Category Archives: learning

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

eJournal: uffmm.org
ISSN 2567-6458, 19.Juni 2022 – 30.December 2022
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

This text is part of the Philosophy of Science theme within the the uffmm.org blog.

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’.
  3. I have started a ‘book project’ in parallel. This was motivated by the need to provide potential users of our new oksimo.R software with a coherent explanation of how the oksimo.R software, when used, generates an empirical theory in the format of a screenplay. The primary source of the book is in German and will be translated step by step here in the uffmm.blog.

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

[] Melanie Mitchell, What Does It Mean to Align AI With Human Values?, quanta magazin, Quantized Columns, 19.Devember 2022, https://www.quantamagazine.org/what-does-it-mean-to-align-ai-with-human-values-20221213#

Comment by Gerd Doeben-Henisch:

[] Nick Bostrom. Superintelligence. Paths, Dangers, Strategies. Oxford University Press, Oxford (UK), 1 edition, 2014.

[] Scott Aaronson, Reform AI Alignment, Update: 22.November 2022, https://scottaaronson.blog/?p=6821

[] Andrew Y. Ng, Stuart J. Russell, Algorithms for Inverse Reinforcement Learning, ICML 2000: Proceedings of the Seventeenth International Conference on Machine LearningJune 2000 Pages 663–670

[] Pat Langley (ed.), ICML ’00: Proceedings of the Seventeenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., 340 Pine Street, Sixth Floor, San Francisco, CA, United States, Conference 29 June 2000- 2 July 2000, 29.June 2000

[] Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum, (2019) Extrapolating Beyond Suboptimal Demonstrations via
Inverse Reinforcement Learning from Observations
, Proceedings of the 36 th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019. Copyright 2019 by the author(s): https://arxiv.org/pdf/1904.06387.pdf

Abstract: Extrapolating Beyond Suboptimal Demonstrations via
Inverse Reinforcement Learning from Observations
Daniel S. Brown * 1 Wonjoon Goo * 1 Prabhat Nagarajan 2 Scott Niekum 1
You can read in the abstract:
“A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator appear near-optimal, rather than inferring the underlying intentions of the demonstrator that may have been poorly executed in practice. In this paper, we introduce
a novel reward-learning-from-observation algorithm, Trajectory-ranked Reward EXtrapolation (T-REX), that extrapolates beyond a set of (ap-
proximately) ranked demonstrations in order to infer high-quality reward functions from a set of potentially poor demonstrations. When combined
with deep reinforcement learning, T-REX outperforms state-of-the-art imitation learning and IRL methods on multiple Atari and MuJoCo bench-
mark tasks and achieves performance that is often more than twice the performance of the best demonstration. We also demonstrate that T-REX
is robust to ranking noise and can accurately extrapolate intention by simply watching a learner noisily improve at a task over time.”

[] Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, Dario Amodei, (2017), Deep reinforcement learning from human preferences, https://arxiv.org/abs/1706.03741

In the abstract you can read: “For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent’s interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback.

[] Melanie Mitchell,(2021), Abstraction and Analogy-Making in Artificial
Intelligence
, https://arxiv.org/pdf/2102.10717.pdf

In the abstract you can read: “Conceptual abstraction and analogy-making are key abilities underlying humans’ abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike abstractions or analogies. This paper reviews the advantages and limitations of several approaches toward this goal, including symbolic methods, deep learning, and probabilistic program induction. The paper concludes with several proposals for designing
challenge tasks and evaluation measures in order to make quantifiable and generalizable progress

[] Melanie Mitchell, (2021), Why AI is Harder Than We Think, https://arxiv.org/pdf/2102.10717.pdf

In the abstract you can read: “Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.”

[] Stuart Russell, (2019), Human Compatible: AI and the Problem of Control, Penguin books, Allen Lane; 1. Edition (8. Oktober 2019)

In the preface you can read: “This book is about the past , present , and future of our attempt to understand and create intelligence . This matters , not because AI is rapidly becoming a pervasive aspect of the present but because it is the dominant technology of the future . The world’s great powers are waking up to this fact , and the world’s largest corporations have known it for some time . We cannot predict exactly how the technology will develop or on what timeline . Nevertheless , we must plan for the possibility that machines will far exceed the human capacity for decision making in the real world . What then ? Everything civilization has to offer is the product of our intelligence ; gaining access to considerably greater intelligence would be the biggest event in human history . The purpose of the book is to explain why it might be the last event in human history and how to make sure that it is not .”

[] David Adkins, Bilal Alsallakh, Adeel Cheema, Narine Kokhlikyan, Emily McReynolds, Pushkar Mishra, Chavez Procope, Jeremy Sawruk, Erin Wang, Polina Zvyagina, (2022), Method Cards for Prescriptive Machine-Learning Transparency, 2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN), CAIN’22, May 16–24, 2022, Pittsburgh, PA, USA, pp. 90 – 100, Association for Computing Machinery, ACM ISBN 978-1-4503-9275-4/22/05, New York, NY, USA, https://doi.org/10.1145/3522664.3528600

In the abstract you can read: “Specialized documentation techniques have been developed to communicate key facts about machine-learning (ML) systems and the datasets and models they rely on. Techniques such as Datasheets,
AI FactSheets, and Model Cards have taken a mainly descriptive
approach, providing various details about the system components.
While the above information is essential for product developers
and external experts to assess whether the ML system meets their
requirements, other stakeholders might find it less actionable. In
particular, ML engineers need guidance on how to mitigate po-
tential shortcomings in order to fix bugs or improve the system’s
performance. We propose a documentation artifact that aims to
provide such guidance in a prescriptive way. Our proposal, called
Method Cards, aims to increase the transparency and reproducibil-
ity of ML systems by allowing stakeholders to reproduce the models,
understand the rationale behind their designs, and introduce adap-
tations in an informed way. We showcase our proposal with an
example in small object detection, and demonstrate how Method
Cards can communicate key considerations that help increase the
transparency and reproducibility of the detection model. We fur-
ther highlight avenues for improving the user experience of ML
engineers based on Method Cards.”

[] John H. Miller, (2022),  Ex Machina: Coevolving Machines and the Origins of the Social Universe, The SFI Press Scholars Series, 410 pages
Paperback ISBN: 978-1947864429 , DOI: 10.37911/9781947864429

In the announcement of the book you can read: “If we could rewind the tape of the Earth’s deep history back to the beginning and start the world anew—would social behavior arise yet again? While the study of origins is foundational to many scientific fields, such as physics and biology, it has rarely been pursued in the social sciences. Yet knowledge of something’s origins often gives us new insights into the present. In Ex Machina, John H. Miller introduces a methodology for exploring systems of adaptive, interacting, choice-making agents, and uses this approach to identify conditions sufficient for the emergence of social behavior. Miller combines ideas from biology, computation, game theory, and the social sciences to evolve a set of interacting automata from asocial to social behavior. Readers will learn how systems of simple adaptive agents—seemingly locked into an asocial morass—can be rapidly transformed into a bountiful social world driven only by a series of small evolutionary changes. Such unexpected revolutions by evolution may provide an important clue to the emergence of social life.”

[] Stefani A. Crabtree, Global Environmental Change, https://doi.org/10.1016/j.gloenvcha.2022.102597

In the abstract you can read: “Analyzing the spatial and temporal properties of information flow with a multi-century perspective could illuminate the sustainability of human resource-use strategies. This paper uses historical and archaeological datasets to assess how spatial, temporal, cognitive, and cultural limitations impact the generation and flow of information about ecosystems within past societies, and thus lead to tradeoffs in sustainable practices. While it is well understood that conflicting priorities can inhibit successful outcomes, case studies from Eastern Polynesia, the North Atlantic, and the American Southwest suggest that imperfect information can also be a major impediment
to sustainability. We formally develop a conceptual model of Environmental Information Flow and Perception (EnIFPe) to examine the scale of information flow to a society and the quality of the information needed to promote sustainable coupled natural-human systems. In our case studies, we assess key aspects of information flow by focusing on food web relationships and nutrient flows in socio-ecological systems, as well as the life cycles, population dynamics, and seasonal rhythms of organisms, the patterns and timing of species’ migration, and the trajectories of human-induced environmental change. We argue that the spatial and temporal dimensions of human environments shape society’s ability to wield information, while acknowledging that varied cultural factors also focus a society’s ability to act on such information. Our analyses demonstrate the analytical importance of completed experiments from the past, and their utility for contemporary debates concerning managing imperfect information and addressing conflicting priorities in modern environmental management and resource use.”



HMI Analysis for the CM:MI paradigm. Part 2. Problem and Vision

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, February 27-March 16, 2021,
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: March 16, 2021 (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 2: Problem & Vision

Context

This text is preceded by the following texts:

Introduction

Before one starts the HMI analysis  some stakeholder  — in our case are the users stakeholder as well as  users in one role —  have to present some given situation — classifiable as a ‘problem’ — to depart from and a vision as the envisioned goal to be realized.

Here we give a short description of the problem for the CM:MI paradigm and the vision, what should be gained.

Problem: Mankind on the Planet Earth

In this project  the mankind  on the planet earth is  understood as the primary problem. ‘Mankind’ is seen here  as the  life form called homo sapiens. Based on the findings of biological evolution one can state that the homo sapiens has — besides many other wonderful capabilities — at least two extraordinary capabilities:

Outside to Inside

The whole body with the brain is  able to convert continuously body-external  events into internal, neural events. And  the brain inside the body receives many events inside the body as external events too. Thus in the brain we can observe a mixup of body-external (outside 1) and body-internal events (outside 2), realized as set of billions of neural processes, highly interrelated.  Most of these neural processes are unconscious, a small part is conscious. Nevertheless  these unconscious and conscious events are  neurally interrelated. This overall conversion from outside 1 and outside 2 into neural processes  can be seen as a mapping. As we know today from biology, psychology and brain sciences this mapping is not a 1-1 mapping. The brain does all the time a kind of filtering — mostly unconscious — sorting out only those events which are judged by the brain to be important. Furthermore the brain is time-slicing all its sensory inputs, storing these time-slices (called ‘memories’), whereby these time-slices again are no 1-1 copies. The storing of time-sclices is a complex (unconscious) process with many kinds of operations like structuring, associating, abstracting, evaluating, and more. From this one can deduce that the content of an individual brain and the surrounding reality of the own body as well as the world outside the own body can be highly different. All kinds of perceived and stored neural events which can be or can become conscious are  here called conscious cognitive substrates or cognitive objects.

Inside to Outside (to Inside)

Generally it is known that the homo sapiens can produce with its body events which have some impact on the world outside the body.  One kind of such events is the production of all kinds of movements, including gestures, running, grasping with hands, painting, writing as well as sounds by his voice. What is of special interest here are forms of communications between different humans, and even more specially those communications enabled by the spoken sounds of a language as well as the written signs of a language. Spoken sounds as well as written signs are here called expressions associated with a known language. Expressions as such have no meaning (A non-speaker of a language L can hear or see expressions of the language L but he/she/x  never will understand anything). But as everyday experience shows nearly every child  starts very soon to learn which kinds of expressions belong to a language and with what kinds of shared experiences they can be associated. This learning is related to many complex neural processes which map expressions internally onto — conscious and unconscious — cognitive objects (including expressions!). This mapping builds up an internal  meaning function from expressions into cognitive objects and vice versa. Because expressions have a dual face (being internal neural structures as well as being body-outside events by conversions from the inside to body-outside) it is possible that a homo sapiens  can transmit its internal encoding of cognitive objects into expressions from his  inside to the outside and thereby another homo sapiens can perceive the produced outside expression and  can map this outside expression into an intern expression. As far as the meaning function of of the receiving homo sapiens  is sufficiently similar to the meaning function of  the sending homo sapiens there exists some probability that the receiving homo sapiens can activate from its memory cognitive objects which have some similarity with those of  the sending  homo sapiens.

