’ representing ‘states’/ ‘situations’, ‘transformations of states’, ‘sequences of states’ and the like. Thus everything which a ‘mathematical reasoning’ HAm needs seems to be available as concrete brain state or brain activity, and this is not only ‘special’ for an individual person alone, it is the common structure of all brains.

Therefore one has to assume that the set of mathematicians MA is a ‘subset’ of the set of human actors HA in general. From this one can further assume that the ‘mathematical reasoning’ HAm is a subset of the general human everyday experience HAX. And, saying this, the meaning function μX as well as the translation function τ should be applicable also to the mathematical reasoning and the mathematical objects as well: (HA, MA, HAX, HAm, BX, LO,LL,LX,L,μX,τ).

These assumptions would explain why it is not only possible but ‘inevitable’ to use the everyday language L to introduce and to use a mathematical language Lm with different kinds of sub-languages (LO,LL, LLL, …). Thus in analogy to the ‘feeling’ ‘to be hungry’ with a cultural encoded kind of accompanying behavior BX we have to assume that the different kinds of internal states and transformations in the case of mathematical reasoning can be associated with an observable kind of ‘behavior’ by using ‘expressions’ embedded (encoded) in certain kinds of ‘behavior’ accepted as ‘typical mathematical’. Introducing expressions like ‘0’, ‘1’, ‘2’, …, ’10’, … (belonging to a language Lo) for certain kinds of ‘objects’ and expressions like ‘+’, ‘-‘ … for certain kinds of operations with these before introduced objects (belonging to a language LL) one can construct combined expressions like ‘1+2=3’ (belonging to a mathematical language Lm). To introduce ‘more abstract objects’ like ‘sets’, ‘relations’, ‘functions’ etc. which have no direct counterpart in the everyday world does not break the assumptions. The everyday language L operates already only with abstract objects like ‘cup’, ‘dog’, ‘house’ etc. The expression ‘cup’ is an abstract concept, which can easily be associated with any kind of concrete phenomena provided by perceptions introducing ‘sets of different properties’, which allow the construction of ‘subsets of properties’ constituting a kind of ‘signature’ for a certain abstract ‘class’ which only exists in the dynamics of the brain. Thus having a set C named ‘cup’ introduces ‘possible elements’, whose ‘interpretation’ can be realized by associating different kinds of sets of properties provided by ‘sensory perception’. But the ‘memory’ as well as the ‘thinking’ can also provide other kinds of properties which can be used too to construct other ‘classes’.

In this outlined perspective of brain dynamics mathematics appears to be a science which is using these brain dynamics in a most direct way without to recur to the the huge amount of everyday experiences. Thus, mathematical languages (today summarized in that language, which enables the so called ‘set theory’) and mathematical thinking in general seems to reflect the basic machinery of the brain processing directly across all different cultures. Engineers worldwide speaking hundreds of different ‘everyday languages’ can work together by using mathematics as their ‘common language’ because this is their ‘common thinking’ underlying all those different everyday languages.

Being a human means being able to think mathematically … besides many other things which characterizes a human actor.

Epiloge

Basically every ordinary language offers all elements which are necessary for mathematics (mathematics is the kernel of every language). But history illustrates that it can be helpful to ‘extend’ an ordinary language with additional expressions (Lo, LL, LLL, …). But the development of modern mathematics and especially computer science shows increasing difficulties by ‘cutting away’ everyday experience in case of elaborating structures, models, theories in a purely formal manner, and to use these formal structures afterwards to interpret the everyday world. This separates the formal productions from the main part of users, of citizens, leading into a situation where only a few specialist have some ‘understanding’ (usually only partially because the formalization goes beyond the individual capacity of understanding), but all others have no more an understanding at all. This has he flavor of a ‘cultural self-destruction’.

In the mentioned oksimo software as part of a new paradigm of a more advanced citizen science this problem of ‘cultural self-destruction’ is avoided because in the format of a ‘computer aided sustainable applied empirical theory (CSAET) the basic language for investigating possible futures is the ordinary language which can be extend as needed with quantifying properties. The computer is not any more needed for ‘understanding’ but only for ‘supporting’ the ‘usage’ of everyday language expressions. This enables the best of both worlds: human thinking as well as machine operations.

COMMENTS

Def: wkp := Wikipedia; en := English

[1a] Bourbaki Group, see: https://en.wikipedia.org/wiki/Nicolas_Bourbaki

[1b] Theory of Sets with a chapter about structures, see: https://en.wikipedia.org/wiki/%C3%89l%C3%A9ments_de_math%C3%A9matique

[2] Gerd Doeben-Henisch, “Extended Concept for Meaning Based Inferences, Version 1”, August 2020, see: https://www.uffmm.org/wp-content/uploads/2020/08/TruthTheoryExtended-v1.pdf

[3] Gerd Doeben-Henisch, “From SYSTEMS Engineering to THEORY Engineering”, June 2022, see: https://www.uffmm.org/2022/05/26/from-systems-engineering-to-theory-engineering/

[4] Humans, How many years ago?, see: wkp (en): https://en.wikipedia.org/wiki/Human#:~:text=Homo%20sapiens,-Linnaeus%2C%201758&text=Anatomically%20modern%20humans%20emerged%20around,local%20populations%20of%20archaic%20humans.

[5] The difference between ‘talking’ about math objects and ‘writing’ is usually not thematised in the philosophy of mathematics. Because the ordinary language is the most general ‘meta language’ for all kinds of specialized languages and the primary source of the ordinary language then ‘speaking’ is perhaps not really a ‘trivial’ aspect in understanding the ‘meaning’ of any kind of language.

[6] “But formalized mathematics cannot in practice be written down in full, and therefore we must have confidence in what might be called the common sense of the mathematician …”. (p.11)

[7] “Sometimes we shall use ordinary language more loosely … and by indications which cannot be translated into formalized language and which are designed to help the reader to reconstruct the whole text. Other passages, equally untranslatable into formalized language, are introduced in order to clarify the ideas involved, if necessary by appealing to the reader’s intuition …”(p.11)

[8] “It may happen at some future date that mathematicians will agree to use modes of reasoning which cannot be formalized in the language described here : it would then be necessary, if not to change the language completely, at least to enlarge its rules of syntax.”(p.9)

[9] “To sum up, we believe that mathematics is destined to survive … but we cannot pretend that this opinion rests on anything more than experience.”(p.13)

OKSIMO and BOURBAKI. A Metamathematical Perspective on Oksimo. Part 1

eJournal: uffmm.org
ISSN 2567-6458, 22.Sept – 24.Sept  2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

(Some minor corrections: 23.Sept 2021)

(A substantial extension: 24.Sept.2021)

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 BOOK: THEORY OF SETS

Covered under the pseudonym of N.Bourbaki [1] appeared 1970 the French edition of a book which 1968 already had been translated into English  (reprinted 1970) called  Theory of Sets.[2] This book is the first book of a series about ELEMENTS OF MATHEMATICS.

To classify this book about set theory as a book of Metamathematics and as such as a book in the perspective of Philosophy of Science will become clear if one starts reading the book.[3]

MATHEMATICS WITH ONE LANGUAGE

It is the basic conviction of the Bourbaki book, that “… it is known to be possible … to derive practically the whole of known mathematics from a single source the Theory of Sets.” (p.9) And from this Bourbaki concludes, that it will be sufficient “… to describe the principles of a single formalized language, to indicate how the Thory of Sets could be written in this language, and then to show how the various branches of mathematics  … fit into this framework.”(p.9)

Thus, the content of mathematics — whatever it is — can according to Bourbaki be described in one single language [Lm] and the content will be called Theory of Sets [T] .

