Since the release of the chatbot ‘chatGPT’ to the larger public, a kind of ‘earthquake’ has been going through the media, worldwide, in many areas, from individuals to institutions, companies, government agencies …. everyone is looking for the ‘chatGPT experience’. These reactions are amazing, and frightening at the same time.
Remark: The text of this post represents a later ‘stage’ of my thinking about the usefulness of the chatGPT algorithm, which started with my first reflections in the text entitled “chatGBT about Rationality: Emotions, Mystik, Unconscious, Conscious, …” from 15./16.January 2023. The main text to this version is an English translation from an originally German text partially generated with the www.DeepL.com/Translator (free version).
FORM
The following lines form only a short note, since it is hardly worthwhile to discuss a ‘surface phenomenon’ so intensively, when the ‘deep structures’ should be explained. Somehow the ‘structures behind chatGPT’ seem to interest hardly anybody (I do not mean technical details of the used algorithms).
chatGPT as an object
The chatbot named ‘chatGPT’ is a piece of software, an algorithm that (i) was invented and programmed by humans. When (ii) people ask it questions, then (iii) it searches the database of documents known to it, which in turn have been created by humans, (iv) for text patterns that have a relation to the question according to certain formal criteria (partly given by the programmers). These ‘text finds’ are (v) also ‘arranged’ according to certain formal criteria (partly given by the programmers) into a new text, which (vi) should come close to those text patterns, which a human reader is ‘used’ to accept as ‘meaningful’.
Text surface – text meaning – truthfulness
A normal human being can distinguish – at least ‘intuitively’ – between the (i) ‘strings’ used as ‘expressions of a language’ and those (ii) ‘knowledge elements’ (in the mind of the hearer-speaker) which are as such ‘independent’ of the language elements, but which (iii) can be ‘freely associated’ by speakers-hearers of a language, so that the correlated ‘knowledge elements’ become what is usually called the ‘meaning’ of the language elements. [1] Of these knowledge elements (iv), every language participant already ‘knows’ ‘pre-linguistically’, as a learning child [2], that some of these knowledge elements are ‘correlatable’ with circumstances of the everyday world under certain circumstances. And the normal language user also ‘intuitively’ (automatically, unconsciously) has the ability to assess such correlation – in the light of the available knowledge – as (v) ‘possible’ or (vi) as rather ‘improbable’ or (vi) as ‘mere fancifulness’.”[3]
The basic ability of a human being to be able to establish a ‘correlation’ of meanings with (intersubjective) environmental facts is called – at least by some – philosophers ‘truth ability’ and in the execution of truth ability one then also can speak of ‘true’ linguistic utterances or of ‘true statements’.[5]
Distinctions like ‘true’, ‘possibly true’, ‘rather not true’ or ‘in no case true’ indicate that the reality reference of human knowledge elements is very diverse and ‘dynamic’. Something that was true a moment ago may not be true the next moment. Something that has long been dismissed as ‘mere fantasy’ may suddenly appear as ‘possible’ or ‘suddenly true’. To move in this ‘dynamically correlated space of meaning’ in such a way that a certain ‘inner and outer consistency’ is preserved, is a complex challenge, which has not yet been fully understood by philosophy and the sciences, let alone even approximately ‘explained’.
The fact is: we humans can do this to a certain extent. Of course, the more complex the knowledge space is, the more diverse the linguistic interactions with other people become, the more difficult it becomes to completely understand all aspects of a linguistic statement in a situation.
‘Air act’ chatGPT
Comparing the chatbot chatGPT with these ‘basic characteristics’ of humans, one can see that chatGPT can do none of these things. (i) It cannot ask questions meaningfully on its own, since there is no reason why it should ask (unless someone induces it to ask). (ii) Text documents (of people) are sets of expressions for him, for which he has no independent assignment of meaning. So he could never independently ask or answer the ‘truth question’ – with all its dynamic shades. He takes everything at ‘face value’ or one says right away that he is ‘only dreaming’.