Although we know today of different kinds of animals having some form of language, there is no species known which is with regard to language comparable to  the homo sapiens. This explains to a large extend why the homo sapiens population was able to cooperate in a way, which not only can include many persons but also can stretch through long periods of time and  can include highly complex cognitive objects and associated behavior.

Negative Complexity

In 2006 I introduced the term negative complexity in my writings to describe the fact that in the world surrounding an individual person there is an amount of language-encoded meaning available which is beyond the capacity of an  individual brain to be processed. Thus whatever kind of experience or knowledge is accumulated in libraries and data bases, if the negative complexity is higher and higher than this knowledge can no longer help individual persons, whole groups, whole populations in a constructive usage of all this. What happens is that the intended well structured ‘sound’ of knowledge is turned into a noisy environment which crashes all kinds of intended structures into nothing or badly deformed somethings.

Entangled Humans

From Quantum Mechanics we know the idea of entangled states. But we must not dig into quantum mechanics to find other phenomena which manifest entangled states. Look around in your everyday world. There exist many occasions where a human person is acting in a situation, but the bodily separateness is a fake. While sitting before a laptop in a room the person is communicating within an online session with other persons. And depending from the  social role and the  membership in some social institution and being part of some project this person will talk, perceive, feel, decide etc. with regard to the known rules of these social environments which are  represented as cognitive objects in its brain. Thus by knowledge, by cognition, the individual person is in its situation completely entangled with other persons which know from these roles and rules  and following thereby  in their behavior these rules too. Sitting with the body in a certain physical location somewhere on the planet does not matter in this moment. The primary reality is this cognitive space in the brains of the participating persons.

If you continue looking around in your everyday world you will probably detect that the everyday world is full of different kinds of  cognitively induced entangled states of persons. These internalized structures are functioning like protocols, like scripts, like rules in a game, telling everybody what is expected from him/her/x, and to that extend, that people adhere to such internalized protocols, the daily life has some structure, has some stability, enables planning of behavior where cooperation between different persons  is necessary. In a cognitively enabled entangled state the individual person becomes a member of something greater, becoming a super person. Entangled persons can do things which usually are not possible as long you are working as a pure individual person.[1]

Entangled Humans and Negative Complexity

Although entangled human persons can principally enable more complex events, structures,  processes, engineering, cultural work than single persons, human entanglement is still limited by the brain capacities as well as by the limits of normal communication. Increasing the amount of meaning relevant artifacts or increasing the velocity of communication events makes things even more worse. There are objective limits for human processing, which can run into negative complexity.

Future is not Waiting

The term ‘future‘ is cognitively empty: there exists nowhere an object which can  be called ‘future’. What we have is some local actual presence (the Now), which the body is turning into internal representations of some kind (becoming the Past), but something like a future does not exist, nowhere. Our knowledge about the future is radically zero.

Nevertheless, because our bodies are part of a physical world (planet, solar system, …) and our entangled scientific work has identified some regularities of this physical world which can be bused for some predictions what could happen with some probability as assumed states where our clocks are showing a different time stamp. But because there are many processes running in parallel, composed of billions of parameters which can be tuned in many directions, a really good forecast is not simple and depends from so many presuppositions.

Since the appearance of homo sapiens some hundred thousands years ago in Africa the homo sapiens became a game changer which makes all computations nearly impossible. Not in the beginning of the appearance of the homo sapiens, but in the course of time homo sapiens enlarged its number, improved its skills in more and more areas, and meanwhile we know, that homo sapiens indeed has started to crash more and more  the conditions of its own life. And principally thinking points out, that homo sapiens could even crash more than only planet earth. Every exemplar of a homo sapiens has a built-in freedom which allows every time to decide to behave in a different way (although in everyday life we are mostly following some protocols). And this built-in freedom is guided by actual knowledge, by emotions, and by available resources. The same child can become a great musician, a great mathematician, a philosopher, a great political leader, an engineer, … but giving the child no resources, depriving it from important social contexts,  giving it the wrong knowledge, it can not manifest its freedom in full richness. As human population we need the best out of all children.

Because  the processing of the planet, the solar system etc.  is going on, we are in need of good forecasts of possible futures, beyond our classical concepts of sharing knowledge. This is where our vision enters.

VISION: DEVELOPING TOGETHER POSSIBLE FUTURES

To find possible and reliable shapes of possible futures we have to exploit all experiences, all knowledge, all ideas, all kinds of creativity by using maximal diversity. Because present knowledge can be false — as history tells us –, we should not rule out all those ideas, which seem to be too crazy at a first glance. Real innovations are always different to what we are used to at that time. Thus the following text is a first rough outline of the vision:

  1. Find a format
  2. which allows any kinds of people
  3. for any kind of given problem
  4. with at least one vision of a possible improvement
  5. together
  6. to search and to find a path leading from the given problem (Now) to the envisioned improved state (future).
  7. For all needed communication any kind of  everyday language should be enough.
  8. As needed this everyday language should be extendable with special expressions.
  9. These considerations about possible paths into the wanted envisioned future state should continuously be supported  by appropriate automatic simulations of such a path.
  10. These simulations should include automatic evaluations based on the given envisioned state.
  11. As far as possible adaptive algorithms should be available to support the search, finding and identification of the best cases (referenced by the visions)  within human planning.

REFERENCES or COMMENTS

[1] One of the most common entangled state in daily life is the usage of normal language! A normal language L works only because the rules of usage of this language L are shared by all speaker-hearer of this language, and these rules are explicit cognitive structures (not necessarily conscious, mostly unconscious!).

Continuation

Yes, it will happen 🙂 Here.

 

 

 

 

 

 

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

ISSN 2567-6458, 23.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

Abstract

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

PDF DOCUMENT

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

CASE STUDIES

eJournal: uffmm.org
ISSN 2567-6458, 4.May  – 16.March   2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

In this section several case studies will  be presented. It will be shown, how the DAAI paradigm can be applied to many different contexts . Since the original version of the DAAI-Theory in Jan 18, 2020 the concept has been further developed centering around the concept of a Collective Man-Machine Intelligence [CM:MI] to address now any kinds of experts for any kind of simulation-based development, testing and gaming. Additionally the concept  now can be associated with any kind of embedded algorithmic intelligence [EAI]  (different to the mainstream concept ‘artificial intelligence’). The new concept can be used with every normal language; no need for any special programming language! Go back to the overall framework.

COLLECTION OF PAPERS

There exists only a loosely  order  between the  different papers due to the character of this elaboration process: generally this is an experimental philosophical process. HMI Analysis applied for the CM:MI paradigm.

 

JANUARY 2021 – OCTOBER 2021

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

APRIL 2020 – JANUARY 2021

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

 

 

ACI – TWO DIFFERENT READINGS

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

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

HCI – HMI – AAI ==> ACI ?

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

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

HISTORY

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

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

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

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

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

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

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

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

COGNITION EMBEDDED IN MATTER

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

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

SOLVING A PROBLEM

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

COGNITION AND AUGMENTED COLLECTIVE INTELLIGENCE

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

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

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

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

ACI ANALYSIS PROCESS

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

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

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

ACTOR STORY (AS)

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

SEVERAL TESTS

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

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

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

COLLECTIVE MEMORY

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

 

 

ENGINEERING AND SOCIETY: The Role of Preferences

eJournal: uffmm.org,
ISSN 2567-6458, 4.May 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

FINAL HYPOTHESIS

This suggests that a symbiosis between creative humans and computing algorithms is an attractive pairing. For this we have to re-invent our official  learning processes in schools and universities to train the next generation of humans in a more inspired and creative usage of algorithms in a game-like learning processes.

CONTEXT

The overall context is given by the description of the Actor-Actor Interaction (AAI) paradigm as a whole.  In this text the special relationship between engineering and the surrounding society is in the focus. And within this very broad and rich relationship the main interest lies in the ethical dimension here understood as those preferences of a society which are more supported than others. It is assumed that such preferences manifesting themselves  in real actions within a space of many other options are pointing to hidden values which guide the decisions of the members of a society. Thus values are hypothetical constructs based on observable actions within a cognitively assumed space of possible alternatives. These cognitively represented possibilities are usually only given in a mixture of explicitly stated symbolic statements and different unconscious factors which are influencing the decisions which are causing the observable actions.

These assumptions represent  until today not a common opinion and are not condensed in some theoretical text. Nevertheless I am using these assumptions here because they help to shed some light on the rather complex process of finding a real solution to a stated problem which is rooted in the cognitive space of the participants of the engineering process. To work with these assumptions in concrete development processes can support a further clarification of all these concepts.

ENGINEERING AND SOCIETY

DUAL: REAL AND COGNITIVE

The relationship between an engineering process and the preferences of a society
The relationship between an engineering process and the preferences of a society

As assumed in the AAI paradigm the engineering process is that process which connects the  event of  stating a problem combined with a first vision of a solution with a final concrete working solution.

The main characteristic of such an engineering process is the dual character of a continuous interaction between the cognitive space of all participants of the process with real world objects, actions, and processes. The real world as such is a lose collection of real things, to some extend connected by regularities inherent in natural things, but the visions of possible states, possible different connections, possible new processes is bound to the cognitive space of biological actors, especially to humans as exemplars of the homo sapiens species.

Thus it is a major factor of training, learning, and education in general to see how the real world can be mapped into some cognitive structures, how the cognitive structures can be transformed by cognitive operations into new structures and how these new cognitive structures can be re-mapped into the real world of bodies.

Within the cognitive dimension exists nearly infinite sets of possible alternatives, which all indicate possible states of a world, whose feasibility is more or less convincing. Limited by time and resources it is usually not possible to explore all these cognitively tapped spaces whether and how they work, what are possible side effects etc.

PREFERENCES

Somehow by nature, somehow by past experience biological system — like the home sapiens — have developed   cultural procedures to induce preferences how one selects possible options, which one should be selected, under which circumstances and with even more constraints. In some situations these preferences can be helpful, in others they can  hide possibilities which afterwards can be  re-detected as being very valuable.

Thus every engineering process which starts  a transformation process from some cognitively given point of view to a new cognitively point of view with a following up translation into some real thing is sharing its cognitive space with possible preferences of  the cognitive space of the surrounding society.

It is an open case whether the engineers as the experts have an experimental, creative attitude to explore without dogmatic constraints the   possible cognitive spaces to find new solutions which can improve life or not. If one assumes that there exist no absolute preferences on account of the substantially limit knowledge of mankind at every point of time and inferring from this fact the necessity to extend an actual knowledge further to enable the mastering of an open unknown future then the engineers will try to explore seriously all possibilities without constraints to extend the power of engineering deeper into the heart of the known as well as unknown universe.