METAMATHEMATICS

Because the one single language Lm used to describe the Theory of Sets shall be a language with certain properties one has to define these properties with some other language, which is talking about Lm. As language for this job Bourbaki is using the ordinary language [Lo].(p.9) But the reasoning within which one is using this ordinary language is called metamathematics (cf. P.10f). Within the metamathematical point of view the language Lm under investigation is seen as a set of previously given objetcs without any kind of meaning, where only the assigned order is of importance.(cf. p.10): “… metamathematical ‘arguments’ usually assert that when a succession of operations has been performed on a text of a given type, then the final text will be of another given type.”(p.10)

What looks here at first glance  as the complete formalization of mathematics it is not. Bourbaki states clearly that “formalized mathematics cannot in practice be written down in full“(p.11) There has to be assumed as ‘last resort’ the assumption of a common sense of the mathematician and the intuition of the reader. (cf. p.11)

COGNITIVE-SEMIOTIC TURN

This conflict between at one hand of  the idea of a formalization of  Mathematics by a formalized language Lm  and on the other hand by the well known proof of Gödel [4] of the incompleteness of the axioms for classical arithmetic  (cf. p.12) is not a real conflict as long as one takes into account — as Bourbaki points out — that the ‘content of mathematics’ is only given in different layers of languages (Lm, Lo, …) which again are embedded in a presupposed ‘common sense’ which is nothing else as the cognitive machinery of human persons including an embedded meaning function relating different kinds of knowledge into different kinds of — internal as well as external — expressions of some language L. Thus any kind of a  ‘reduction of meaning’ seems never to be a ‘complete reduction’ but only a ‘technical reduction’ to introduce some ‘artificial (abstract) objetcs’ which can only work because of their embedding in some richer context.

This new perspective can be called the cognitive-semiotic turn which became possible by new insights of modern brain sciences in connection with pysychology and semiotics.

From this new point of view one can derive the idea of embedding metamathemics in a more advanced actor theory providing all the ingredients to make metamathematics more ‘rational’.

OUTLINE OF ACTOR THEORY

Actor theory first outline
Figure 1: Actor theory first outline

The details of the Actor Theory [AT] can become quite complex. Here a first outline of the basic ideas and what this can mean for a metamathematical point of view of mathematics.

World is not World

The main idea is founded in the new insights of Biology and Neuro-Psychology of the handling of body-world interactions as exercised by humans. One of the main insights is rooted back to von Uexküll [5] more than 100 years ago, when he described how every biological organism perceives and handles some world outside of the body  with the inner neuronal structures given! Thus different life forms in the same outside world  W will peceive and act neuronally in different worlds! Brain X acts in world X which is somehow related to the outside world W as well as Brain Y acts in world Y which also is  somehow related to the outside world W.

These basic insights relate as well to more developed life forms as such as  humans are. We as humans do not perceive and think the world W outside of our bodies ‘as it is’ but only as our brain inside our body can process all the body states related to the outside world in the mode of the inside brain. Thus if the different human individuals would have different brains they would live in different worlds and their would be no chance of a simple communication. But as we know from physiological and behavioral  studies humans can to some extend communicate successfully. Thus there exists inside of every human individual a human-processed world h(W) which is different from other life-forms like a rat, a worm, an octopus, etc.

From this basic insight it follows that if we speak about the world W we do indeed  not speak about the world  W directly but about the world W as it is processed in a human-specific manner, the  world h(W). This has many implications.

  1. Because we know already that the world h(W) is not a static but a dynamic world depending from our learning history it can happen — and it happens all the time — that different individuals have different learning histories.  This can result in quite strong differences of experience and knowledge attached to different individuals, which can prevent a simple understanding between such individuals: the learned world h1(W) can to some degree be different from the learned world  h2(W) such that a simple and direct understanding will not be possible.
  2. This difference between the outside world W and the processed inside world h(W) relates to the communication too! The spoken or written expressions E of some language L are belonging to the outside world. They have a counterpart in the inner world as inner expressions E*, which can be associated with all kinds of processed inner states of the inner world h(W) = W*. These possible — and learned — associations between inner expressions and inner states belonging to h(W) is assumed here to be that what commonly is called meaning. Thus one has to assume an internal meaning function μ which maps the internal expressions E* of some internal language L*  into parts of the internally processed world h(W)=W* and vice versa. Thus we have μ: E* <—> W*. Thus μ(e*) would point to some part w* of the internally processed world W* as the ‘meaning’ of the internal expression e*.
  3. This semiotic architecture of human beings enables a nearly infinite space of expressions as well as associated meanings definable during learning processes. This is powerful, but it is also very demanding for the speaker-hearer: to enable a succesful communication between different speaker-hearer these have to train their language usage under sufficient similar conditions thereby constructing individual meaning functions which work — hopefully — sufficiently similar. If not then communication can slow down, can produce lots of misunderstandings or can even break down completely. [6]
  4. In the case of mathematics it is a long debated question whether mathematics can be reduced to the expressions Em of some mathematical language Lm or if mathematics has some mathematical objects on its own which are different from the expressions. If one would assume that mathematics has no objects on its own but only some expressions Em, then it would become difficult to argue whether exactly these expressions Em should be used and not some other expressions Ex. Moreover to classify expressions as ‘axioms’ or ‘theorems’ would be completely arbitrary.   The only ‘anchor’ of non-arbitrariness would consist in some formal criteria of a formal consistency which would disable the formal generation of pairs of expressions {a,a*} where one is excluding the other. But even such a formal consistency presupposes some criteria which are beyond the expressions as such! Thus mathematics would need some criteria outside mathematics. This can be understood as an argument for metamathematics.  But according to Bourbaki  metamathematics is defined as a set of operations on given expressions without a specific meaning.  This is not enough to establish formal consistency! Thus even metamathematics is pointing to something outside of given mathematical expressions.  What can this be?
PART 2

To be continued …

COMMENTS

[*] More recent versions of the specification of the oksimo oftware can be found in the bolg oksimo.org. Unfortunately are the texts in that blog  — at the time if this writing — still only in German. Hopefully this will change in the future.

[1] Bourbaki group in Wikipedia [EN]: https://en.wikipedia.org/wiki/Nicolas_Bourbaki

[2] N.Bourbaki (1970), Theory of Sets, Series: ELEMENTS OF MATHEMATICS, Springer, Berlin — Heidelberg — New York (Engl. Translation from the French edition 1970)

[3] The first time when the author of this text has encountered the book was some time between 1984 – 1987 while being a PhD-student at the Ludwig-Maximilians Univesty [LMU] in Munich. It was in a seminar with Prof. Peter Hinst about structural approaches to Philosophy of Science. The point of view at that time was completely different to the point of view applied in this text.

[4] Kurt Goedel. Über formal unentscheidbare Sätze der Principia
Mathematica und verwandter Systeme, i. Monatshefte fuer
Mathematik und Physik, 38:173–98, 1931.

[5] Jakob von Uexküll, 1909, Umwelt und Innenwelt der Tiere. Berlin: J.Springer.

[6] Probably everybody has made the experience in his life of being part of a situation where nobody speaks a language, which one is used to speak …

 

 

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 */

HMI ANALYSIS, Part 4: Tool based Actor Story Development with Testing and Gaming

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, March 3-4, 2021,
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: March 4, 2021, 07:49h (Minor corrections; relating to the UN SDGs)

HISTORY

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

HMI ANALYSIS, Part 4: Tool based Actor Story Development with Testing and Gaming

Context

This text is preceded by the following texts:

INFO GRAPH

Overview about different scenarios which will be possible for the development, simulation, testing and gaming of actor stories using the oksimo software tool

Introduction

In the preceding post it has been explained, how one can format an actor story [AS] as a theory in the  format  of  an Evaluated Theory Tε with Algorithmic Intelligence:   Tε,α=<M,∑,ε,α>.