If chatGPT, because of its large text database, has a subset of expressions that are somehow classified as ‘true’, then the algorithm can ‘in principle’ indirectly determine ‘probabilities’ that other sets of expressions that are not classified as ‘true’ then do ‘with some probability’ appear to be ‘true’. Whether the current chatGPT algorithm uses such ‘probable truths’ explicitly is unclear. In principle, it translates texts into ‘vector spaces’ that are ‘mapped into each other’ in various ways, and parts of these vector spaces are then output again in the form of a ‘text’. The concept of ‘truth’ does not appear in these mathematical operations – to my current knowledge. If, then it would be also only the formal logical concept of truth [4]; but this lies with respect to the vector spaces ‘above’ the vector spaces, forms with respect to these a ‘meta-concept’. If one wanted to actually apply this to the vector spaces and operations on these vector spaces, then one would have to completely rewrite the code of chatGPT. If one would do this – but nobody will be able to do this – then the code of chatGPT would have the status of a formal theory (as in mathematics) (see remark [5]). From an empirical truth capability chatGPT would then still be miles away.
Hybrid illusory truths
In the use case where the algorithm named ‘chatGPT’ uses expression sets similar to the texts that humans produce and read, chatGPT navigates purely formally and with probabilities through the space of formal expression elements. However, a human who ‘reads’ the expression sets produced by chatGPT automatically (= unconsciously!) activates his or her ‘linguistic knowledge of meaning’ and projects it into the abstract expression sets of chatGBT. As one can observe (and hears and reads from others), the abstract expression sets produced by chatGBT are so similar to the usual text input of humans – purely formally – that a human can seemingly effortlessly correlate his meaning knowledge with these texts. This has the consequence that the receiving (reading, listening) human has the ‘feeling’ that chatGPT produces ‘meaningful texts’. In the ‘projection’ of the reading/listening human YES, but in the production of chatGPT NO. chatGBT has only formal expression sets (coded as vector spaces), with which it calculates ‘blindly’. It does not have ‘meanings’ in the human sense even rudimentarily.
Back to the Human?
(Last change: 27.February 2023)
How easily people are impressed by a ‘fake machine’ to the point of apparently forgetting themselves in face of the machine by feeling ‘stupid’ and ‘inefficient’, although the machine only makes ‘correlations’ between human questions and human knowledge documents in a purely formal way, is actually frightening [6a,b], [7], at least in a double sense: (i)Instead of better recognizing (and using) one’s own potentials, one stares spellbound like the famous ‘rabbit at the snake’, although the machine is still a ‘product of the human mind’. (ii) This ‘cognitive deception’ misses to better understand the actually immense potential of ‘collective human intelligence’, which of course could then be advanced by at least one evolutionary level higher by incorporating modern technologies. The challenge of the hour is ‘Collective Human-Machine Intelligence’ in the context of sustainable development with priority given to human collective intelligence. The current so-called ‘artificial (= machine) intelligence’ is only present by rather primitive algorithms. Integrated into a developed ‘collective human intelligence’ quite different forms of ‘intelligence’ could be realized, ones we currently can only dream of at most.
Commenting on other articles from other authors about chatGPT
(Last change: 14.April 2023)
[7], [8],[9],[11],[12],[13],[14]
Comments
(Last change: 3.April 2023)
wkp-en: en.wikipedia.org
[1] In the many thousands of ‘natural languages’ of this world one can observe how ‘experiential environmental facts’ can become ‘knowledge elements’ via ‘perception’, which are then correlated with different expressions in each language. Linguists (and semioticians) therefore speak here of ‘conventions’, ‘freely agreed assignments’.
[2] Due to physical interaction with the environment, which enables ‘perceptual events’ that are distinguishable from the ‘remembered and known knowledge elements’.
[3] The classification of ‘knowledge elements’ as ‘imaginations/ fantasies’ can be wrong, as many examples show, like vice versa, the classification as ‘probably correlatable’ can be wrong too!
[4] Not the ‘classical (Aristotelian) logic’ since the Aristotelian logic did not yet realize a stricCommenting on other articles from other authors about chatGPTt separation of ‘form’ (elements of expression) and ‘content’ (meaning).