EXPLORING COGNITIVE POSSIBILITIES

At the start one has only a rough description of the problem and a rough vision of a wanted solution which gives some direction for the search of an optimal solution. This direction represents also a kind of a preference what is wanted as the outcome of the process.

On account of the inherent duality of human thinking and communication embracing the cognitive space as well as the realm of real things which both are connected by complex mappings realized by the brain which operates  nearly completely unconscious a long process of concrete real and cognitive actions is necessary to materialize cognitive realities within a  communication process. Main modes of materialization are the usage of symbolic languages, paintings (diagrams), physical models, algorithms for computation and simulations, and especially gaming (in several different modes).

As everybody can know  these communication processes are not simple, can be a source of  confusions, and the coordination of different brains with different cognitive spaces as well as different layouts of unconscious factors  is a difficult and very demanding endeavor.

The communication mode gaming is of a special interest here  because it is one of the oldest and most natural modes to learn but in the official education processes in schools and  universities (and in companies) it was until recently not part of the official curricula. But it is the only mode where one can exercise the dimensions of preferences explicitly in combination with an exploring process and — if one wants — with the explicit social dimension of having more than one brain involved.

In the last about 50 – 100 years the term project has gained more and more acceptance and indeed the organization of projects resembles a game but it is usually handled as a hierarchical, constraints-driven process where creativity and concurrent developing (= gaming) is not a main topic. Even if companies allow concurrent development teams these teams are cognitively separated and the implicit cognitive structures are black boxes which can not be evaluated as such.

In the presupposed AAI paradigm here the open creative space has a high priority to increase the chance for innovation. Innovation is the most valuable property in face of an unknown future!

While the open space for a real creativity has to be executed in all the mentioned modes of communication the final gaming mode is of special importance.  To enable a gaming process one has explicitly to define explicit win-lose states. This  objectifies values/ preferences hidden   in the cognitive space before. Such an  objectification makes things transparent, enables more rationality and allows the explicit testing of these defined win-lose states as feasible or not. Only tested hypothesis represent tested empirical knowledge. And because in a gaming mode whole groups or even all members of a social network can participate in a  learning process of the functioning and possible outcome of a presented solution everybody can be included.  This implies a common sharing of experience and knowledge which simplifies the communication and therefore the coordination of the different brains with their unconsciousness a lot.

TESTING AND EVALUATION

Testing a proposed solution is another expression for measuring the solution. Measuring is understood here as a basic comparison between the target to be measured (here the proposed solution) and the before agreed norm which shall be used as point of reference for the comparison.

But what can be a before agreed norm?

Some aspects can be mentioned here:

  1. First of all there is the proposed solution as such, which is here a proposal for a possible assistive actor in an assumed environment for some intended executive actors which has to fulfill some job (task).
  2. Part of this proposed solution are given constraints and non-functional requirements.
  3. Part of this proposed solution are some preferences as win-lose states which have to be reached.
  4. Another difficult to define factor are the executive actors if they are biological systems. Biological systems with their basic built in ability to act free, to be learning systems, and this associated with a not-definable large unconscious realm.

Given the explicit preferences constrained by many assumptions one can test only, whether the invited test persons understood as possible instances of the  intended executive actors are able to fulfill the defined task(s) in some predefined amount of time within an allowed threshold of making errors with an expected percentage of solved sub-tasks together with a sufficient subjective satisfaction with the whole process.

But because biological executive actors are learning systems they  will behave in different repeated  tests differently, they can furthermore change their motivations and   their interests, they can change their emotional commitment, and because of their   built-in basic freedom to act there can be no 100% probability that they will act at time t as they have acted all the time before.

Thus for all kinds of jobs where the process is more or less fixed, where nothing new  will happen, the participation of biological executive actors in such a process is questionable. It seems (hypothesis), that biological executing actors are better placed  in jobs where there is some minimal rate of curiosity, of innovation, and of creativity combined with learning.

If this hypothesis is empirically sound (as it seems), then all jobs where human persons are involved should have more the character of games then something else.

It is an interesting side note that the actual research in robotics under the label of developmental robotics is struck by the problem how one can make robots continuously learning following interesting preferences. Given a preference an algorithm can work — under certain circumstances — often better than a human person to find an optimal solution, but lacking such a preference the algorithm is lost. And actually there exists not the faintest idea how algorithms should acquire that kind of preferences which are interesting and important for an unknown future.

On the contrary, humans are known to be creative, innovative, detecting new preferences etc. but they have only limited capacities to explore these creative findings until some telling endpoint.

This suggests that a symbiosis between creative humans and computing algorithms is an attractive pairing. For this we have to re-invent our official  learning processes in schools and universities to train the next generation of humans in a more inspired and creative usage of algorithms in a game-like learning processes.

 

 

 

 

SIMULATION AND GAMING

eJournal: uffmm.org,
ISSN 2567-6458, 3.May 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

Within the AAI paradigm the following steps will usually be distinguished:

  1. A given problem and a wanted solution.
  2. An assumed context and intended executing and assisting actors.
  3. Assumed non-functional requirements (NFRs).
  4. An actor story (AS) describing at least one task including all the functional requirements.
  5. An usability test, often enhanced with passive or interactive simulations.
  6. An evaluation of the test.
  7. Some repeated improvements.

With these elements one can analyze and design the behavior surface of an  assistive actor which can approach the requirements of the stakeholder.

SIMULATION AND GAMING

Comparing these elements with a (computer) game then one can detect immediately that  a game characteristically allows to win or to lose. The possible win-states or lose-states stop a game. Often the winning state includes additionally  some measures how ‘strong’ or how ‘big’ someone has won or lost a game.

Thus in a game one has besides the rules of the game R which determine what is allowed to do in a game some set of value lables V which indicate some property, some object, some state as associated with some value v,  optionally associated further with some numbers to quantify the value between a maximum and a minimum.

In most board games you will reach an end state where you are the winner or the loser independent of some value. In other games one plays as often as necessary to reach some accumulated value which gives a measure who is better than someone else.

Doing AAI analysis as part of engineering it is usually sufficient to develop an assistive actor with a behavior surface  which satisfies all requirements and some basic needs of the executive actors (the classical users).

But this newly developed product (the assistive actor for certain tasks) will be part of a social situation with different human actors. In these social situations there are often more requirements, demands, emotions around than only the original  design criteria for the technical product.

For some people the aesthetic properties of a technical product can be important or some other cultural code which associates the technical product with these cultural codes making it precious or not.

Furthermore there can be whole processes within which a product can be used or not, making it precious or not.

COLLECTIVE INTELLIGENCE AND AUTOPOIETIC GAMES

In the case of simulations one has already from the beginning a special context given by the experience and the knowledge of the executive actors.  In some cases this knowledge is assumed to be stable or closed. Therefore there is no need to think of the assistive actor as a tool which has not only to support the fulfilling of a defined task but additionally to support the development of the knowledge and experience of the executive actor further. But there are social situations in a city, in an institution, in learning in general where the assumed knowledge and experience is neither complete nor stable. On the contrary in these situations there is a strong need to develop the assumed knowledge further and do this as a joined effort to improve the collective intelligence collectively.

If one sees the models and rules underlying a simulation as a kind of a representation of the assumed collective knowledge then  a simulation can help to visualize this knowledge, make it an experience, explore its different consequences.  And as far as the executive actors are writing the rules of change by themselves, they understand the simulation and they can change the rules to understand better, what can improve the process and possible goal states. This kind of collective development of models and rules as well as testing can be called autopoietic because the executing actors have two roles:(i)  following some rules (which they have defined by themselves) they explore what will happen, when one adheres to these rules; (ii) changing the rules to change the possible outcomes.

This procedure establishes some kind of collective learning within an autopoietic process.

If one enriches this setting with explicit goal states, states of assumed advantages, then one can look at this collective learning as a serious pattern of collective learning by autopoietic games.

For many context like cities, educational institutions, and even companies  this kind of collective learning by autopoietic games can be a very productive way to develop the collective intelligence of many people at the same time gaining knowledge by having some exciting fun.

Autopoietic gaming as support for collective learning processes
Autopoietic gaming as support for collective learning processes

 

 

THE BIG PICTURE: HCI – HMI – AAI in History – Engineering – Society – Philosophy

eJournal: uffmm.org,
ISSN 2567-6458, 20.April 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

A first draft version …

CONTEXT

The context for this text is the whole block dedicated to the AAI (Actor-Actor Interaction)  paradigm. The aim of this text is to give the big picture of all dimensions and components of this subject as it shows up during April 2019.

The first dimension introduced is the historical dimension, because this allows a first orientation in the course of events which lead  to the actual situation. It starts with the early days of real computers in the thirties and forties of the 20 century.

The second dimension is the engineering dimension which describes the special view within which we are looking onto the overall topic of interactions between human persons and computers (or machines or technology or society). We are interested how to transform a given problem into a valuable solution in a methodological sound way called engineering.

The third dimension is the whole of society because engineering happens always as some process within a society.  Society provides the resources which can be used and spends the preferences (values) what is understood as ‘valuable’, as ‘good’.

The fourth dimension is Philosophy as that kind of thinking which takes everything into account which can be thought and within thinking Philosophy clarifies conditions of thinking, possible tools of thinking and has to clarify when some symbolic expression becomes true.

HISTORY

In history we are looking back in the course of events. And this looking back is in a first step guided by the  concepts of HCI (Human-Computer Interface) and  HMI (Human-Machine Interaction).

It is an interesting phenomenon how the original focus of the interface between human persons and the early computers shifted to  the more general picture of interaction because the computer as machine developed rapidly on account of the rapid development of the enabling hardware (HW)  the enabling software (SW).

Within the general framework of hardware and software the so-called artificial intelligence (AI) developed first as a sub-topic on its own. Since the last 10 – 20 years it became in a way productive that it now  seems to become a normal part of every kind of software. Software and smart software seem to be   interchangeable. Thus the  new wording of augmented or collective intelligence is emerging intending to bridge the possible gap between humans with their human intelligence and machine intelligence. There is some motivation from the side of society not to allow the impression that the smart (intelligent) machines will replace some day the humans. Instead one is propagating the vision of a new collective shape of intelligence where human and machine intelligence allows a symbiosis where each side gives hist best and receives a maximum in a win-win situation.

What is revealing about the actual situation is the fact that the mainstream is always talking about intelligence but not seriously about learning! Intelligence is by its roots a static concept representing some capabilities at a certain point of time, while learning is the more general dynamic concept that a system can change its behavior depending from actual external stimuli as well as internal states. And such a change includes real changes of some of its internal states. Intelligence does not communicate this dynamics! The most demanding aspect of learning is the need for preferences. Without preferences learning is impossible. Today machine learning is a very weak example of learning because the question of preferences is not a real topic there. One assumes that some reward is available, but one does not really investigate this topic. The rare research trying to do this job is stating that there is not the faintest idea around how a general continuous learning could happen. Human society is of no help for this problem while human societies have a clash of many, often opposite, values, and they have no commonly accepted view how to improve this situation.

ENGINEERING

Engineering is the art and the science to transform a given problem into a valuable and working solution. What is valuable decides the surrounding enabling society and this judgment can change during the course of time.  Whether some solution is judged to be working can change during the course of time too but the criteria used for this judgment are more stable because of their adherence to concrete capabilities of technical solutions.