In the following text it will be explained which kinds of different scenarios will be possible to elaborate, to simulate, to test, and to enable gaming with  an actor story theory by using the oksimo software tool.

UNIVERSAL TEAM

The classical distinctions between certain types of managers, special experts and the rest of the world is given up here in favor of a stronger generalization: everybody is a potential expert with regard to a future, which nobody knows. This is emphasized by the fact, that everybody can use its usual mother tongue, a normal language, every language. Nothing more is needed.

BASIC MODELS (S, X)

As minimal elements for all possible applications it is assumed here that the experts define at least a given situation (state) [S] and a set of change rules [X].

The given state S is  either (i)  taken as it is or (ii)  as a state which  should be improved. In both cases the initial state S is called the start state [S0].

The change rules X describe possible changes which transform a given state S into a changed successor state S’.

A pair of S and X as (S,X) is called a basic model M(S,X). One can define as many models as one wants.

A DIRECTION BY A VISION V

A vision [V] can describe a possible state SV  in an assumed future. If such a state SV is given, then this state becomes a goal state SGoal In this case  we assume V ≠ 0. If no explicit goal is given, then we assume V = 0.

DEVELOPMENT BY GOALS

If a vision is given (V ≠ 0), then the vision can be used to induce a direction which can/ shall be approached by creating a set X, which enables the generation of a sequence of states with the start state S0 as first state followed by successor state Si until the goal state SGoal has been reached or at least it holds that the goal state is a subset of the reached state: SGoalSn.

It is possible to use many basic models M(S,X) in parallel and for each model Mi one can define a different goal Vi (the typical situation in a pluralistic society).

Thus there can be many basic theories T(M,V) in parallel.

STEADY STATES (V = 0)

If no explicit visions are defined (V = 0) then every direction of change is allowed. A basic steady state theory T(M,V) with V = 0 can   be written as T(M,0). Whether such a case can be of interest is not clear at the moment.

BASIC INTERACTION PATTERNS

The following interaction modes are assumed as typical cases:

  1. N-1: Within an online session an interactive webpage with the oksimo software is active and the whole group can interact with the oksimo software tool.
  2. N-N-1: N-many participants can individually login into the interactive oksimo website and being logged in they can collaborate within the oksimo software with one project.
  3. N-N-N: N-many participants can individually login into the interactive oksimo website and there everybody can run its own process or can collaborate in various ways.

The default case is case (1). The exact dates for the availability of modes (2) – (3) depends from how fast the roadmap can be realized.

BASIC APPLICATIONS
  1. Exploring Simulation-Based Development [ESBD] (V ≠ 0): If the main goal is to find a path from a given state today S (Now) to an envisioned state V in the future then one has  to collect appropriate change rules X to approach the final goal state SGoal better and better. Activating the simulator ∑ during search and construction phase at will can be of great help, especially if the documents (S, X, V) are becoming more and more complex.
  2. Embedded Simulation-Based  Testing [ESBT] (V ≠ 0): If a basic  actor story theory T(M,) is given with a given goal (V ≠ 0) then it is of great help if the simulation is done in interactive mode where the simulator is not applying the change rules by itself but by asking different logged in users which rule they want to apply and how. These tests show not only which kinds of errors will occur but they can also show during n-many repetitions to which degree an user  can learn to behave task-conform. If the tests will not show the expected outcomes then this can point  to possible deficiencies of the software as well to specialties of the user.
  3. Embedded Simulation-Based Gaming [ESBTG] (V ≠ 0):  The case of gaming is partially  different to the case of testing.  Although it is assumed here too that at least one vision (goal) is given, it is additionally assumed that  there exists  a competition between different players or different teams. Different to testing exists in gaming according to the goal(s) the role of a winner: that player/ team which has reached a defined  goal state before the other player/ teams,  has won. As a side-effect of gaming one can also evaluate the playing environment and give some feedback to the developers.
ALGORITHMIC INTELLIGENCE
  1. Case ESBD, T(S,X,V,∑,ε,α): Because a normal simulation with the simulator always does  produce only one path from the start state to the goal state it is desirable to have an algorithm α which would run on demand as many times as wanted and thereby the algorithm α would search for all possible paths and at the same time it would look for those derivations, where the goal state satisfies with  ε certain special requirements. Thus the result from the application of α onto a given model M with the vision V would generate the set SV* of all those final states which satisfy the special requirements.
  2. Case ESBG, T(S,X,V,∑,ε,α):   The case of gaming allows at least three kinds of interesting applications for algorithmic intelligence: (i) Introduce non-biological players with learning capabilities which can act simultaneously with the biological players; (ii) Introduce non-biological players with learning capabilities which have to learn how to support, to assist, to train biological player. This second case addresses the challenging task to develop algorithmic tutors for several kinds of learning tasks. (iii) Another variant of case (ii) is to enable the development of a personal algorithmic assistant who works only with one person on a long-term basis.

The kinds of algorithmic Intelligence in (2)(i)-(iii) are different to the  mentioned algorithmic intelligence α in (1).

TYPES OF ACTORS

As the default standard case of an actor it is assumed that there are biological actors, usually human persons, which will not be analyzed with their inner structure [IS]. While the behavior of every system — and  therefore any biological system too — can be described with a behavior function φ: I x IS —> IS x O (if one has all the necessary knowledge), in the default case of biological systems  no behavior function φ is specified, φ = 0. During interactive simulations biological systems act by themselves.

If non-biological actors are used — e.g. automata with a certain machine program (an algorithm) — then one can use these only if one has a fully specified behavior function φ. From this follows that a  change rule which is associated with a non-biological actor has in its Eplus and in its Eminus part not a concrete expression but a variable, which will be computed during the simulation by the non-biological actor depending from its input and its behavior function φ: φ(input)IS=(Eplus, Eminus)IS.

FINAL COMMENT

Everybody who has read the parts (1) – (4) has now a general knowledge about the motivation to develop the oksimo software tool to support human kind to have a better communication and thinking of possible futures and a first understanding (hopefully :-)) how this tool can work. Reading the UN sustainable development goals [SDGs] [1] you will learn, that the SDG4 (Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all) is fundamental to all other SDGs. The oksimo software tool is one tool to be of help to reach these goals.

REFERENCES

[1] The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries – developed and developing – in a global partnership. They recognize that ending poverty and other deprivations must go hand-in-hand with strategies that improve health and education, reduce inequality, and spur economic growth – all while tackling climate change and working to preserve our oceans and forests. See PDF: https://sdgs.un.org/sites/default/files/publication/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf

[2] UN, SDG4, PDF, Argumentation why the SDG4 ist fundamental for all other SDGs: https://sdgs.un.org/sites/default/files/publications/2275sdbeginswitheducation.pdf

 

 

 

 

 

 

 

 

HMI Analysis for the CM:MI paradigm. Part 3. Actor Story and Theories

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, March 2, 2021,
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: March 2, 2021 13:59h (Minor corrections)

HISTORY

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

HMI ANALYSIS, Part 3: Actor Story and  Theories

Context

This text is preceded by the following texts:

Introduction

Having a vision is that moment  where something really new in the whole universe is getting an initial status in some real brain which can enable other neural events which  can possibly be translated in bodily events which finally can change the body-external outside world. If this possibility is turned into reality than the outside world has been changed.

When human persons (groups of homo sapiens specimens) as experts — here acting as stakeholder and intended users as one but in different roles! — have stated a problem and a vision document, then they have to translate these inevitably more fuzzy than clear ideas into the concrete terms of an everyday world, into something which can really work.