[5] There are also contexts in which one speaks of ‘true statements’ although there is no relation to a concrete world experience. For example in the field of mathematics, where one likes to say that a statement is ‘true’. But this is a completely ‘different truth’. Here it is about the fact that in the context of a ‘mathematical theory’ certain ‘basic assumptions’ were made (which must have nothing to do with a concrete reality), and one then ‘derives’ other statements starting from these basic assumptions with the help of a formal concept of inference (the formal logic). A ‘derived statement’ (usually called a ‘theorem’), also has no relation to a concrete reality. It is ‘logically true’ or ‘formally true’. If one would ‘relate’ the basic assumptions of a mathematical theory to concrete reality by – certainly not very simple – ‘interpretations’ (as e.g. in ‘applied physics’), then it may be, under special conditions, that the formally derived statements of such an ’empirically interpreted abstract theory’ gain an ’empirical meaning’, which may be ‘correlatable’ under certain conditions; then such statements would not only be called ‘logically true’, but also ’empirically true’. As the history of science and philosophy of science shows, however, the ‘transition’ from empirically interpreted abstract theories to empirically interpretable inferences with truth claims is not trivial. The reason lies in the used ‘logical inference concept’. In modern formal logic there are almost ‘arbitrarily many’ different formal inference terms possible. Whether such a formal inference term really ‘adequately represents’ the structure of empirical facts via abstract structures with formal inferences is not at all certain! This pro’simulation’blem is not really clarified in the philosophy of science so far!
[6a] Weizenbaum’s 1966 chatbot ‘Eliza’, despite its simplicity, was able to make human users believe that the program ‘understood’ them even when they were told that it was just a simple algorithm. See the keyword ‚Eliza‘ in wkp-en: https://en.wikipedia.org/wiki/ELIZA
[6b] Joseph Weizenbaum, 1966, „ELIZA. A Computer Program For the Study of Natural Language. Communication Between Man And Machine“, Communications of the ACM, Vol.9, No.1, January 1966, URL: https://cse.buffalo.edu/~rapaport/572/S02/weizenbaum.eliza.1966.pdf . Note: Although the program ‘Eliza’ by Weizenbaum was very simple, all users were fascinated by the program because they had the feeling “It understands me”, while the program only mirrored the questions and statements of the users. In other words, the users were ‘fascinated by themselves’ with the program as a kind of ‘mirror’.
[7] Ted Chiang, 2023, “ChatGPT Is a Blurry JPEG of the Web. OpenAI’s chatbot offers paraphrases, whereas Google offers quotes. Which do we prefer?”, The NEW YORKER, February 9, 2023. URL: https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web . Note: Chang looks to the chatGPT program using the paradigm of a ‘compression algorithm’: the abundance of information is ‘condensed/abstracted’ so that a slightly blurred image of the text volumes is created, not a 1-to-1 copy. This gives the user the impression of understanding at the expense of access to detail and accuracy. The texts of chatGPT are not ‘true’, but they ‘mute’.
[8] Dietmar Hansch, 2023, “The more honest name would be ‘Simulated Intelligence’. Which deficits bots like chatGBT suffer from and what that must mean for our dealings with them.”, FAZ Frankfurter Allgemeine Zeitung, March 1, 2023, p.N1 . Note: While Chiang (see [7]) approaches the phenomenon chatGPT with the concept ‘compression algorithm’ Hansch prefers the terms ‘statistical-incremental learning’ as well as ‘insight learning’. For Hansch, insight learning is tied to ‘mind’ and ‘consciousness’, for which he postulates ‘equivalent structures’ in the brain. Regarding insight learning, Hansch further comments “insight learning is not only faster, but also indispensable for a deep, holistic understanding of the world, which grasps far-reaching connections as well as conveys criteria for truth and truthfulness.” It is not surprising then when Hansch writes “Insight learning is the highest form of learning…”. With reference to this frame of reference established by Hansch, he classifies chatGPT in the sense that it is only capable of ‘statistical-incremental learning’. Further, Hansch postulates for humans, “Human learning is never purely objective, we always structure the world in relation to our needs, feelings, and conscious purposes…”. He calls this the ‘human reference’ in human cognition, and it is precisely this what he also denies for chatGPT. For common designation ‘AI’ as ‘Artificial Intelligence’ he postulates that the term ‘intelligence’ in this word combination has nothing to do with the meaning we associate with ‘intelligence’ in the case of humans, so in no case has the term intelligence anything to do with ‘insight learning’, as he has stated before. To give more expression to this fact of mismatch he would rather use the term ‘simulated intelligence’ (see also [9]). This conceptual strategy seems strange, since the term simulation [10] normally presupposes that there is a clear state of affairs, for which one defines a simplified ‘model’, by means of which the behavior of the original system can then be — simplified — viewed and examined in important respects. In the present case, however, it is not quite clear what the original system should be, which is to be simulated in the case of AI. There is so far no unified definition of ‘intelligence’ in the context of ‘AI’! As far as Hansch’s terminology itself is concerned, the terms ‘statistical-incremental learning’ as well as ‘insight learning’ are not clearly defined either; the relation to observable human behavior let alone to the postulated ‘equivalent brain structures’ is arbitrarily unclear (which is not improved by the relation to terms like ‘consciousness’ and ‘mind’ which are not defined yet).