While engineering was and is  always  a kind of an art and needs such aspects like creativity, innovation, intuition etc. it is also and as far as possible a procedure driven by defined methods how to do things, and these methods are as far as possible backed up by scientific theories. The real engineer therefore synthesizes art, technology and science in a unique way which can not completely be learned in the schools.

In the past as well as in the present engineering has to happen in teams of many, often many thousands or even more, people which coordinate their brains by communication which enables in the individual brains some kind of understanding, of emerging world pictures,  which in turn guide the perception, the decisions, and the concrete behavior of everybody. And these cognitive processes are embedded — in every individual team member — in mixtures of desires, emotions, as well as motivations, which can support the cognitive processes or obstruct them. Therefore an optimal result can only be reached if the communication serves all necessary cognitive processes and the interactions between the team members enable the necessary constructive desires, emotions, and motivations.

If an engineering process is done by a small group of dedicated experts  — usually triggered by the given problem of an individual stakeholder — this can work well for many situations. It has the flavor of a so-called top-down approach. If the engineering deals with states of affairs where different kinds of people, citizens of some town etc. are affected by the results of such a process, the restriction to  a small group of experts  can become highly counterproductive. In those cases of a widespread interest it seems promising to include representatives of all the involved persons into the executing team to recognize their experiences and their kinds of preferences. This has to be done in a way which is understandable and appreciative, showing esteem for the others. This manner of extending the team of usual experts by situative experts can be termed bottom-up approach. In this usage of the term bottom-up this is not the opposite to top-down but  is reflecting the extend in which members of a society are included insofar they are affected by the results of a process.

SOCIETY

Societies in the past and the present occur in a great variety of value systems, organizational structures, systems of power etc.  Engineering processes within a society  are depending completely on the available resources of a society and of its value systems.

The population dynamics, the needs and wishes of the people, the real territories, the climate, housing, traffic, and many different things are constantly producing demands to be solved if life shall be able and continue during the course of time.

The self-understanding and the self-management of societies is crucial for their ability to used engineering to improve life. This deserves communication and education to a sufficient extend, appropriate public rules of management, otherwise the necessary understanding and the freedom to act is lacking to use engineering  in the right way.

PHILOSOPHY

Without communication no common constructive process can happen. Communication happens according to many  implicit rules compressed in the formula who when can speak how about what with whom etc. Communication enables cognitive processes of for instance  understanding, explanations, lines of arguments.  Especially important for survival is the ability to make true descriptions and the ability to decide whether a statement is true or not. Without this basic ability communication will break down, coordination will break down, life will break down.

The basic discipline to clarify the rules and conditions of true communication, of cognition in general, is called Philosophy. All the more modern empirical disciplines are specializations of the general scope of Philosophy and it is Philosophy which integrates all the special disciplines in one, coherent framework (this is the ideal; actually we are far from this ideal).

Thus to describe the process of engineering driven by different kinds of actors which are coordinating themselves by communication is primarily the task of philosophy with all their sub-disciplines.

Thus some of the topics of Philosophy are language, text, theory, verification of a  theory, functions within theories as algorithms, computation in general, inferences of true statements from given theories, and the like.

In this text I apply Philosophy as far as necessary. Especially I am introducing a new process model extending the classical systems engineering approach by including the driving actors explicitly in the formal representation of the process. Learning machines are included as standard tools to improve human thinking and communication. You can name this Augmented Social Learning Systems (ASLS). Compared to the wording Augmented Intelligence (AI) (as used for instance by the IBM marketing) the ASLS concept stresses that the primary point of reference are the biological systems which created and create machine intelligence as a new tool to enhance biological intelligence as part of biological learning systems. Compared to the wording Collective Intelligence (CI) (as propagated by the MIT, especially by Thomas W.Malone and colleagues) the spirit of the CI concept seems to be   similar, but perhaps only a weak similarity.

AAI THEORY V2 –A Philosophical Framework

eJournal: uffmm.org,
ISSN 2567-6458, 22.February 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: 23.February 2019 (continued the text)

Last change: 24.February 2019 (extended the text)

CONTEXT

In the overview of the AAI paradigm version 2 you can find this section  dealing with the philosophical perspective of the AAI paradigm. Enjoy reading (or not, then send a comment :-)).

THE DAILY LIFE PERSPECTIVE

The perspective of Philosophy is rooted in the everyday life perspective. With our body we occur in a space with other bodies and objects; different features, properties  are associated with the objects, different kinds of relations an changes from one state to another.

From the empirical sciences we have learned to see more details of the everyday life with regard to detailed structures of matter and biological life, with regard to the long history of the actual world, with regard to many interesting dynamics within the objects, within biological systems, as part of earth, the solar system and much more.

A certain aspect of the empirical view of the world is the fact, that some biological systems called ‘homo sapiens’, which emerged only some 300.000 years ago in Africa, show a special property usually called ‘consciousness’ combined with the ability to ‘communicate by symbolic languages’.

General setting of the homo sapiens species (simplified)
Figure 1: General setting of the homo sapiens species (simplified)

As we know today the consciousness is associated with the brain, which in turn is embedded in the body, which  is further embedded in an environment.

Thus those ‘things’ about which we are ‘conscious’ are not ‘directly’ the objects and events of the surrounding real world but the ‘constructions of the brain’ based on actual external and internal sensor inputs as well as already collected ‘knowledge’. To qualify the ‘conscious things’ as ‘different’ from the assumed ‘real things’ ‘outside there’ it is common to speak of these brain-generated virtual things either as ‘qualia’ or — more often — as ‘phenomena’ which are  different to the assumed possible real things somewhere ‘out there’.

PHILOSOPHY AS FIRST PERSON VIEW

‘Philosophy’ has many facets.  One enters the scene if we are taking the insight into the general virtual character of our primary knowledge to be the primary and irreducible perspective of knowledge.  Every other more special kind of knowledge is necessarily a subspace of this primary phenomenological knowledge.

There is already from the beginning a fundamental distinction possible in the realm of conscious phenomena (PH): there are phenomena which can be ‘generated’ by the consciousness ‘itself’  — mostly called ‘by will’ — and those which are occurring and disappearing without a direct influence of the consciousness, which are in a certain basic sense ‘given’ and ‘independent’,  which are appearing  and disappearing according to ‘their own’. It is common to call these independent phenomena ’empirical phenomena’ which represent a true subset of all phenomena: PH_emp  PH. Attention: These empirical phenomena’ are still ‘phenomena’, virtual entities generated by the brain inside the brain, not directly controllable ‘by will’.

There is a further basic distinction which differentiates the empirical phenomena into those PH_emp_bdy which are controlled by some processes in the body (being tired, being hungry, having pain, …) and those PH_emp_ext which are controlled by objects and events in the environment beyond the body (light, sounds, temperature, surfaces of objects, …). Both subsets of empirical phenomena are different: PH_emp_bdy PH_emp_ext = 0. Because phenomena usually are occurring  associated with typical other phenomena there are ‘clusters’/ ‘pattern’ of phenomena which ‘represent’ possible events or states.

Modern empirical science has ‘refined’ the concept of an empirical phenomenon by introducing  ‘standard objects’ which can be used to ‘compare’ some empirical phenomenon with such an empirical standard object. Thus even when the perception of two different observers possibly differs somehow with regard to a certain empirical phenomenon, the additional comparison with an ’empirical standard object’ which is the ‘same’ for both observers, enhances the quality, improves the precision of the perception of the empirical phenomena.

From these considerations we can derive the following informal definitions:

  1. Something is ‘empirical‘ if it is the ‘real counterpart’ of a phenomenon which can be observed by other persons in my environment too.
  2. Something is ‘standardized empirical‘ if it is empirical and can additionally be associated with a before introduced empirical standard object.
  3. Something is ‘weak empirical‘ if it is the ‘real counterpart’ of a phenomenon which can potentially be observed by other persons in my body as causally correlated with the phenomenon.
  4. Something is ‘cognitive‘ if it is the counterpart of a phenomenon which is not empirical in one of the meanings (1) – (3).

It is a common task within philosophy to analyze the space of the phenomena with regard to its structure as well as to its dynamics.  Until today there exists not yet a complete accepted theory for this subject. This indicates that this seems to be some ‘hard’ task to do.

BRIDGING THE GAP BETWEEN BRAINS

As one can see in figure 1 a brain in a body is completely disconnected from the brain in another body. There is a real, deep ‘gap’ which has to be overcome if the two brains want to ‘coordinate’ their ‘planned actions’.

Luckily the emergence of homo sapiens with the new extended property of ‘consciousness’ was accompanied by another exciting property, the ability to ‘talk’. This ability enabled the creation of symbolic languages which can help two disconnected brains to have some exchange.

But ‘language’ does not consist of sounds or a ‘sequence of sounds’ only; the special power of a language is the further property that sequences of sounds can be associated with ‘something else’ which serves as the ‘meaning’ of these sounds. Thus we can use sounds to ‘talk about’ other things like objects, events, properties etc.

The single brain ‘knows’ about the relationship between some sounds and ‘something else’ because the brain is able to ‘generate relations’ between brain-structures for sounds and brain-structures for something else. These relations are some real connections in the brain. Therefore sounds can be related to ‘something  else’ or certain objects, and events, objects etc.  can become related to certain sounds. But these ‘meaning relations’ can only ‘bridge the gap’ to another brain if both brains are using the same ‘mapping’, the same ‘encoding’. This is only possible if the two brains with their bodies share a real world situation RW_S where the perceptions of the both brains are associated with the same parts of the real world between both bodies. If this is the case the perceptions P(RW_S) can become somehow ‘synchronized’ by the shared part of the real world which in turn is transformed in the brain structures P(RW_S) —> B_S which represent in the brain the stimulating aspects of the real world.  These brain structures B_S can then be associated with some sound structures B_A written as a relation  MEANING(B_S, B_A). Such a relation  realizes an encoding which can be used for communication. Communication is using sound sequences exchanged between brains via the body and the air of an environment as ‘expressions’ which can be recognized as part of a learned encoding which enables the receiving brain to identify a possible meaning candidate.

DIFFERENT MODES TO EXPRESS MEANING

Following the evolution of communication one can distinguish four important modes of expressing meaning, which will be used in this AAI paradigm.

VISUAL ENCODING

A direct way to express the internal meaning structures of a brain is to use a ‘visual code’ which represents by some kinds of drawing the visual shapes of objects in the space, some attributes of  shapes, which are common for all people who can ‘see’. Thus a picture and then a sequence of pictures like a comic or a story board can communicate simple ideas of situations, participating objects, persons and animals, showing changes in the arrangement of the shapes in the space.

Pictorial expressions representing aspects of the visual and the auditory sens modes
Figure 2: Pictorial expressions representing aspects of the visual and the auditory sens modes

Even with a simple visual code one can generate many sequences of situations which all together can ‘tell a story’. The basic elements are a presupposed ‘space’ with possible ‘objects’ in this space with different positions, sizes, relations and properties. One can even enhance these visual shapes with written expressions of  a spoken language. The sequence of the pictures represents additionally some ‘timely order’. ‘Changes’ can be encoded by ‘differences’ between consecutive pictures.