To enable a real cooperation  the experts have to generate a symbolic description of their vision (called specification) — using an everyday language, possibly enhanced by special expressions —  in a way that  it can became clear to the whole group, which kind of real events, actions and processes are intended.

In the general case an engineering specification describes concrete forms of entanglements of human persons which enable  these human persons to cooperate   in a real situation. Thereby the translation of  the vision inside the brain  into the everyday body-external reality happens. This is the language of life in the universe.

WRITING A STORY

To elaborate a usable specification can metaphorically be understood  as the writing of a new story: which kinds of actors will do something in certain situations, what kinds of other objects, instruments etc. will be used, what kinds of intrinsic motivations and experiences are pushing individual actors, what are possible outcomes of situations with certain actors, which kind of cooperation is  helpful, and the like. Such a story is  called here  Actor Story [AS].

COULD BE REAL

An Actor Story must be written in a way, that all participating experts can understand the language of the specification in a way that   the content, the meaning of the specification is either decidable real or that it eventually can become real.  At least the starting point of the story should be classifiable as   being decidable actual real. What it means to be decidable actual real has to be defined and agreed between the participating experts before they start writing the Actor Story.

ACTOR STORY [AS]

An Actor Story assumes that the described reality is classifiable as a set of situations (states) and  a situation as part of the Actor Story — abbreviated: situationAS — is understood  as a set of expressions of some everyday language. Every expression being part of an situationAS can be decided as being real (= being true) in the understood real situation.

If the understood real situation is changing (by some event), then the describing situationAS has to be changed too; either some expressions have to be removed or have to be added.

Every kind of change in the real situation S* has to be represented in the actor story with the situationAS S symbolically in the format of a change rule:

X: If condition  C is satisfied in S then with probability π  add to S Eplus and remove from  S Eminus.

or as a formula:

S’π = S + Eplus – Eminus

This reads as follows: If there is an situationAS S and there is a change rule X, then you can apply this change rule X with probability π onto S if the condition of X is satisfied in S. In that case you have to add Eplus to S and you have to remove Eminus from S. The result of these operations is the new (successor) state S’.

The expression C is satisfied in S means, that all elements of C are elements of S too, written as C ⊆ S. The expression add Eplus to S means, that the set Eplus is unified with the set S, written as Eplus ∪ S (or here: Eplus + S). The expression remove Eminus from S means, that the set Eminus is subtracted from the set S, written as S – Eminus.

The concept of apply change rule X to a given state S resulting in S’ is logically a kind of a derivation. Given S,X you will derive by applicating X the new  S’. One can write this as S,X ⊢X S’. The ‘meaning’ of the sign ⊢  is explained above.

Because every successor state S’ can become again a given state S onto which change rules X can be applied — written shortly as X(S)=S’, X(S’)=S”, … — the repeated application of change rules X can generate a whole sequence of states, written as SQ(S,X) = <S’, S”, … Sgoal>.

To realize such a derivation in the real world outside of the thinking of the experts one needs a machine, a computer — formally an automaton — which can read S and X documents and can then can compute the derivation leading to S’. An automaton which is doing such a job is often called a simulator [SIM], abbreviated here as ∑. We could then write with more information:

S,X ⊢ S’

This will read: Given a set S of many states S and a set X of change rules we can derive by an actor story simulator ∑ a successor state S’.

A Model M=<S,X>

In this context of a set S and a set of change rules X we can speak of a model M which is defined by these two sets.

A Theory T=<M,>

Combining a model M with an actor story simulator enables a theory T which allows a set of derivations based on the model, written as SQ(S,X,⊢) = <S’, S”, … Sgoal>. Every derived final state Sgoal in such a derivation is called a theorem of T.

An Empirical Theory Temp

An empirical theory Temp is possible if there exists a theory T with a group of experts which are using this theory and where these experts can interpret the expressions used in theory T by their built-in meaning functions in a way that they always can decide whether the expressions are related to a real situation or not.

Evaluation [ε]

If one generates an Actor Story Theory [TAS] then it can be of practical importance to get some measure how good this theory is. Because measurement is always an operation of comparison between the subject x to be measured and some agreed standard s one has to clarify which kind of a standard for to be good is available. In the general case the only possible source of standards are the experts themselves. In the context of an Actor Story the experts have agreed to some vision [V] which they think to be a better state than a  given state S classified as a problem [P]. These assumptions allow a possible evaluation of a given state S in the ‘light’ of an agreed vision V as follows:

ε: V x S —> |V ⊆ S|[%]
ε(V,S) = |V ⊆ S|[%]

This reads as follows: the evaluation ε is a mapping from the sets V and S into the number of elements from the set V included in the set S converted in the percentage of the number of elements included. Thus if no  element of V is included in the set S then 0% of the vision is realized, if all elements are included then 100%, etc. As more ‘fine grained’ the set V is as more ‘fine grained’  the evaluation can be.

An Evaluated Theory Tε=<M,,ε>

If one combines the concept of a  theory T with the concept of evaluation ε then one can use the evaluation in combination with the derivation in the way that every  state in a derivation SQ(S,X,⊢) = <S’, S”, … Sgoal> will additionally be evaluated, thus one gets sequences of pairs as follows:

SQ(S,X,⊢∑,ε) = <(S’,ε(V,S’)), (S”,ε(V,S”)), …, (Sgoal, ε(V,Sgoal))>

In the ideal case Sgoal is evaluated to 100% ‘good’. In real cases 100% is only an ideal value which usually will only  be approximated until some threshold.

An Evaluated Theory Tε with Algorithmic Intelligence Tε,α=<M,,ε,α>

Because every theory defines a so-called problem space which is here enhanced by some evaluation function one can add an additional operation α (realized by an algorithm) which can repeat the simulator based derivations enhanced with the evaluations to identify those sets of theorems which are qualified as the best theorems according to some criteria given. This operation α is here called algorithmic intelligence of an actor story AS]. The existence of such an algorithmic intelligence of an actor story [αAS] allows the introduction of another derivation concept:

S,X ⊢∑,ε,α S* ⊆  S’

This reads as follows: Given a set S and a set X an evaluated theory with algorithmic intelligence Tε,α can derive a subset S* of all possible theorems S’ where S* matches certain given criteria within V.

WHERE WE ARE NOW

As it should have become clear now the work of HMI analysis is the elaboration of a story which can be done in the format of different kinds of theories all of which can be simulated and evaluated. Even better, the only language you have to know is your everyday language, your mother tongue (mathematics is understood here as a sub-language of the everyday language, which in some special cases can be of some help). For this theory every human person — in all ages! — can be a valuable  colleague to help you in understanding better possible futures. Because all parts of an actor story theory are plain texts, everybody ran read and understand everything. And if different groups of experts have investigated different  aspects of a common field you can merge all texts by only ‘pressing a button’ and you will immediately see how all these texts either work together or show discrepancies. The last effect is a great opportunity  to improve learning and understanding! Together we represent some of the power of life in the universe.

CONTINUATION

See here.