[9] Severin Tatarczyk, Feb 19, 2023, on ‘Simulated Intelligence’: https://www.severint.net/2023/02/19/kompakt-warum-ich-den-begriff-simulierte-intelligenz-bevorzuge-und-warum-chatbots-so-menschlich-auf-uns-wirken/
[10] See the term ‘simulation’ in wkp-en: https://en.wikipedia.org/wiki/Simulation
[11] Doris Brelowski pointed me to the following article: James Bridle, 16.March 2023, „The stupidity of AI. Artificial intelligence in its current form is based on the wholesale appropriation of existing culture, and the notion that it is actually intelligent could be actively dangerous“, URL: https://www.theguardian.com/technology/2023/mar/16/the-stupidity-of-ai-artificial-intelligence-dall-e-chatgpt?CMP=Share_AndroidApp_Other . Comment: An article that knowledgeably and very sophisticatedly describes the interplay between forms of AI that are being ‘unleashed’ on the entire Internet by large corporations, and what this is doing to human culture and then, of course, to humans themselves. Two quotes from this very readable article: Quote 1: „The entirety of this kind of publicly available AI, whether it works with images or words, as well as the many data-driven applications like it, is based on this wholesale appropriation of existing culture, the scope of which we can barely comprehend. Public or private, legal or otherwise, most of the text and images scraped up by these systems exist in the nebulous domain of “fair use” (permitted in the US, but questionable if not outright illegal in the EU). Like most of what goes on inside advanced neural networks, it’s really impossible to understand how they work from the outside, rare encounters such as Lapine’s aside. But we can be certain of this: far from being the magical, novel creations of brilliant machines, the outputs of this kind of AI is entirely dependent on the uncredited and unremunerated work of generations of human artists.“ Quote 2: „Now, this didn’t happen because ChatGPT is inherently rightwing. It’s because it’s inherently stupid. It has read most of the internet, and it knows what human language is supposed to sound like, but it has no relation to reality whatsoever. It is dreaming sentences that sound about right, and listening to it talk is frankly about as interesting as listening to someone’s dreams. It is very good at producing what sounds like sense, and best of all at producing cliche and banality, which has composed the majority of its diet, but it remains incapable of relating meaningfully to the world as it actually is. Distrust anyone who pretends that this is an echo, even an approximation, of consciousness. (As this piece was going to publication, OpenAI released a new version of the system that powers ChatGPT, and said it was “less likely to make up facts”.)“
[12] David Krakauer in an Interview with Brian Gallagher in Nautilus, March 27, 2023, Does GPT-4 Really Understand What We’re Saying?, URL: https://nautil.us/does-gpt-4-really-understand-what-were-saying-291034/?_sp=d9a7861a-9644-44a7-8ba7-f95ee526d468.1680528060130. David Krakauer, an evolutionary theorist and president of the Santa Fe Institute for complexity science, analyzes the role of chat-GPT-4 models compared to the human language model and a more differentiated understanding of what ‘understanding’ and ‘Intelligence’ could mean. His main points of criticism are in close agreement with the position int he text above. He points out that (i) one has clearly to distinguish between the ‘information concept’ of Shannon and the concept of ‘meaning’. Something can represent a high information load but can nevertheless be empty of any meaning. Then he points out (ii) that there are several possible variants of the meaning of ‘understanding’. Coordinating with human understanding can work, but to understand in a constructive sense: no. Then Krakauer (iii) relates GPT-4 to the standard model of science which he characterizes as ‘parsimony’; chat-GPT-4 is clearly the opposite. Another point (iv) is the fact, that human experience has an ’emotional’ and a ‘physical’ aspect based on somato-sensory perceptions within its body. This is missing with GPT-4. This is somehow related (v) to the fact, that the human brain with its ‘algorithms’ is the product of millions of years of evolution in a complex environment. The GPT-4 algorithms have nothing comparable; they have only to ‘convince’ humans. Finally (vi) humans can generate ‘physical models’ inspired by their experience and can quickly argue by using such models. Thus Krakauer concludes “So the narrative that says we’ve rediscovered human reasoning is so misguided in so many ways. Just demonstrably false. That can’t be the way to go.”