FROM SPOKEN TO WRITTEN LANGUAGE EXPRESSIONS

Later in the evolution of language, much later, the homo sapiens has learned to translate the spoken language L_s in a written format L_w using signs for parts of words or even whole words.  The possible meaning of these written expressions were no longer directly ‘visible’. The meaning was now only available for those people who had learned how these written expressions are associated with intended meanings encoded in the head of all language participants. Thus only hearing or reading a language expression would tell the reader either ‘nothing’ or some ‘possible meanings’ or a ‘definite meaning’.

A written textual version in parallel to a pictorial version
Figure 3: A written textual version in parallel to a pictorial version

If one has only the written expressions then one has to ‘know’ with which ‘meaning in the brain’ the expressions have to be associated. And what is very special with the written expressions compared to the pictorial expressions is the fact that the elements of the pictorial expressions are always very ‘concrete’ visual objects while the written expressions are ‘general’ expressions allowing many different concrete interpretations. Thus the expression ‘person’ can be used to be associated with many thousands different concrete objects; the same holds for the expression ‘road’, ‘moving’, ‘before’ and so on. Thus the written expressions are like ‘manufacturing instructions’ to search for possible meanings and configure these meanings to a ‘reasonable’ complex matter. And because written expressions are in general rather ‘abstract’/ ‘general’ which allow numerous possible concrete realizations they are very ‘economic’ because they use minimal expressions to built many complex meanings. Nevertheless the daily experience with spoken and written expressions shows that they are continuously candidates for false interpretations.

FORMAL MATHEMATICAL WRITTEN EXPRESSIONS

Besides the written expressions of everyday languages one can observe later in the history of written languages the steady development of a specialized version called ‘formal languages’ L_f with many different domains of application. Here I am  focusing   on the formal written languages which are used in mathematics as well as some pictorial elements to ‘visualize’  the intended ‘meaning’ of these formal mathematical expressions.

Properties of an acyclic directed graph with nodes (vertices) and edges (directed edges = arrows)
Fig. 4: Properties of an acyclic directed graph with nodes (vertices) and edges (directed edges = arrows)

One prominent concept in mathematics is the concept of a ‘graph’. In  the basic version there are only some ‘nodes’ (also called vertices) and some ‘edges’ connecting the nodes.  Formally one can represent these edges as ‘pairs of nodes’. If N represents the set of nodes then N x N represents the set of all pairs of these nodes.

In a more specialized version the edges are ‘directed’ (like a ‘one way road’) and also can be ‘looped back’ to a node   occurring ‘earlier’ in the graph. If such back-looping arrows occur a graph is called a ‘cyclic graph’.

Directed cyclic graph extended to represent 'states of affairs'
Fig.5: Directed cyclic graph extended to represent ‘states of affairs’

If one wants to use such a graph to describe some ‘states of affairs’ with their possible ‘changes’ one can ‘interpret’ a ‘node’ as  a state of affairs and an arrow as a change which turns one state of affairs S in a new one S’ which is minimally different to the old one.

As a state of affairs I  understand here a ‘situation’ embedded in some ‘context’ presupposing some common ‘space’. The possible ‘changes’ represented by arrows presuppose some dimension of ‘time’. Thus if a node n’  is following a node n indicated by an arrow then the state of affairs represented by the node n’ is to interpret as following the state of affairs represented in the node n with regard to the presupposed time T ‘later’, or n < n’ with ‘<‘ as a symbol for a timely ordering relation.

Example of a state of affairs with a 2-dimensional space configured as a grid with a black and a white token
Fig.6: Example of a state of affairs with a 2-dimensional space configured as a grid with a black and a white token

The space can be any kind of a space. If one assumes as an example a 2-dimensional space configured as a grid –as shown in figure 6 — with two tokens at certain positions one can introduce a language to describe the ‘facts’ which constitute the state of affairs. In this example one needs ‘names for objects’, ‘properties of objects’ as well as ‘relations between objects’. A possible finite set of facts for situation 1 could be the following:

  1. TOKEN(T1), BLACK(T1), POSITION(T1,1,1)
  2. TOKEN(T2), WHITE(T2), POSITION(T2,2,1)
  3. NEIGHBOR(T1,T2)
  4. CELL(C1), POSITION(1,2), FREE(C1)

‘T1’, ‘T2’, as well as ‘C1’ are names of objects, ‘TOKEN’, ‘BACK’ etc. are names of properties, and ‘NEIGHBOR’ is a relation between objects. This results in the equation:

S1 = {TOKEN(T1), BLACK(T1), POSITION(T1,1,1), TOKEN(T2), WHITE(T2), POSITION(T2,2,1), NEIGHBOR(T1,T2), CELL(C1), POSITION(1,2), FREE(C1)}

These facts describe the situation S1. If it is important to describe possible objects ‘external to the situation’ as important factors which can cause some changes then one can describe these objects as a set of facts  in a separated ‘context’. In this example this could be two players which can move the black and white tokens and thereby causing a change of the situation. What is the situation and what belongs to a context is somewhat arbitrary. If one describes the agriculture of some region one usually would not count the planets and the atmosphere as part of this region but one knows that e.g. the sun can severely influence the situation   in combination with the atmosphere.

Change of a state of affairs given as a state which will be enhanced by a new object
Fig.7: Change of a state of affairs given as a state which will be enhanced by a new object

Let us stay with a state of affairs with only a situation without a context. The state of affairs is     a ‘state’. In the example shown in figure 6 I assume a ‘change’ caused by the insertion of a new black token at position (2,2). Written in the language of facts L_fact we get:

  1. TOKEN(T3), BLACK(T3), POSITION(2,2), NEIGHBOR(T3,T2)

Thus the new state S2 is generated out of the old state S1 by unifying S1 with the set of new facts: S2 = S1 {TOKEN(T3), BLACK(T3), POSITION(2,2), NEIGHBOR(T3,T2)}. All the other facts of S1 are still ‘valid’. In a more general manner one can introduce a change-expression with the following format:

<S1, S2, add(S1,{TOKEN(T3), BLACK(T3), POSITION(2,2), NEIGHBOR(T3,T2)})>

This can be read as follows: The follow-up state S2 is generated out of the state S1 by adding to the state S1 the set of facts { … }.

This layout of a change expression can also be used if some facts have to be modified or removed from a state. If for instance  by some reason the white token should be removed from the situation one could write:

<S1, S2, subtract(S1,{TOKEN(T2), WHITE(T2), POSITION(2,1)})>

Another notation for this is S2 = S1 – {TOKEN(T2), WHITE(T2), POSITION(2,1)}.

The resulting state S2 would then look like:

S2 = {TOKEN(T1), BLACK(T1), POSITION(T1,1,1), CELL(C1), POSITION(1,2), FREE(C1)}

And a combination of subtraction of facts and addition of facts would read as follows:

<S1, S2, subtract(S1,{TOKEN(T2), WHITE(T2), POSITION(2,1)}, add(S1,{TOKEN(T3), BLACK(T3), POSITION(2,2)})>

This would result in the final state S2:

S2 = {TOKEN(T1), BLACK(T1), POSITION(T1,1,1), CELL(C1), POSITION(1,2), FREE(C1),TOKEN(T3), BLACK(T3), POSITION(2,2)}

These simple examples demonstrate another fact: while facts about objects and their properties are independent from each other do relational facts depend from the state of their object facts. The relation of neighborhood e.g. depends from the participating neighbors. If — as in the example above — the object token T2 disappears then the relation ‘NEIGHBOR(T1,T2)’ no longer holds. This points to a hierarchy of dependencies with the ‘basic facts’ at the ‘root’ of a situation and all the other facts ‘above’ basic facts or ‘higher’ depending from the basic facts. Thus ‘higher order’ facts should be added only for the actual state and have to be ‘re-computed’ for every follow-up state anew.

If one would specify a context for state S1 saying that there are two players and one allows for each player actions like ‘move’, ‘insert’ or ‘delete’ then one could make the change from state S1 to state S2 more precise. Assuming the following facts for the context:

  1. PLAYER(PB1), PLAYER(PW1), HAS-THE-TURN(PB1)

In that case one could enhance the change statement in the following way:

<S1, S2, PB1,insert(TOKEN(T3,2,2)),add(S1,{TOKEN(T3), BLACK(T3), POSITION(2,2)})>

This would read as follows: given state S1 the player PB1 inserts a  black token at position (2,2); this yields a new state S2.

With or without a specified context but with regard to a set of possible change statements it can be — which is the usual case — that there is more than one option what can be changed. Some of the main types of changes are the following ones:

  1. RANDOM
  2. NOT RANDOM, which can be specified as follows:
    1. With PROBABILITIES (classical, quantum probability, …)
    2. DETERMINISTIC

Furthermore, if the causing object is an actor which can adapt structurally or even learn locally then this actor can appear in some time period like a deterministic system, in different collected time periods as an ‘oscillating system’ with different behavior, or even as a random system with changing probabilities. This make the forecast of systems with adaptive and/ or learning systems rather difficult.

Another aspect results from the fact that there can be states either with one actor which can cause more than one action in parallel or a state with multiple actors which can act simultaneously. In both cases the resulting total change has eventually to be ‘filtered’ through some additional rules telling what  is ‘possible’ in a state and what not. Thus if in the example of figure 6 both player want to insert a token at position (2,2) simultaneously then either  the rules of the game would forbid such a simultaneous action or — like in a computer game — simultaneous actions are allowed but the ‘geometry of a 2-dimensional space’ would not allow that two different tokens are at the same position.

Another aspect of change is the dimension of time. If the time dimension is not explicitly specified then a change from some state S_i to a state S_j does only mark the follow up state S_j as later. There is no specific ‘metric’ of time. If instead a certain ‘clock’ is specified then all changes have to be aligned with this ‘overall clock’. Then one can specify at what ‘point of time t’ the change will begin and at what point of time t*’ the change will be ended. If there is more than one change specified then these different changes can have different timings.

THIRD PERSON VIEW

Up until now the point of view describing a state and the possible changes of states is done in the so-called 3rd-person view: what can a person perceive if it is part of a situation and is looking into the situation.  It is explicitly assumed that such a person can perceive only the ‘surface’ of objects, including all kinds of actors. Thus if a driver of a car stears his car in a certain direction than the ‘observing person’ can see what happens, but can not ‘look into’ the driver ‘why’ he is steering in this way or ‘what he is planning next’.

A 3rd-person view is assumed to be the ‘normal mode of observation’ and it is the normal mode of empirical science.

Nevertheless there are situations where one wants to ‘understand’ a bit more ‘what is going on in a system’. Thus a biologist can be  interested to understand what mechanisms ‘inside a plant’ are responsible for the growth of a plant or for some kinds of plant-disfunctions. There are similar cases for to understand the behavior of animals and men. For instance it is an interesting question what kinds of ‘processes’ are in an animal available to ‘navigate’ in the environment across distances. Even if the biologist can look ‘into the body’, even ‘into the brain’, the cells as such do not tell a sufficient story. One has to understand the ‘functions’ which are enabled by the billions of cells, these functions are complex relations associated with certain ‘structures’ and certain ‘signals’. For this it is necessary to construct an explicit formal (mathematical) model/ theory representing all the necessary signals and relations which can be used to ‘explain’ the obsrvable behavior and which ‘explains’ the behavior of the billions of cells enabling such a behavior.