 

 

 

 

 

 

 

 

KOMEGA REQUIREMENTS No.4, Version 2. Basic Application Scenario

ISSN 2567-6458, 28.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

PDF DOCUMENT

requirements-no4-v2-27Aug2020

KOMEGA REQUIREMENTS No.4, Version 1

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

CONTEXT

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

PDF DOCUMENT

requirements-no4-v1-26Aug2020

KOMEGA REQUIREMENTS No.3, Version 1. Basic Application Scenario – Editing S

ISSN 2567-6458, 26.July – 12.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

PDF DOCUMENT

requirements-no3-v1-12Aug2020 (Last update: August 12, 2020)

KOMEGA REQUIREMENTS No.2. Actor Story Overview

ISSN 2567-6458, 26.July – 12.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

PDF DOCUMENT

requirements-no2-v1-11Aug2020 (Last change: August 12, 2020)

KOMEGA REQUIREMENTS No.1. Basic Application Scenario

KOMEGA REQUIREMENTS No.1. Basic Application Scenario

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

CONTEXT

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

PDF TEXT:

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

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

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

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

STARTING WITH PYTHON3 – The very beginning – part 9

Journal: uffmm.org,
ISSN 2567-6458, July 24-25, 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email:gerd@doeben-henisch.de

CONTEXT

This is the next step in the python3 programming project. The overall context is still the python Co-Learning project.

SUBJECT

In this file you will see a first encounter between the AAI paradigm (described in the theory part of this uffmm blog) and some applications of the python programming language. A simple virtual world with objects and actors can become activated with a free selectable size, amount of objects and amount of actors. In later post lots of experiments with this virtual world will be described as well as many extensions.

SOURCE CODE
Main file: vw4.py

The main file ‘vw4.py’ describes the start of a virtual world and then allows a loop to run this world n-many times.

Import file: vwmanager.py

The main file ‘vw4.py’ is using many functions to enable the process. All these functions are collected in the file ‘vwmanager.py’. This file will automatically be loaded during run time of the program vw4.py.

COMMENTS

comment-vw4

DEMO

TEST RUN AUG 19, 2919, 12:56h

gerd@Doeben-Henisch:~/code$ python3 vw4.py
Amount of information: 1 is maximum, 0 is minimum0
Number of columns (= equal to rows!) of 2D-grid ?4
[‘_’, ‘_’, ‘_’, ‘_’]

[‘_’, ‘_’, ‘_’, ‘_’]

[‘_’, ‘_’, ‘_’, ‘_’]

[‘_’, ‘_’, ‘_’, ‘_’]

Percentage (as integer) of obstacles in the 2D-grid?77
Percentage (as integer) of Food Objects in the 2D-grid ?44
Percentage (as integer) of Actor Objects in the 2D-grid ?15

Objects as obstacles

[0, 2, ‘O’]

[0, 3, ‘O’]

[1, 2, ‘O’]

[2, 3, ‘O’]

Objects as food

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Objects as actor

[1, 3, ‘A’, [0, 1000, 100, 500, 0]]

[3, 2, ‘A’, [1, 1000, 100, 500, 0]]

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘A’, ‘F’]

END OF PREPARATION

WORLD CYCLE STARTS

—————————————————-
Real percentage of obstacles = 25.0
Real percentage of food = 37.5
Real percentage of actors = 12.5
—————————————————-
How many CYCLES do you want?25
Singe Step = 1 or Continous = 0?1
Length of olA 2

—————————————————–

WORLD AT CYCLE = 0

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘A’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 1000, 100, 500, -1]]

[2, 1, ‘A’, [1, 1000, 100, 500, 8]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 1

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘A’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 900, 100, 500, -1]]

[2, 1, ‘A’, [1, 900, 100, 500, 0]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 2

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘A’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 800, 100, 500, -1]]

[1, 1, ‘A’, [1, 1300, 100, 500, 1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 500, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 3

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 700, 100, 500, -1]]

[2, 0, ‘A’, [1, 1700, 100, 500, 6]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 600, 100]]

[2, 0, ‘F’, [2, 500, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 4

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘A’]

[‘A’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 600, 100, 500, -1]]

[1, 0, ‘A’, [1, 1600, 100, 500, 1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 700, 100]]

[2, 0, ‘F’, [2, 600, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 5

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 500, 100, 500, -1]]

[1, 1, ‘A’, [1, 2000, 100, 500, 3]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 300, 100]]

[2, 0, ‘F’, [2, 700, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 6

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 400, 100, 500, -1]]

[1, 1, ‘A’, [1, 1900, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 400, 100]]

[2, 0, ‘F’, [2, 800, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 7

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 300, 100, 500, -1]]

[1, 1, ‘A’, [1, 1800, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 500, 100]]

[2, 0, ‘F’, [2, 900, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 8

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 200, 100, 500, -1]]

[1, 1, ‘A’, [1, 1700, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 600, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 2

—————————————————–

WORLD AT CYCLE = 9

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘A’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 3, ‘A’, [0, 100, 100, 500, 0]]

[1, 0, ‘A’, [1, 1600, 100, 500, 7]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 700, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 10

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 0, ‘A’, [1, 1500, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 800, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 11

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 0, ‘A’, [1, 1400, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 900, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 12

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 0, ‘A’, [1, 1300, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 13

[‘F’, ‘_’, ‘O’, ‘O’]

[‘A’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[2, 0, ‘A’, [1, 1700, 100, 500, 5]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 500, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 14

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘A’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 1, ‘A’, [1, 2100, 100, 500, 2]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 500, 100]]

[2, 0, ‘F’, [2, 600, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 15

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2500, 100, 500, 8]]

[0, 0, ‘F’, [0, 500, 100]]

[1, 1, ‘F’, [1, 600, 100]]

[2, 0, ‘F’, [2, 700, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 16

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2400, 100, 500, -1]]

[0, 0, ‘F’, [0, 600, 100]]

[1, 1, ‘F’, [1, 700, 100]]

[2, 0, ‘F’, [2, 800, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 17

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2300, 100, 500, -1]]

[0, 0, ‘F’, [0, 700, 100]]

[1, 1, ‘F’, [1, 800, 100]]

[2, 0, ‘F’, [2, 900, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 18

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2200, 100, 500, -1]]

[0, 0, ‘F’, [0, 800, 100]]

[1, 1, ‘F’, [1, 900, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 19

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2100, 100, 500, -1]]

[0, 0, ‘F’, [0, 900, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 20

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 2000, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 21

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 0, ‘A’, [1, 1900, 100, 500, 0]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 22

[‘A’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[0, 1, ‘A’, [1, 1800, 100, 500, 3]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 1000, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 23

[‘F’, ‘A’, ‘O’, ‘O’]

[‘_’, ‘F’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 1, ‘A’, [1, 2200, 100, 500, 5]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 500, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

Length of olA 1

—————————————————–

WORLD AT CYCLE = 24

[‘F’, ‘_’, ‘O’, ‘O’]

[‘_’, ‘A’, ‘O’, ‘_’]

[‘F’, ‘_’, ‘F’, ‘O’]

[‘F’, ‘_’, ‘_’, ‘F’]

Press key c for continuation!c
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

Updated energy levels in olF and olA
[1, 1, ‘A’, [1, 2100, 100, 500, -1]]

[0, 0, ‘F’, [0, 1000, 100]]

[1, 1, ‘F’, [1, 600, 100]]

[2, 0, ‘F’, [2, 1000, 100]]

[2, 2, ‘F’, [3, 1000, 100]]

[3, 0, ‘F’, [4, 1000, 100]]

[3, 3, ‘F’, [5, 1000, 100]]

 

STARTING WITH PYTHON3 – The very beginning – part 5

Journal: uffmm.org,
ISSN 2567-6458, July 18-19, 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email:
gerd@doeben-henisch.de

CONTEXT

This is the next step in the python3 programming project. The overall context is still the python Co-Learning project.

SUBJECT

After a first clearing of the environment for python programming we have started with the structure of the python programming language, and in this section will continue dealing with the object type sequences and string and more programming elements are shown in a simple example of a creative actor.

Remark: for general help information go directly to the python manuals, which you can find associated with the entry for python 3.7.3 if you press the Windows-Button, look to the list of Apps (= programs), and identify the entry for python 3.7.3. If you open the python entry by clicking you see the sub-entry python 3.7.3 Manuals. If you click on this sub-entry the python documentation will open. In this documentation you can find nearly everything you will need. For Beginners you even find a small tutorial.