[13] By Marie-José Kolly (text) and Merlin Flügel (illustration), 11.04.2023, “Chatbots like GPT can form wonderful sentences. That’s exactly what makes them a problem.” Artificial intelligence fools us into believing something that is not. A plea against the general enthusiasm. Online newspaper ‘Republik’ from Schweiz, URL: https://www.republik.ch/2023/04/11/chatbots-wie-gpt-koennen-wunderbare-saetze-bilden-genau-das-macht-sie-zum-problem? Here are some comments:
The text by Marie-José Kolly stands out because the algorithm named chatGPT(4) is characterized here both in its input-output behavior and additionally a comparison to humans is made at least to some extent.
The basic problem of the algorithm chatGPT(4) is (as also pointed out in my text above) that it has as input data exclusively text sets (also those of the users), which are analyzed according to purely statistical procedures in their formal properties. On the basis of the analyzed regularities, arbitrary text collages can then be generated, which are very similar in form to human texts, so much so that many people take them for ‘human-generated texts’. In fact, however, the algorithm lacks what we humans call ‘world knowledge’, it lacks real ‘thinking’, it lacks ‘own’ value positions, and the algorithm ‘does not understand’ its own text.
Due to this lack of its own reference to the world, the algorithm can be manipulated very easily via the available text volumes. A ‘mass production’ of ‘junk texts’, of ‘disinformation’ is thus very easily possible.
If one considers that modern democracies can only function if the majority of citizens have a common basis of facts that can be assumed to be ‘true’, a common body of knowledge, and reliable media, then the chatGPT(4) algorithm can massively destroy precisely these requirements for a democracy.
The interesting question then is whether chatGPT(4) can actually support a human society, especially a democratic society, in a positive-constructive way?
In any case, it is known that humans learn the use of their language from childhood on in direct contact with a real world, largely playfully, in interaction with other children/people. For humans ‘words’ are never isolated quantities, but they are always dynamically integrated into equally dynamic contexts. Language is never only ‘form’ but always at the same time ‘content’, and this in many different ways. This is only possible because humans have complex cognitive abilities, which include corresponding memory abilities as well as abilities for generalization.
The cultural-historical development from spoken language, via writing, books, libraries up to enormous digital data memories has indeed achieved tremendous things concerning the ‘forms’ of language and therein – possibly – encoded knowledge, but there is the impression that the ‘automation’ of the forms drives them into ‘isolation’, so that the forms lose more and more their contact to reality, to meaning, to truth. Language as a central moment of enabling more complex knowledge and more complex action is thus increasingly becoming a ‘parasite’ that claims more and more space and in the process destroys more and more meaning and truth.
[14] Gary Marcus, April 2023, Hoping for the Best as AI Evolves, Gary Marcus on the systems that “pose a real and imminent threat to the fabric of society.” Communications of the ACM, Volume 66, Issue 4, April 2023 pp 6–7, https://doi.org/10.1145/3583078 , Comment: Gary Marcus writes on the occasion of the effects of systems like chatGPT(OpenAI), Dalle-E2 and Lensa about the seriously increasing negative effects these tools can have within a society, to an extent that poses a serious threat to every society! These tools are inherently flawed in the areas of thinking, facts and hallucinations. At near zero cost, they can be used to create and execute large-scale disinformation campaigns very quickly. Looking to the globally important website ‘Stack Overflow’ for programmers as an example, one could (and can) see how the inflationary use of chatGPT due to its inherent many flaws pushes the Stack Overflow’s management team having to urge its users to completely stop using chatGPT in order to prevent the site’s collapse after 14 years. In the case of big players who specifically target disinformation, such a measure is ineffective. These players aim to create a data world in which no one will be able to trust anyone. With this in mind, Gary Marcus sets out 4 postulates that every society should implement: (1) Automatically generated not certified content should be completely banned; (2) Legally effective measures must be adopted that can prevent ‘misinformation’; (3) User accounts must be made tamper-proof; (4) A new generation of AI tools is needed that can verify facts. (Translated with partial support from www.DeepL.com/Translator (free version))
In the introduction of the main text it has been underlined that within a sustainable empirical theory it is not only necessary to widen the scope with a maximum of diversity, but at the same time it is also necessary to enable the capability for an optimal prediction about the ‘possible states of a possible future’.
the meaning machinery
In the text after this introduction it has been outlined that between human actors the most powerful tool for the clarification of the given situation — the NOW — is the everyday language with a ‘built in’ potential in every human actor for infinite meanings. This individual internal meaning space as part of the individual cognitive structure is equipped with an ‘abstract – concrete’ meaning structure with the ability to distinguish between ‘true’ and ‘not true’, and furthermore equipped with the ability to ‘play around’ with meanings in a ‘new way’.