In a simpler, ‘relaxed’ kind of modeling  one would not take into account the properties and behavior of the ‘real cells’ but one would limit the scope to build a formal model which suffices to explain the oservable behavior.

This kind of approach to set up models of possible ‘internal’ (as such hidden) processes of an actor can extend the 3rd-person view substantially. These models are called in this text ‘actor models (AM)’.

HIDDEN WORLD PROCESSES

In this text all reported 3rd-person observations are called ‘actor story’, independent whether they are done in a pictorial or a textual mode.

As has been pointed out such actor stories are somewhat ‘limited’ in what they can describe.

It is possible to extend such an actor story (AS)  by several actor models (AM).

An actor story defines the situations in which an actor can occur. This  includes all kinds of stimuli which can trigger the possible senses of the actor as well as all kinds of actions an actor can apply to a situation.

The actor model of such an actor has to enable the actor to handle all these assumed stimuli as well as all these actions in the expected way.

While the actor story can be checked whether it is describing a process in an empirical ‘sound’ way,  the actor models are either ‘purely theoretical’ but ‘behavioral sound’ or they are also empirically sound with regard to the body of a biological or a technological system.

A serious challenge is the occurrence of adaptiv or/ and locally learning systems. While the actor story is a finite  description of possible states and changes, adaptiv or/ and locally learning systeme can change their behavior while ‘living’ in the actor story. These changes in the behavior can not completely be ‘foreseen’!

COGNITIVE EXPERT PROCESSES

According to the preceding considerations a homo sapiens as a biological system has besides many properties at least a consciousness and the ability to talk and by this to communicate with symbolic languages.

Looking to basic modes of an actor story (AS) one can infer some basic concepts inherently present in the communication.

Without having an explicit model of the internal processes in a homo sapiens system one can infer some basic properties from the communicative acts:

  1. Speaker and hearer presuppose a space within which objects with properties can occur.
  2. Changes can happen which presuppose some timely ordering.
  3. There is a disctinction between concrete things and abstract concepts which correspond to many concrete things.
  4. There is an implicit hierarchy of concepts starting with concrete objects at the ‘root level’ given as occurence in a concrete situation. Other concepts of ‘higher levels’ refer to concepts of lower levels.
  5. There are different kinds of relations between objects on different conceptual levels.
  6. The usage of language expressions presupposes structures which can be associated with the expressions as their ‘meanings’. The mapping between expressions and their meaning has to be learned by each actor separately, but in cooperation with all the other actors, with which the actor wants to share his meanings.
  7. It is assume that all the processes which enable the generation of concepts, concept hierarchies, relations, meaning relations etc. are unconscious! In the consciousness one can  use parts of the unconscious structures and processes under strictly limited conditions.
  8. To ‘learn’ dedicated matters and to be ‘critical’ about the quality of what one is learnig requires some disciplin, some learning methods, and a ‘learning-friendly’ environment. There is no guaranteed method of success.
  9. There are lots of unconscious processes which can influence understanding, learning, planning, decisions etc. and which until today are not yet sufficiently cleared up.

 

 

 

 

 

 

 

 

AAI THEORY V2 – USABILITY AND USEFULNESS

eJournal: uffmm.org
ISSN 2567-6458, 4.February 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

REMARK (5.May 2019)

This text  has to be reviewed again on account of the new aspect of gaming as  discussed in the post Engineering and Society.

CONTEXT

An overview of the enhanced AAI theory  version 2 you can find here.  In this post we talk about the sixth chapter dealing with usability and usefulness.

USABILITY AND USEFULNESS

In the AAI paradigm the concept of usability is seen as a sub-topic of the more broader concept of usefulness. Furthermore Usefulness  as well as usability are understood as measurements comparing some target with some presupposed norm.

Example: If someone wants to buy a product A whose prize fits well with the available budget and this product A shows only  an average usability then the product is probably ‘more useful’ for the buyer than another product B which does not fit with the budget although it  has a better usability. A conflict can  arise if the weaker value of the usability of product A causes during the usage of product A ‘bad effects’ onto the user of product A which in turn produce additional negative costs which enhance the original ‘nice price’ to a degree where the product A becomes finally  ‘more costly’ than product B.

Therefore  the concept usefulness will be  defined independently from the concept usability and depends completely  from the person or company who is searching for the solution of a problem. The concept of usability depends directly on the real structure of an  actor, a biological one or a non-biological one. Thus independent of the definition of the actual usefulness the given structure of an actor implies certain capabilities with regard to input, output as well as to  internal   processing. Therefore if an X seems to be highly useful for someone and to get X  needs a certain actor story to become realized with certain actors then it can matter whether this process includes a ‘good usability’ for the participating actors or not.

In the AAI paradigm both concepts usefulness as well as usability will be analyzed to provide a  chance to check the contributions of both concepts  in some predefined duration of usage. This allows the analysis of the sustainability of the wanted usefulness restricted to  usability as a parameter. There can be even more parameters   included in the evaluation of the actor story  to enhance the scope of   sustainability. Depending from the definition of the concept of resilience one can interpret the concept of sustainability used in this AAI paradigm as compatible with the resilience concept too.

MEASUREMENT

To speak about ‘usefulness’, ‘usability’, ‘sustainability’ (or ‘resilience’) requires some kind of a scale of values with an   ordering relation R allowing to state about  some values x,y   whether R(x,y) or R(y,x) or EQUAL(x,y). The values used in the scale have to be generated by some defined process P which is understood as a measurement process M which basically compares some target X with some predefined norm N and gives as a result a pair (v,N) telling a number v associated with the applied norm N. Written: M : X x N —> V x N.

A measurement procedure M must be transparent and repeatable in the sense that the repeated application of the measurement procedure M will generate the same results than before. Associated with the measurement procedure there can exist many additional parameters like ‘location’, ‘time’, ‘temperature’, ‘humidity’,  ‘used technologies’, etc.

Because there exist targets X which are not static it can be a problem when and how often one has to measure these targets to get some reliable value. And this problem becomes even worse if the target includes adaptive systems which are changing constantly like in the case of  biological systems.

All biological systems have some degree of learnability. Thus if a human actor is acting as part of an actor story  the human actor will learn every time he is working through the process. Thus making errors during his first run of the process does not imply that he will repeat these errors the next time. Usually one can observe a learning curve associated with n-many runs which show — mostly — a decrease in errors, a decrease in processing time, and — in general — a change of all parameters, which can be measured. Thus a certain actor story can receive a good usability value after a defined number of usages.  But there are other possible subjective parameters like satisfaction, being excited, being interested and the like which can change in the opposite direction, because to become well adapted to  the process can be boring which in turn can lead to less concentrations with many different negative consequences.

 

 

 

 

ACTOR-ACTOR INTERACTION [AAI] WITHIN A SYSTEMS ENGINEERING PROCESS (SEP). An Actor Centered Approach to Problem Solving

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

ATTENTION: The actual Version  you will find HERE.

Draft version 22.June 2018

Update 26.June 2018 (Chapter AS-AM Summary)

Update 4.July 2018 (Chapter 4 Actor Model; improving the terminology of environments with actors, actors as input-output systems, basic and real interface, a first typology of input-output systems…)

Update 17.July 2018 (Preface, Introduction new)

Update 19.July 2018 (Introduction final paragraph!, new chapters!)

Update 20.July 2018 (Disentanglement of chapter ‘Simulation & Verification’ into two independent chapters; corrections in the chapter ‘Introduction’; corrections in chapter ‘AAI Analysis’; extracting ‘Simulation’ from chapter ‘Actor Story’ to new chapter ‘Simulation’; New chapter ‘Simulation’; Rewriting of chapter ‘Looking Forward’)

Update 22.July 2018 (Rewriting the beginning of the chapter ‘Actor Story (AS)’, not completed; converting chapter ‘AS+AM Summary’ to ‘AS and AM Philosophy’, not completed)

Update 23.July 2018 (Attaching a new chapter with a Case Study illustrating an actor story (AS). This case study is still unfinished. It is a case study of  a real project!)

Update 7.August 2018 (Modifying chapter Actor Story, the introduction)

Update 8.August 2018 (Modifying chapter  AS as Text, Comic, Graph; especially section about the textual mode and the pictorial mode; first sketch for a mapping from the textual mode into the pictorial mode)

Update 9.August 2018 (Modification of the section ‘Mathematical Actor Story (MAS) in chapter 4).

Update 11.August 2018 (Improving chapter 3 ‘Actor Story; nearly complete rewriting of chapter 4 ‘AS as text, comic, graph’.)

Update 12.August 2018 (Minor corrections in the chapters 3+4)

Update 13.August 2018 (I am still catched by the chapters 3+4. In chapter  the cognitive structure of the actors has been further enhanced; in chapter 4 a complete example of a mathematical actor story could now been attached.)

Update 14.August 2018 (minor corrections to chapter 4 + 5; change-statements define for each state individual combinatorial spaces (a little bit like a quantum state); whether and how these spaces will be concretized/ realized depends completely from the participating actors)

Update 15.August 2018 (Canceled the appendix with the case study stub and replaced it with an overview for  a supporting software tool which is needed for the real usage of this theory. At the moment it is open who will write the software.)

Update 2.October 2018 (Configuring the whole book now with 3 parts: I. Theory, II. Application, III. Software. Gerd has his focus on part I, Zeynep will focus on part II and ‘somebody’ will focus on part III (in the worst case we will — nevertheless — have a minimal version :-)). For a first quick overview about everything read the ‘Preface’ and the ‘Introduction’.

Update 4.November 2018 (Rewriting the Introduction (and some minor corrections in the Preface). The idea of the rewriting was to address all the topics which will be discussed in the book and pointing out to the logical connections between them. This induces some wrong links in the following chapters, which are not yet updated. Some chapters are yet completely missing. But to improve the clearness of the focus and the logical inter-dependencies helps to elaborate the missing texts a lot. Another change is the wording of the title. Until now it is difficult to find a title which is exactly matching the content. The new proposal shows the focus ‘AAI’ but lists the keywords of the main topics within AAA analysis because these topics are usually not necessarily associated with AAI.)

ACTOR-ACTOR INTERACTION [AAI]. An Actor Centered Approach to Problem Solving. Combining Engineering and Philosophy

by

GERD DOEBEN-HENISCH in cooperation with  LOUWRENCE ERASMUS, ZEYNEP TUNCER

LATEST  VERSION AS PDF

BACKGROUND INFORMATION 19.Dec.2018: Application domain ‘Communal Planning and e-Gaming’

BACKGROUND INFORMATION 24.Dec.2018: The AAI-paradigm and Quantum Logic

PRE-VIEW: NEW EXPANDED AAI THEORY 23.January 2019: Outline of the new expanded  AAI Paradigm. Before re-writing the main text with these ideas the new advanced AAI theory will first be tested during the summer 2019 within a lecture with student teams as well as in  several workshops outside the Frankfurt University of Applied Sciences with members of different institutions.