SZENARIO

For the further discussion of additional properties of python string and sequence objects I will assume again a simple scenario. I will expand the last scenario with the simple input-output actor by introducing some creativity into the actor. This means that the actor receives again either one word or sequences of words but instead of classifying the word according to some categories or instead of giving back the list of the multiple words as individual entities the actor will change the input creatively.
In case of a single word the actor will re-order the symbols of the string and additionally he can replace one individual symbol by some random symbol out of a finite alphabet.
In case of multiple words the actor will first partition the sequence of words into the individual words in a list, then he will also re-order these items of the list, will then re-order the letters in the words, and finally he can replace in every word one individual symbol by some random symbol out of a finite alphabet. After these operations the list is again concatenated to one sequence of words.
In this version of the program one can repeat in two ways: either (i) input manually new words or (ii) redirect the output into the input that the actor can continue to change the output further.
Interesting feature Cognitive Entropy: If the user selects always the closed world option then the set of available letters will not be expanded during all the repetitions. This reveals then after some repetitions the implicit tendency of all words to become more and more equal until only one type of word ‘survived’. This depends on the random character of the process which increases the chances of the bigger numbers to overrun the smaller ones. The other option is the open world option. This includes that in a repetition a completely new letter can be introduced in a single word. This opposes the implicit tendency of cognitive entropy to enforce the big numbers against the smaller ones.

How can this scenario be realized?

ACTOR STORY

1. There is a user (as executive actor) who can enter single or multiple words into the input interface of an assisting interface.
2. After confirming the input the assisting actor will respond in a creative manner. These creativity is manifested in changed orders of symbols and words as well as by replaced symbols.
3. After the response the user can either repeat the sequence or he can stop. If repeating then he can select between two options: (i) enter manually new words as input or (ii) redirect the output of the system as new input. This allows a continuous creative change of the words.
4. The repeated re-direction offers again two options: (i) Closed world, no real input, or (ii) Open world; some real new input

IMPLEMENTATION

Download here the python source code. This text appears as an HTML-document, because the blog-software does not allow to load a python program file directly.

stringDemo2.py

DEMOS

Single word in a closed world:

PS C:\Users\gerd_2\code> python stringDemo2.py
Single word = ‘1’ or Multiple words = ‘2’
1
New manual input =’1′ or Redirect the last output = ‘2’
1
Closed world =’1′ or Open world =’2′
1
Input a single word
abcde
Your input word is = abcde
New in-word order with worder():
ebaca
STOP = ‘N’, CONTINUE != ‘N’
y
Single word = ‘1’ or Multiple words = ‘2’
1
New manual input =’1′ or Redirect the last output = ‘2’
2
Closed world =’1′ or Open world =’2′
1
The last output was = ebaca
New in-word order with worder():
ccbaa
STOP = ‘N’, CONTINUE != ‘N’
y
Single word = ‘1’ or Multiple words = ‘2’
1
New manual input =’1′ or Redirect the last output = ‘2’
2
Closed world =’1′ or Open world =’2′
1
The last output was = ccbaa
New in-word order with worder():
ccccb
STOP = ‘N’, CONTINUE != ‘N’
y
Single word = ‘1’ or Multiple words = ‘2’
1
New manual input =’1′ or Redirect the last output = ‘2’
2
Closed world =’1′ or Open world =’2′
1
The last output was = ccccb
New in-word order with worder():
ccccc
STOP = ‘N’, CONTINUE != ‘N’

The original word ‘abcde’ has been changed to ‘ccccc’ in a closed world environment. If one introduces an open world scenario then this monotonicity can never happen.

Multiple words in a closed world

PS C:\Users\gerd_2\code> python stringDemo2.py
Single word = ‘1’ or Multiple words = ‘2’
2
New manual input =’1′ or Redirect the last output = ‘2’
1
Closed world =’1′ or Open world =’2′
1
Input multiple words
abc def geh
Your input words are = abc def geh
List version of sqorder input =
[‘abc’, ‘def’, ‘geh’]
New word order in sequence with sqorder():
def geh geh
List version of input in mcworder()=
[‘def’, ‘geh’, ‘geh’]
New in-word order with worder():
fef
New in-word order with worder():
hee
New in-word order with worder():
ege
New word-sequence order :
fef hee ege
STOP = ‘N’, CONTINUE != ‘N’
y
Single word = ‘1’ or Multiple words = ‘2’
2
New manual input =’1′ or Redirect the last output = ‘2’
2
Closed world =’1′ or Open world =’2′
1
The last output was = fef hee ege
List version of sqorder input =
[‘fef’, ‘hee’, ‘ege’]
New word order in sequence with sqorder():
fef fef ege
List version of input in mcworder()=
[‘fef’, ‘fef’, ‘ege’]
New in-word order with worder():
fff
New in-word order with worder():
fee
New in-word order with worder():
eee
New word-sequence order :
fff fee eee
STOP = ‘N’, CONTINUE != ‘N’
y
Single word = ‘1’ or Multiple words = ‘2’
2
New manual input =’1′ or Redirect the last output = ‘2’
2
Closed world =’1′ or Open world =’2′
1
The last output was = fff fee eee
List version of sqorder input =
[‘fff’, ‘fee’, ‘eee’]
New word order in sequence with sqorder():
eee fee fee
List version of input in mcworder()=
[‘eee’, ‘fee’, ‘fee’]
New in-word order with worder():
eee
New in-word order with worder():
eef
New in-word order with worder():
eee
New word-sequence order :
eee eef eee
STOP = ‘N’, CONTINUE != ‘N’
y
Single word = ‘1’ or Multiple words = ‘2’
2
New manual input =’1′ or Redirect the last output = ‘2’
2
Closed world =’1′ or Open world =’2′
1
The last output was = eee eef eee
List version of sqorder input =
[‘eee’, ‘eef’, ‘eee’]
New word order in sequence with sqorder():
eee eee eee
List version of input in mcworder()=
[‘eee’, ‘eee’, ‘eee’]
New in-word order with worder():
eee
New in-word order with worder():
eee
New in-word order with worder():
eee
New word-sequence order :
eee eee eee
STOP = ‘N’, CONTINUE != ‘N’

You can see that the cognitive entropy replicates with the closed world assumption in the multi-word scenario too.

EXERCISES

Here are some details of objects and operations.

Letters and Numbers

With  ord(‘a’) one can get the decimal code of the letter as ’97’ and the other way around one can translate a decimal number ’97’ in a letter with  chr(97) to ‘a’.  For ord(‘z’) one gets ‘122’, and then one can use the numbers to compute characters which has been used in the program to find random characters to be inserted in a word.