COORDINATION
Thus every human actor can generate within his cognitive dimension some states or situations accompanied with potential new processes leading to new states. To share this ‘internal meanings’ with other brains to ‘compare’ properties of the ‘own’ thinking with properties of the thinking of ‘others’ the only chance is to communicate with other human actors mediated by the shared everyday language. If this communication is successful it arises the possibility to ‘coordinate’ the own thinking about states and possible actions with others. A ‘joint undertaking’ is becoming possible.
shared thinking
To simplify the process of communication it is possible, that a human actor does not ‘wait’ until some point in the future to communicate the content of the thinking, but even ‘while the thinking process is going on’ a human actor can ‘translate his thinking’ in language expressions which ‘fit the processed meanings’ as good as possible.Doing this another human actor can observe the language activity, can try to ‘understand’, and can try to ‘respond’ to the observations with his language expressions. Such an ‘interplay’ of expressions in the context of multiple thinking processes can show directly either a ‘congruence’ or a ‘difference’. This can help each participant in the communication to clarify the own thinking. At the same time an exchange of language expressions associated with possible meanings inside the different brains can ‘stimulate’ different kinds of memory and thinking processes and through this the space of shared meanings can be ‘enlarged’.
phenomenal space 1 and 2
Human actors with their ability to construct meaning spaces and the ability to share parts of the meaning space by language communication are embedded with their bodies in a ‘body-external environment’ usual called ‘external world’ or ‘nature’ associated with the property to be ‘real’.
Equipped with a body with multiple different kinds of ‘sensors’ some of the environmental properties can stimulate these sensors which in turn send neuronal signals to the embedded brain. The first stage of the ‘processing of sensor signals’ is usually called ‘perception’. Perception is not a passive 1-to-1 mapping of signals into the brain but it is already a highly sophisticated processing where the ‘raw signals’ of the sensors — which already are doing some processing on their own — are ‘transformed’ into more complex signals which the human actor in its perception does perceive as ‘features’, ‘properties’, ‘figures’, ‘patterns’ etc. which usually are called ‘phenomena’. They all together are called ‘phenomenal space’. In a ‘naive thinking’ this phenomenal space is taken ‘as the external world directly’. During life a human actor can learn — this must not happen! –, that the ‘phenomenal space’ is a ‘derived space’ triggered by an ‘assumed outside world’ which ’causes’ by its properties the sensors to react in a certain way. But the ‘actual nature’ of the outside world is not really known. Let us call the unknown outside world of properties ‘phenomenal space 1’ and the derived phenomenal space inside the body-brain ‘phenomenal space 2’.
TIMELY ORDERING
Due to the availability of the phenomenal space 2 the different human actors can try to ‘explore’ the ‘unknown assumed real world’ based on the available phenomena.
If one takes a wider look to the working of the brain of a human actor one can detect that the processing of the brain of the phenomenal space is using additional mechanisms:
The phenomenal space is organized in ‘time slices’ of a certain fixed duration. The ‘content’ of a time slice during the time window (t,t’) will be ‘overwritten’ during the next time slice (t’,t”) by those phenomena, which are then ‘actual’, which are then constituting the NOW. The phenomena from the time window before (t’,t”) can become ‘stored’ in some other parts of the brain usually called ‘memory’.
The ‘storing’ of phenomena in parts of the brain called ‘memory’ happens in a highly sophisticated way enabling ‘abstract structures’ with an ‘interface’ for ‘concrete properties’ typical for the phenomenal space, and which can become associated with other ‘content’ of the memory.
It is an astonishing ability of the memory to enable an ‘ordering’ of memory contents related to situations as having occurred ‘before’ or ‘after’ some other property. Therefore the ‘content of the memory’ can represent collections of ‘stored NOWs’, which can be ‘ordered’ in a ‘sequence of NOWs’, and thereby the ‘dimension of time’ appears as a ‘framing property’ of ‘remembered phenomena’.