AASE – Actor-Actor Systems Engineering. Theory & Applications. Micro-Edition (Vers.9)

eJournal: uffmm.org, ISSN 2567-6458
13.June  2018
Email: info@uffmm.org
Authors: Gerd Doeben-Henisch, Zeynep Tuncer,  Louwrence Erasmus
Email: doeben@fb2.fra-uas.de
Email: gerd@doeben-henisch.de

PDF

CONTENTS

1 History: From HCI to AAI …
2 Different Views …
3 Philosophy of the AAI-Expert …
4 Problem (Document) …
5 Check for Analysis …
6 AAI-Analysis …
6.1 Actor Story (AS) . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.1 Textual Actor Story (TAS) . . . . . . . . . . . . . . .
6.1.2 Pictorial Actor Story (PAT) . . . . . . . . . . . . . .
6.1.3 Mathematical Actor Story (MAS) . . . . . . . . . . .
6.1.4 Simulated Actor Story (SAS) . . . . . . . . . . . . .
6.1.5 Task Induced Actor Requirements (TAR) . . . . . . .
6.1.6 Actor Induced Actor Requirements (UAR) . . . . . .
6.1.7 Interface-Requirements and Interface-Design . . . .
6.2 Actor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.1 Actor and Actor Story . . . . . . . . . . . . . . . . .
6.2.2 Actor Model . . . . . . . . . . . . . . . . . . . . . .
6.2.3 Actor as Input-Output System . . . . . . . . . . . .
6.2.4 Learning Input-Output Systems . . . . . . . . . . . .
6.2.5 General AM . . . . . . . . . . . . . . . . . . . . . .
6.2.6 Sound Functions . . . . . . . . . . . . . . . . . . .
6.2.7 Special AM . . . . . . . . . . . . . . . . . . . . . .
6.2.8 Hypothetical Model of a User – The GOMS Paradigm
6.2.9 Example: An Electronically Locked Door . . . . . . .
6.2.10 A GOMS Model Example . . . . . . . . . . . . . . .
6.2.11 Further Extensions . . . . . . . . . . . . . . . . . .
6.2.12 Design Principles; Interface Design . . . . . . . . .
6.3 Simulation of Actor Models (AMs) within an Actor Story (AS) .
6.4 Assistive Actor-Demonstrator . . . . . . . . . . . . . . . . . .
6.5 Approaching an Optimum Result . . . . .
7 What Comes Next: The Real System
7.1 Logical Design, Implementation, Validation . . . .
7.2 Conceptual Gap In Systems Engineering? . . .
8 The AASE-Paradigm …
References

Abstract

This text is based on the the paper “AAI – Actor-Actor Interaction. A Philosophy of Science View” from 3.Oct.2017 and version 11 of the paper “AAI – Actor-Actor Interaction. An Example Template” and it   transforms these views in the new paradigm ‘Actor- Actor Systems Engineering’ understood as a theory as well as a paradigm for and infinite set of applications. In analogy to the slogan ’Object-Oriented Software Engineering (OO SWE)’ one can understand the new acronym AASE as a systems engineering approach where the actor-actor interactions are the base concepts for the whole engineering process. Furthermore it is a clear intention to view the topic AASE explicitly from the point of view of a theory (as understood in Philosophy of Science) as well as from the point of view of possible applications (as understood in systems engineering). Thus the classical term of Human-Machine Interaction (HMI) or even the older Human-Computer Interaction (HCI) is now embedded within the new AASE approach. The same holds for the fuzzy discipline of Artificial Intelligence (AI) or the subset of AI called Machine Learning (ML). Although the AASE-approach is completely in its beginning one can already see how powerful this new conceptual framework  is.

 

 

ACTOR-ACTOR INTERACTION. Philosophy of the Actor

eJournal: uffmm.org, ISSN 2567-6458
16.March 2018
Email: info@uffmm.org
Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de
Frankfurt University of Applied Sciences (FRA-UAS)
Institut for New Media (INM, Frankfurt)

PDF

CONTENTS

I   A Vision as a Problem to be Solved … 1
II   Language, Meaning & Ontology …  2
     II-A   Language Levels . . . . . . . . .  . . 2
     II-B  Common Empirical Matter .  . . . . . 2
     II-C   Perceptual Levels . . . . . . .  . . . . 3
     II-D   Space & Time . . . . . . . .  . . . . . 4
     II-E    Different Language Modes . . . 4
     II-F    Meaning of Expressions & Ontology … 4
     II-G   True Expressions . . . . . . .  . . . .  5
     II-H   The Congruence of Meaning  . . . .  5
III   Actor Algebra … 6
IV   World Algebra  … 7
V    How to continue … 8
VI References … 8

Abstract

As preparation for this text one should read the chapter about the basic layout of an Actor-Actor Analysis (AAA) as part of an systems engineering process (SEP). In this text it will be described which internal conditions one has to assume for an actor who uses a language to talk about his observations oft he world to someone else in a verifiable way. Topics which are explained in this text are e.g. ’language’,’meaning’, ’ontology’, ’consciousness’, ’true utterance’, ’synonymous expression.

INTELLIGENT MACHINES – INTRODUCTION

eJournal: uffmm.org,
ISSN 2567-6458, 09.Oct 2017 – April 9, 2022, 13:30 h
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

 Remark April 2022

This post from Oct 2017 will be reviewed in the new conceptual framework of an Applied Empirical Theory [AET] with an additional Dynamic Format [DF]. For more details see HERE.

OVERVIEW

A short story telling You, (i) how we interface the intelligent machines (IM) part with the actor-actor interaction (AAI) part, (ii) a first working definition of intelligent machines (IM) in this text, and (iii) defining intelligence and how one can this measure.

IM WITHIN AAI

In this blog we see IM not isolated, as a stand alone endeavor, but as embedded in a discipline called actor-actor interaction (AAI)(later called DAAI := Distributed Actor Actor Interaction).  AAI investigates complex tasks and looks how different kinds of actors are interacting in these contexts with technical systems. As far as the participating systems have been technical systems one speaks here of a system interface (SI) as that part of a technical system, which is interacting with the human actor. In the case of biological systems (mostly humans, but it could be animals as well), one speaks of the user interface (UI). In this text we generalize both cases by the general concept of an actor — biological and non-biological –, which has some actor interface (ActI), and this actor interface embraces all properties which are relevant for the interactions of the actor.

For the analysis of the behavior of actors in such task-environments one can distinguish two important concepts: the actor story (AS) describing the context as an observable process, as well as different actor models (AM). Actor models are special extensions of an actor story because an actor model describes the observable behavior of actors as a behavior function (BF) with a set of assumptions about possible internal states of the actors. The assumptions about possible internal states (IS) are either completely arbitrary or empirically motivated.

The embedding of IM within AAI can be realized through the concept of an actor model (UM) and the actor story (AS). Whatever is important for something which is called an intelligent machine application (IMA) can be defined as an actor model within an actor story. This embedding of IM within AAI offers many advantages.

This has to be explained with some more details.

An Intelligent Machine (IM) in an Actor Story

Let us assume that there exists a mathematical-graph representation of an actor story written as AS_{L_{ε}}. Such a graph has nodes which represent situations. Formally these are sets of properties, probably more fine-grained by subsets which represent different kinds of actors embedded in this situation as well as different kinds of non-actors.

Actors can be classified (as introduced above) as either biological actors (BA) or non-biological actors (NBA). Both kinds of actors can — in another reading — be subsumed under the general term of input-output-systems (IO-SYS). An input-output system can be a learning system or non-learning. Another basic property is that of being intelligent or non-intelligent. Being a learning system and being an intelligent system is usually strongly connected, but this must not necessarily be so. Being a learning system can be associated with being non-intelligent and being intelligent can be connected with being non-learning.(cf. Figure 1)

Classification of input-output systems according to learning, intelligence and beeing biological or not biological
Classification of input-output systems according to learning, intelligence and being biological or non-biological

While biological systems are always learning and intelligent, one can find non-biological systems of all types: non-learning and non-intelligent, non-intelligent and learning, non-learning and intelligent, and learning and intelligent.

Learning System

To classify a system as a learning system this requires the general ability to change the behavior of this system in time thus that there exists a time-span (t1,t2) after which the behavior as response to  certain critical stimuli has changed compared to the time before. [1] From this requirement it follows, that a learning system is an input-output system with at least one internal state which can change. Thus we have the general assumption:

Def: Learning System (LS)

  1. LS(x) iff
  2. x=<I, O, IS, phi >
  3. φ : I x IS —> IS x O
  4. I := Input
  5. O := Output
  6. IS := Internal states

Some x is a learning system (LS) if it is a structure containing sets for input (I), Output (O), as well as internal states (IS). These sets are operated by a behavior function φ which maps inputs and actual internal states to output as well as back to internal states. The set of possible learning functions is infinite.

Intelligent System

The term ‘intelligent’ and ‘intelligence’ is until now not standardized. This means that everybody is using it at little bit arbitrarily.

In this text we take the basic idea of a scientific usage of the term ‘intelligence’ from experimental psychology, which has developed clearly defined operational concepts since the end of the 19. Century which have been proved as quite stable in their empirical applications. [2a,b,c] 

The central idea of the psychological concept of the usage of the term ‘intelligence’ is to associate the usage of the term ‘intelligence’ with observable behavior of those actors, which shall be classified according defined methods of measurement.

In the case of experimental psychology the actors have been biological systems, mainly humans, in the first years of the research school children of certain ages. Because nobody did know what ‘intelligence’ means ‘as such’ one agreed to accept the observable behavior of children in certain task environments as ‘manifestations’ of a ‘presupposed unknown intelligence’. Thus the ability of children to solve defined tasks in a certain defined manner became a norm for what is called ‘intelligence’. Solving the tasks in a certain time with less than a certain amount of errors was used as a ‘baseline’ and all behavior deviating from the baseline was ‘better’ or ‘poorer’.

Thus the ‘content’ of the ‘meaning’ of the term ‘intelligence’ has been delegated to historical patterns of behavior which were common in a certain time-span in a certain geographical and cultural region.

While these behavior patterns can change during the course of time the general method of measurement is invariant.

In the time since then experimental psychology has modified and elaborated this first concept in some directions.

One direction is the modification of the kind of tasks which are used for the tests. With regard to the cultural context one has modified the content, thereby looking to find such kinds of task which seem to be ‘invariant’ with regard to the presupposed intelligence factor. This is an ongoing process.

The other direction is the focus on the actors as such. Because biological systems like humans change the development of their intelligence with age one has tried to find out ‘typical tasks for every age’. This too is an ongoing process.

This history of experimental psychology gives very interesting examples how one can approach the problem of the usage and the measurement of some X which we call ‘intelligence’.

In the context of an AAI-approach we have not only biological systems, but also non-biological systems. Thus most of the elaborated parameters of psychology for human actors are not general enough.

One possible strategy to generalize the intelligence-paradigm of experimental psychology could be to ‘free’ the selection of task sets from the narrow human cultures of the past and require only ‘clearly defined task sets with defined interfaces and defined contexts’. All these tasks sets can be arranged either in one super-set or in a parameterized field of sets. The sum of all these sets defines then a space of possible behavior and associated with this a space of possible measurable intelligence.

A task has then to be given as an actor story according to the AAI-paradigm. Such a specified actor story allows the formal definition of a complexity measure which can be used to measure the ‘amount of intelligence necessary to solve such a task’.

With such a more general and extendable approach to the measurement of observable intelligence one can compare all kinds of systems with each other. With such an approach one can further show objectively, where biological and non-biological systems differ generally, where they are similar, and to which extend they differ with regard to concrete circumstances.