Strings and Lists

There are some operations only available for list-objects and others only for string-objects.  Thus to change and re-arrange a string directly is not possible, but translating a string in a list, then apply some operations, and then transfer the changed list back into a string, this works fine. Thus translate a word w into a list wl by wl = list(w) allows the re-order of these elements by appending: wll.append(wl[r]). Afterwords I have translated the list again in a string by constructing a new string wnew by concatenating all letters step by step: wnew=wnew+wl[i]. If yould try to transfer the list directly like in the following example, then you will get as a result again  list:

>> w=’abcd’
>>> wl=list(w)
>>> wl
[‘a’, ‘b’, ‘c’, ‘d’]
>>> wn=str(wl)
>>> wn
“[‘a’, ‘b’, ‘c’, ‘d’]”

Immediate Help

If one needs direct information about the operations which are possible with a certain object like here the string object ‘w’, then one can ask for all possible operation like this:

>> dir(w)
[‘__add__’, ‘__class__’, ‘__contains__’, ‘__delattr__’, ‘__dir__’, ‘__doc__’, ‘__eq__’, ‘__format__’, ‘__ge__’, ‘__getattribute__’, ‘__getitem__’, ‘__getnewargs__’, ‘__gt__’, ‘__hash__’, ‘__init__’, ‘__init_subclass__’, ‘__iter__’, ‘__le__’, ‘__len__’, ‘__lt__’, ‘__mod__’, ‘__mul__’, ‘__ne__’, ‘__new__’, ‘__reduce__’, ‘__reduce_ex__’, ‘__repr__’, ‘__rmod__’, ‘__rmul__’, ‘__setattr__’, ‘__sizeof__’, ‘__str__’, ‘__subclasshook__’, ‘capitalize’, ‘casefold’, ‘center’, ‘count’, ‘encode’, ‘endswith’, ‘expandtabs’, ‘find’, ‘format’, ‘format_map’, ‘index’, ‘isalnum’, ‘isalpha’, ‘isascii’, ‘isdecimal’, ‘isdigit’, ‘isidentifier’, ‘islower’, ‘isnumeric’, ‘isprintable’, ‘isspace’, ‘istitle’, ‘isupper’, ‘join’, ‘ljust’, ‘lower’, ‘lstrip’, ‘maketrans’, ‘partition’, ‘replace’, ‘rfind’, ‘rindex’, ‘rjust’, ‘rpartition’, ‘rsplit’, ‘rstrip’, ‘split’, ‘splitlines’, ‘startswith’, ‘strip’, ‘swapcase’, ‘title’, ‘translate’, ‘upper’, ‘zfill’]
>>>

In the case that ‘w’ is a sequence of strings/ words like w=’abc def’, then does the list operations be of no help, because one gets a list of letters, not of words:

>> wl2=list(w)
>>> wl2
[‘a’, ‘b’, ‘c’, ‘ ‘, ‘d’, ‘e’, ‘f’]

For the program one needs a list of single words. Looking to the possible operations with string objects with Dir() above, one sees the name ‘split’. We can ask, what this ‘split’ is about:

>>> help(str.split)
Help on method_descriptor:

split(self, /, sep=None, maxsplit=-1)
Return a list of the words in the string, using sep as the delimiter string.

sep
The delimiter according which to split the string.
None (the default value) means split according to any whitespace,
and discard empty strings from the result.
maxsplit
Maximum number of splits to do.
-1 (the default value) means no limit.

This sounds as if it could be of help. Indeed, that is the mechanism I have used:

>> w=’abc def’
>>> w
‘abc def’
>>> wl=w.split()
>>> wl
[‘abc’, ‘def’]

Function Definition

As you can see in the program text the minimal structure of a function definition is as follows:

def fname(Input-Arguments):
     some commands
    [return VarNames]

The name is needed for the identification of the command, the input variables to get some values from the outside to work on and finally, but optionally, you can return the values of some variables back to the outside of the function.

The For-Loop

Besides the loop organized with the while-command there is the other command with a fixed number of repetitions indicated by the for-command:

for i in range(n):
commands

The operator ‘range()’ delivers a sequence of numbers from ‘0’ to ‘n-1’ and attaches these to the variable ‘i’. Thus the variable i takes one after the other all the numbers from range(). During one repetition all the commands will be executed which are listed after the for-command.

Random Numbers

In this program very heavily I have used random numbers. To be able to do this one has before this usage to import the random number library. I did this with the call:

import random as rnd

This introduces additionally an abbreviation ‘rnd’. Thus if one wants to call a certain operation from the random object one can write like this:

r=rnd.randrange(0,n)

In this example one uses  the randrange() operation from random with the arguments (0,n) this means that an integer random number will be generated in the intervall [0,n-1].

If-Operator with Combined Conditions

In the program you can find statements like

if opt==’1′ and opt2==’1′ and opt3==’1′:

Following the if-keyword you see three different conditions

opt==’1′
opt2==’1′
opt3==’1′

which are put together to one expression by the logical operator ‘and’. This means that all three conditions must simultaneously be true, otherwise this combined condition will not work.

Introduce the Import Module Mechanism

See for this the two files:

stringDemo2b.py
stringDemos.py

StringDemo2b.py is the same as stringDemo2.py discussed above but all the supporting functions are now removed from the main file and stored in an extra file called ‘stringDemos.py’ which works for the main file stringDemo2b.py as a module file. That this works there must be a special

import stringDemos as sd

command and at each occurence of a function call with functions from the imported module in the main module stringDemo2b.py one has to add the prefix ‘sd.’ indicating, that these functions are now located in a special place.

This import call does work only if the special path for the import module ‘stringDemos.py’ is visible to the python modulecall mechanisms. In this case the Path with the modul stringDemos.py is given as C:\Users\gerd_2\code. If one wants know what the actual path names are which are known to the python system one can use a system call:

>> import sys
>>> sys.path
>>> …

If the wanted path is not yet part of these given paths one can append the new path like this:

>> sys.path.append(‘C:\\Users\\gerd_2\\code’)

If this has all done rightly one can work with the program like before. The main advantage of this splitting of the main program and of the supporting functions is (i) a greater transparency of the main code and (ii) the supporting functions can now easily be used from other programs too if needed.

A next possible continuation you can find HERE.

 

STARTING WITH PYTHON3 – The very beginning – part 4

Journal: uffmm.org,
ISSN 2567-6458, July 15, 2019 – May 9, 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email:
gerd@doeben-henisch.de

Change: July 16, 2019 (Some re-arrangement of the content :-))

CONTEXT

This is the next step in the python3 programming project. The overall context is still the python Co-Learning project.

SUBJECT

After a first clearing of the environment for python programming we have started with the structure of the python programming language, and in this section will deal with the object type string(s).

Remark: the following information about strings you can get directly from the python manuals, which you can find associated with the entry for python 3.7.3 if you press the Windows-Button, look to the list of Apps (= programs), and identify the entry for python 3.7.3. If you open the python entry by clicking you see the sub-entry python 3.7.3 Manuals. If you click on this sub-entry the python documentation will open. In this documentation you can find nearly everything you will need. For Beginners you even find a nice tutorial.

TOPIC: VALUES (OBJECTS) AS STRINGS

PROBLEM(s)

(1) When I see a single word (a string of symbols) I do not know which type this is in python. (2) If I have a statement with many words I would like to get from this a partition into all the single words for further processing.

VISION OF A SOLUTION

There is a simple software actor which can receive as input either single words or multiple words and which can respond by giving either the type of the received word or the list of the received multiple words.

ACTOR STORY (AS)

We assume a human user as executing actor (eA) and a piece of running software as an assisting actor (aA). For these both we assume the following sequence of states:

  1. The user will start the program by calling python and the name of the program.
  2. The program offers the user two options: single word or multiple words.
  3. The user has to select one of these options.
  4. After the selection the user can enter accordingly either one  or multiple words.
  5. The program will respond either with the recognized type in python or with a list of words.
  6. Finally asks the program the user whether he/she will continue or stop.
  7. Depending from the answer of the user the program will continue or stop.