Based on this capability to organize remembered phenomena in ‘sequences of states’ representing a so-called ‘timely order’ the brain can ‘operate’ on such sequences in various ways. It can e.g. ‘compare’ two states in such a sequence whether these are ‘the same’ or whether they are ‘different’. A difference points to a ‘change’ in the phenomenal space. Longer sequences — even including changes — can perhaps show up as ‘repetitions’ compared to ‘earlier’ sequences. Such ‘repeating sequences’ can perhaps represent a ‘pattern’ pointing to some ‘hidden factors’ responsible for the pattern.
formal representations [1]
Basic outline of human actor as part of an external world with an internal phenomenal space 2, including a memory and the capability to elaborate cognitive meta-levels using the dimension of time. There is a limited exchange medium between different brains realized by language communication. Formal models are an instrument to represent recognized timely sequences of sets of properties with typical changes.
Based on a rather sophisticated internal processing structure every human actor has the capability to compose language descriptions which can ‘represent’ with the aid of sets of language expressions different kinds of local situations. Every expression can represent some ‘meaning’ which is encoded in every human actor in an individual manner. Such a language encoding can partially becoming ‘standardized’ by shared language learning in typical everyday living situations. To that extend as language encodings (the assumed meaning) is shared between different human actors they can use this common meaning space to communicate their experience.
Based on the built-in property of abstract knowledge to have an interface to ‘more concrete’ meanings, which finally can be related to ‘concrete perceptual phenomena’ available in the sensual perceptions, every human actor can ‘check’ whether an actual meaning seems to have an ‘actual correspondence’ to some properties in the ‘real environment’. If this phenomenal setting in the phenomenal space 2 with a correspondence to the sensual perceptions is encoded in a language expression E then usually it is told that the ‘meaning of E’ is true; otherwise not.
Because the perceptual interface to an assumed real world is common to all human actors they can ‘synchronize’ their perceptions by sharing the related encoded language expressions.
If a group of human actors sharing a real situation agrees about a ‘set of language expressions’ in the sens that each expression is assumed to be ‘true’, then one can assume, that every expression ‘represents’ some encoded meanings which are related to the shared empirical situation, and therefore the expressions represent ‘properties of the assumed real world’. Such kinds of ‘meaning constructions’ can be further ‘supported’ by the usage of ‘standardized procedures’ called ‘measurement procedures’.
Under this assumption one can infer, that a ‘change in the realm of real world properties’ has to be encoded in a ‘new language expression’ associated with the ‘new real world properties’ and has to be included in the set of expressions describing an actual situation. At the same time it can happen, that an expression of the actual set of expressions is becoming ‘outdated’ because the properties, this expression has encoded, are gone. Thus, the overall ‘dynamics of a set of expressions representing an actual set of real world properties’ can be realized as follows:
Agree on a first set of expression to be a ‘true’ description of a given set of real world properties.
After an agreed period of time one has to check whether (i) the encoded meaning of an expression is gone or (ii) whether a new real world property has appeared which seems to be ‘important’ but is not yet encoded in a language expression of the set. Depending from this check either (i) one has to delete those expressions which are no longer ‘true’ or (ii) one has to introduce new expressions for the new real world properties.
In a strictly ‘observational approach’ the human actors are only observing the course of events after some — regular or spontaneous –time span, making their observations (‘measurements’) and compare these observations with their last ‘true description’ of the actual situation. Following this pattern of behavior they can deduce from the series of true descriptions <D1, D2, …, Dn> for every pair of descriptions (Di,Di+1) a ‘difference description’ as a ‘rule’ in the following way: (i) IF x is a subset of expressions in Di+1, which are not yet members of the set of expressions in Di, THEN ‘add’ these expressions to the set of expressions in Di. (ii) IF y is a subset of expressions in Di, which are no more members of the set of expressions in Di+1, THEN ‘delete’ these expressions from the set of expressions in Di. (iii) Construct a ‘condition-set’ of expressions as subset of Di, which has to be fulfilled to apply (i) and (ii).
Doing this for every pair of descriptions then one is getting a set of ‘change rules’ X which can be used, to ‘generate’ — starting with the first description D0 — all the follow-up descriptions only by ‘applying a change rule Xi‘ to the last generated description.
This first purely observational approach works, but every change rule Xi in this set of change rules X can be very ‘singular’ pointing to a true singularity in the mathematical sense, because there is not ‘common rule’ to predict this singularity.