Measuring Intelligence by Actor Stories

Presupposing actor stories (AS) (ideally formalized as mathematical graphs) on can define a first operational general measurement of intelligence.

Def: Task-Intelligence of a task τ (TInt(τ))

    1. Every defined task τ represents a graph g with one shortest path pmin(τ)= π_{min} from a start node to a goal node.
    2. Every such shortest path π_{min} has a certain number of nodes path-nodes(π_{min})=ν.
    3. The number of solved nodes (ν_{solved}) can become related against the total number of nodes ν as ν_{solved}/ν. We take TInt(τ)= ν_{solved}/ν. It follows that TInt(τ) is between 0 and 1: 0 ≤ TInt(τ)≤ 1.
    4. To every task  a maximal duration Δ_{max} is attached; all nodes which are solved within this maximal duration time Δ_{max} are declared as ‘solved’, all the others as ‘un-solved’.

The usual case will require more than one task to be realized. Thus we introduce the concept of a task field (TF).

Def: Task-Field of type x (TF_{x})
Def: Task-Field Intelligence (TFInt)

A task-field TF of type x includes a finite set of individual tasks like TF_{x} = { τ{x.1}, τ{x.2}, … , τ{x.n} } with n ≥ 2. The sum of all individual task intelligence values TInt(τ{x.i}) has to be normalized to 1, i.e. (TInt(τ{x.1}) + TInt(τ{x.2}) + … + TInt(τ{x.n}))/ n (with 0 in the nominator not allowed). Thus the value of the intelligence of a task field of type x TFInt(TF_{x}) is again in the domain of [0,1].

Because the different tasks in a task field TF can be of different difficulty it should be possible to introduce some weighting for the individual task intelligence values. This should not change the general mechanism.

Def: Combined Task-Fields (TF)

In face of the huge variety of possible task fields in this world it can make sens to introduce more general layers by grouping task fields of different types together to larger combined fields, like TF_{x,…,z} = TF_{x} ∪ TF_{y} ∪ … ∪ TF_{z}. The task field intelligence TFInt of such combined task fields would be computed as before.

Def: Omega Task-Field at time t (TF_{ω}(t))

The most comprehensive assembly of such combinations shall here be called the Omega-Task-Field at time t TF_{ω}(t). This indicates the known maximum of intelligence measurements at that point of time.

Measurement Comments

With these assumptions the term intelligence will be restricted to clearly defined domains either to an individual task, to a task-field of type x, or to some grouped task-fields or being related to the actual omega task-field. In every such domain the intelligence value is in the realm of [0,1] or written as some value between 0 or 100%.

Independent of the type of an actor — biological or not — one can measure the intelligence of such an actor with the same domains of defined tasks. As a result one can easily compare all known actors with regard to such defined task domains.

Because the acting actors can be quite different by their input-output capabilities it follows that every actor has to organize some interface which enables him to use the defined task. There are no special restrictions to the format of such an interface, but there is one requirement which has to be observed strictly: the interface as such is not allowed to do any kind of computation beyond providing only the necessary input from the task domain or to provide the necessary output to the domain. Only then are the different tests able to reveal some difference between the different actors.

If the tests show differences between certain types of actors with regard to a certain task or a task-field then this is a chance to develop smart assistive interfaces which can help the actor in question to overcome his weakness compared to the other type of actor. Thus this kind of measuring intelligence can be a strong supporter for a better world in the future.

Another consequence of the differing intelligence values can be to look to the inner structure of an actor with weaker values and asking how one could improve his capabilities. This can be done e.g. by different kinds of training or by improving his system structures.

COMMENTS

[1] Sara J.Shettleworth, Biological Approaches to the Study of Learning, pp.185 – 219, in: N.J.Mackintosh (Ed.), Animal Learning and Cognition, Academic Press, San Diego, New York, London et.al., 1994

[2a] Ernest R.Hilgard, Rita L.Atkinson, Richard C.Atkinson, Introduction to Psychology, Harcourt Brace Jovanovic, Inc., Psychology, 7th ed., New York, San Diego, Chicago et.al, 1979

[2b] Detlef H.Rost, Intelligenz. Fakten und Mythen, Belz Verlag, Weinheim – Basel, 2009

[2c] Detlef H.Rost, Handbuch Intelligenz, Beltz Verlag, Weinheim – Basel, 2013

AAI – Actor-Actor Interaction. A Philosophy of Science View

AAI – Actor-Actor Interaction.
A Philosophy of Science View
eJournal: uffmm.org, ISSN 2567-6458

Gerd Doeben-Henisch
info@uffmm.org
gerd@doeben-henisch.de

PDF

ABSTRACT

On the cover page of this blog you find a first general view on the subject matter of an integrated engineering approach for the future. Here we give a short description of the main idea of the analysis phase of systems engineering how this will be realized within the actor-actor interaction paradigm as described in this text.

INTRODUCTION

Overview of the analysis phase of systems engineering as realized within an actor-actor interaction paradigm
Overview of the analysis phase of systems engineering as realized within an actor-actor interaction paradigm

As you can see in figure Nr.1 there are the following main topics within the Actor-Actor Interaction (AAI) paradigm as used in this text (Comment: The more traditional formula is known as Human-Machine Interaction (HMI)):

Triggered by a problem document D_p from the problem phase (P) of the engineering process the AAI-experts have to analyze, what are the potential requirements following from this document, all the time also communicating with the stakeholder to keep in touch with the hidden intentions of the stakeholder.

The idea is to identify at least one task (T) with at least one goal state (G) which shall be arrived after running a task.

A task is assumed to represent a sequence of states (at least a start state and a goal state) which can have more than one option in every state, not excluding repetitions.

Every task presupposes some context (C) which gives the environment for the task.

The number of tasks and their length is in principle not limited, but their can be certain constraints (CS) given which have to be fulfilled required by the stakeholder or by some other important rules/ laws. Such constraints will probably limit the number of tasks as well as their length.

Actor Story

Every task as a sequence of states can be viewed as a story which describes a process. A story is a text (TXT) which is static and hides the implicit meaning in the brains of the participating actors. Only if an actor has some (learned) understanding of the used language then the actor is able to translate the perceptions of the process in an appropriate text and vice versa the text into corresponding perceptions or equivalently ‘thoughts’ representing the perceptions.

In this text it is assumed that a story is describing only the observable behavior of the participating actors, not their possible internal states (IS). For to describe the internal states (IS) it is further assumed that one describes the internal states in a new text called actor model (AM). The usual story is called an actor story (AS). Thus the actor story (AS) is the environment for the actor models (AM).

In this text three main modes of actor stories are distinguished:

  1. An actor story written in some everyday language L_0 called AS_L0 .
  2. A translation of the everyday language L_0 into a mathematical language L_math which can represent graphs, called AS_Lmath.
  3. A translation of the hidden meaning which resides in the brains of the AAI-experts into a pictorial language L_pict (like a comic strip), called AS_Lpict.

To make the relationship between the graph-version AS_Lmath and the pictorial version AS_Lpict visible one needs an explicit mapping Int from one version into the other one, like: Int : AS_Lmath <—> AS_Lpict. This mapping Int works like a lexicon from one language into another one.

From a philosophy of science point of view one has to consider that the different kinds of actor stories have a meaning which is rooted in the intended processes assumed to be necessary for the realization of the different tasks. The processes as such are dynamic, but the stories as such are static. Thus a stakeholder (SH) or an AAI-expert who wants to get some understanding of the intended processes has to rely on his internal brain simulations associated with the meaning of these stories. Because every actor has its own internal simulation which can not be perceived from the other actors there is some probability that the simulations of the different actors can be different. This can cause misunderstandings, errors, and frustrations.(Comment: This problem has been discussed in [DHW07])

One remedy to minimize such errors is the construction of automata (AT) derived from the math mode AS_Lmath of the actor stories. Because the math mode represents a graph one can derive Der from this version directly (and automatically) the description of an automaton which can completely simulate the actor story, thus one can assume Der(AS_Lmath) = AT_AS_Lmath.

But, from the point of view of Philosophy of science this derived automaton AT_AS_Lmath is still only a static text. This text describes the potential behavior of an automaton AT. Taking a real computer (COMP) one can feed this real computer with the description of the automaton AT AT_AS_Lmath and make the real computer behave like the described automaton. If we did this then we have a real simulation (SIM) of the theoretical behavior of the theoretical automaton AT realized by the real computer COMP. Thus we have SIM = COMP(AT_AS_Lmath). (Comment: These ideas have been discussed in [EDH11].)

Such a real simulation is dynamic and visible for everybody. All participating actors can see the same simulation and if there is some deviation from the intention of the stakeholder then this can become perceivable for everybody immediately.

Actor Model

As mentioned above the actor story (AS) describes only the observable behavior of some actor, but not possible internal states (IS) which could be responsible for the observable behavior.

If necessary it is possible to define for every actor an individual actor model; indeed one can define more than one model to explore the possibilities of different internal structures to enable a certain behavior.

The general pattern of actor models follows in this text the concept of input-output systems (IOSYS), which are in principle able to learn. What the term ‘learning’ designates concretely will be explained in later sections. The same holds of the term ‘intelligent’ and ‘intelligence’.

The basic assumptions about input-output systems used here reads a follows:

Def: Input-Output System (IOSYS)

IOSYS(x) iff x=< I, O, IS, phi>
phi : I x IS —> IS x O
I := Input
O := Output
IS := Internal

As in the case of the actor story (AS) the primary descriptions of actor models (AM) are static texts. To make the hidden meanings of these descriptions ‘explicit’, ‘visible’ one has again to convert the static texts into descriptions of automata, which can be feed into real computers which in turn then simulate the behavior of these theoretical automata as a real process.

Combining the real simulation of an actor story with the real simulations of all the participating actors described in the actor models can show a dynamic, impressive process which is full visible to all collaborating stakeholders and AAI-experts.

Testing

Having all actor stories and actor models at hand, ideally implemented as real simulations, one has to test the interaction of the elaborated actors with real actors, which are intended to work within these explorative stories and models. This is done by actor tests (former: usability tests) where (i) real actors are confronted with real tasks and have to perform in the intended way; (ii) real actors are interviewed with questionnaires about their subjective feelings during their task completion.

Every such test will yield some new insights how to change the settings a bit to gain eventually some improvements. Repeating these cycles of designing, testing, and modifying can generate a finite set of test-results T where possibly one subset is the ‘best’ compared to all the others. This can give some security that this design is probably the ‘relative best design’ with regards to T.

Further Readings:

  1. Analysis
  2. Simulation
  3. Testing
  4. User Modeling
  5. User Modeling and AI

For a newer version of the AAi-text see HERE..

REFERENCES

[DHW07] G. Doeben-Henisch and M. Wagner. Validation within safety critical systems engineering from a computation semiotics point of view.
Proceedings of the IEEE Africon2007 Conference, pages Pages: 1 – 7, 2007.
[EDH11] Louwrence Erasmus and Gerd Doeben-Henisch. A theory of the
system engineering process. In ISEM 2011 International Conference. IEEE, 2011.

EXAMPLE

For a toy-example to these concepts please see the post AAI – Actor-Actor Interaction. A Toy-Example, No.1