IMPLEMENTATION

Here you can download the sourcecode: stringDemo1

# File stringDemo1.py
# Author: G.Doeben-Henisch
# First date: July 15, 2019

##################
# Function definition sword()

def sword(w1):
w=str(w1)
if w.islower():
print(‘Is lower\n’)
elif w.isalpha() :
print(‘Is alpha\n’)
elif w.isdecimal():
print(‘Is decimal\n’)
elif w.isascii():
print(‘Is ascii\n’)
else : print(‘Is not lower, alpha, decimal, ascii\n’)

##########################
# Main Programm

###############
# Start main loop

loop=’Y’
while loop==’Y’:

###################
# Ask for Options

opt=input(‘Single word =1 or multiple words =2\n’)

if opt==’1′:
w1=input(‘Input a single word\n’)
sword(w1) # Call for new function defined above

elif opt==’2′:
w1=input(‘Input multiple words\n’)
w2=w1.split() # Call for built-in method of class str
print(w2)

loop=input(‘To stop enter N or Y otherwise\n’) # Check whether loop shall be repeated

DEMO

Here it is assumed that the code of the python program is stored in the folder ‘code’ in my home director.

I am starting the windows power shell (PS) by clicking on the icon. Then I enter the command ‘cd code’ to enter the folder code. Then I call the python interpreter together with the demo programm ‘stringDemo1.py’:

PS C:\Users\gerd_2\code> python stringDemo1.py
Single word =1 or multiple words =2

Then I select first option ‘Single word’ with entering 1:

1
Input a single word
Abrakadabra
Is alpha

To stop enter N

After entering 1 the program asks me to enter a single word.

I am entering the fantasy word ‘Abrakadabra’.

Then the program responds with the classification ‘Is alpha’, what is correct. If I want to stop I have to enter ‘N’ otherwise it contiues.

I want o try another word, therefore I am entering ‘Y’:

Y
Single word =1 or multiple words =2

I select again ‘1’ and the new menue appears:

1
Input a single word
29282726
Is decimal

To stop enter N

I entered a sequence of digits which has been classified as ‘decimal’.

I want to contiue with ‘Y’ and entering ‘2’:

Y
Single word =1 or multiple words =2
2
Input multiple words
Hans kommt meistens zu spät
[‘Hans’, ‘kommt’, ‘meistens’, ‘zu’, ‘spät’]
To stop enter N

I have entered a German sentence with 5 words. The response of the system is to identify every single word and generate a list of the individual words.

Thus, so far, the test works fine.

COMMENTS TO THE SOURCE CODE

Before the main program a new function ‘sword()’ has been defined:

def sword(w1):

The python keyword ‘def‘ indicates that here the definition of a function  takes place, ‘sword‘ is the name of this new function, and ‘w1‘ is the input argument for this function. ‘w1’ as such is the name of a variable pointing to some memory place and the value of this variable at this place will depend from the context.

w=str(w1)

The input variable w1 is taken by the operator str and str translates the input value into a python object of type ‘string’. Thus the further operations with the object string can assume that it is a string and therefore one can apply alle the operations to the object which can be applied to strings.

if w.islower():

One of these string-specific operations is islower(). Attached to the string object ‘w’ by a dot-operator ‘.’ the operation ‘islower() will check, whether the string object ‘w’ contains lower case symbols. If yes then the following ‘print()’ operation will send this message to the output, otherwise the program continues with the next ‘elif‘ statement.

The ‘if‘ (and following the if the ‘elif‘) keyword states a condition (whether ‘w’ is of type ‘lower case symbols’). The statement closes with the ‘:’ sign. This statement can be ‘true’ or not. If it is true then the part after the ‘:’ sign will be executed (the ‘print()’ action), if false then the next condition ‘elif … :’ will be checked.

If no condition would be true then the ‘else: …’ statement would be executed.

The main program is organized as a loop which can iterate as long as the user does not stop it. This entails that the user can enter as many words or multi-words as he/ she wants.

loop=’Y’
while loop==’Y’:

In the first line the variable ‘loop’ receives as a value the string ‘Y’ (short for ‘yes’). In the next line starts the loop with the python key-word ‘while’ forming a condition statement ‘while … :’. This is similar to the condition statements above with ‘if …. :’ and ‘elif … :’.

The condition depends on the expression ‘loop == ‘Y” which means that  as long as the variable loop is logically equal == to the value ‘Y’ the loop condition  is ‘true’ and the part after the ‘:’ sign will be executed. Thus if one wants to break this loop one has to change the value of the variable ‘loop’ before the while-statement ‘while … :’ will be checked again. This check is done in the last line of the while-execution part with the input command:

loop=input(‘To stop enter N\n’)

Before the while-condition will be checked again there is this input() operator asking the user to enter a ‘N’ if he/ she wantds to stop. If the user  enters a  ‘N’  in the input line the result of his input will be stored in the variable called ‘loop’ and therefore the variable will have the value ‘==’N” which is different from ‘==’Y”. But what would happen if the user enters something different from ‘N’ and ‘Y’, because ‘Y’ is expected for repetition?

Because the user does not know that he/she has to enter ‘Y’ to continue the program will highly probably stop even if the user does not want to stop. To avoid this unwanted case one should change the code for the while-condition as follows:

while loop!=’N’:

This states that the loop will be true as long as the value of the loop variable is different != from the value ‘N’ which will explicitly asked from the user at the end of the loop.

The main part of the while-loop distinguishes two cases: single word or multiple words. This is realized by a new input() operation:

opt=input(‘Single word =1 or multiple words =2\n’)

The user can enter a ‘1’ or a ‘2’, which will be stored in the variable ‘opt’. Then a construction with an if or an elif will test which one of these both happens. Depending from the option 1 or 2 ther program asks the user again with an input() operation for the specific input (one word or multiple words).

sword(w1)

In the case of the one word input in the variable ‘w1’ w1 contains as value a string input which will be delivered as input argument to the new function ‘sword()’ (explanation see above). In case of input 2 the

w2=w1.split()

‘split()’ operation will be applied to the object ‘w1’ by the dot operator ‘.’. This operation will take every word separated by a ‘blank’ and generates a list ‘[ … ]’ with the individual words as elements.

A next possible continuation you can find HERE.

 

PHILOSOPHY LAB

eJournal: uffmm.org

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

Changes: July 20.2019 (Rewriting the introduction)

CONTEXT

This Philosophy Lab section of the uffmm science blog is the last extension of the uffmm blog, happening July 2019. It has been provoked by the meta reflections about the AAI engineering approach.

SCOPE OF SECTION

This section deals with  the following topics:

  1. How can we talk about science including the scientist (and engineer!) as the main actors? In a certain sense one can say that science is mainly a specific way how to communicate and to verify the communication content. This presupposes that there is something called knowledge located in the heads of the actors.
  2. The presupposed knowledge usually is targeting different scopes encoded in different languages. The language enables or delimits meaning and meaning objects can either enable or  delimit a certain language. As part of the society and as exemplars of the homo sapiens species scientists participate in the main behavior tendencies to assimilate majority behavior and majority meanings. This can reduce the realm of knowledge in many ways. Biological life in general is the opposite to physical entropy by generating auotopoietically during the course of time  more and more complexity. This is due to a built-in creativity and the freedom to select. Thus life is always oscillating between conformity and experiment.
  3. The survival of modern societies depends highly on the ability   to communicate with maximal sharing of experience by exploring fast and extensively possible state spaces with their pros and cons. Knowledge must be round the clock visible to all, computablemodular, constructive, in the format of interactive games with transparent rules. Machines should be re-formatted as primarily helping humans, not otherwise around.
  4. To enable such new open and dynamic knowledge spaces one has to redefine computing machines extending the Turing machine (TM) concept to a  world machine (WM) concept which offers several new services for social groups, whole cities or countries. In the future there is no distinction between man and machine because there is a complete symbiotic unification because  the machines have become an integral part of a personality, the extension of the body in some new way; probably  far beyond the cyborg paradigm.
  5. The basic creativity and freedom of biological life has been further developed in a fundamental all embracing spirituality of life in the universe which is targeting a re-creation of the whole universe by using the universe for the universe.