It would be desirable to ‘dig into possible hidden factors’ which are responsible for the observed changes but they would allow to ‘reduce the number’ of individual change rules of X. But for such a ‘rule-compression’ there exists from the outset no usable knowledge. Such a reduction will only be possible if a certain amount of research work will be done hopefully to discover the hidden factors.
All the change rules which could be found through such observational processes can in the future be re-used to explore possible outcomes for selected situations.
COMMENTS
[1] For the final format of this section I have got important suggestions from René Thom by reading the introduction of his book “Structural Stability and Morphogenesis: An Outline of a General Theory of Models” (1972, 1989). See my review post HERE : https://www.uffmm.org/2022/08/22/rene-thom-structural-stability-and-morphogenesis-an-outline-of-a-general-theory-of-models-original-french-edition-1972-updated-by-the-author-and-translated-into-english-by-d-h-fowler-1989/
The whole text shows a dynamic, which induces many changes. Difficult to plan ‘in advance’.
Perhaps, some time, it will look like a ‘book’, at least ‘for a moment’.
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
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.”)
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.”)
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’.“
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.“)
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])”)
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. )
!!! From here all the following chapters have to be re-written !!!
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.)
/* 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 = “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 testableconjectures, and from scientific laws, which are descriptive accounts of the way nature behaves under certain conditions.”
[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.”
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 testableconjectures, 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
[27] SDG 4 in wkp-en: https://en.wikipedia.org/wiki/Sustainable_Development_Goal_4
[28] Thomas Rid, Rise of the Machines. A Cybernetic History, W.W.Norton & Company, 2016, New York – London
[29] Doeben-Henisch, G., 2006, Reducing Negative Complexity by a Semiotic System In: Gudwin, R., & Queiroz, J., (Eds). Semiotics and Intelligent Systems Development. Hershey et al: Idea Group Publishing, 2006, pp.330-342
[30] Döben-Henisch, G., Reinforcing the global heartbeat: Introducing the planet earth simulator project, In M. Faßler & C. Terkowsky (Eds.), URBAN FICTIONS. Die Zukunft des Städtischen. München, Germany: Wilhelm Fink Verlag, 2006, pp.251-263
[29] The idea that individual disciplines are not good enough for the ‘whole of knowledge’ is expressed in a clear way in a video of the theoretical physicist and philosopher Carlo Rovell: Carlo Rovelli on physics and philosophy, June 1, 2022, Video from the Perimeter Institute for Theoretical Physics. Theoretical physicist, philosopher, and international bestselling author Carlo Rovelli joins Lauren and Colin for a conversation about the quest for quantum gravity, the importance of unlearning outdated ideas, and a very unique way to get out of a speeding ticket.
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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.”
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.
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
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.”
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.”
This post shows a simple simulation example with the beta-version of the new Version 2 of the oksimo programming environment. This example shall illustrate the concept of an ‘Everyday Empirical Theory‘ as described in this blog 11 days before. It is intentionally as ‘simple as possible’. Probably some more examples will be shown.
FROM THEORY TO AN APPLICATION
To apply a theory concept in an everyday world there are many formats possible. In this text it will be shown how such an application would look like if one is applying the oksimo programming environment. Until now there exists only a German Blog (oksimo.org) describing the oksimo paradigm a little bit. But the examples there are written with oksimo version 1, which didn’t allow to use math. In version 2 this is possible, accompanied by some visual graph features.
Everyday Experts – Basic Ideas
This figure shows a simple outline of the basic assumptions of the oksimo programming environment constituting the oksimo paradigm: (i) Every human person is assumed to be a ‘natural expert’ being member of a bigger population which shares the same ‘everyday language’ including basic math. (ii) An actor is embedded in some empirical environment including the own body and other human actors. (iii) Human actors are capable of elaborating as inner states different kinds of ‘mental (cognitive) models’ based on their experience of the environment and their own body. (iv) Human actors are further capable to use symbolic languages to ‘represent’ properties of these mental models encoded in symbolic expressions. Such symbolic encoding presupposes an ‘inner meaning function’ which has to be learned. (v) In the oksimo programming environment one needs for the description of a ‘given state’ two kinds of symbolic expressions: (v.1) Language expressions to describe general properties and relations which are assumed to be ‘given’ (= ‘valid by experience’); (v.2) Language expressions to name concrete quantitative properties (simple math expressions).This figure shows the idea how to change a given state (situation) by so-called ‘change rules’. A change rule encodes experience from the everyday world under which conditions some properties of a given situation S can be ‘changed’ in a way, that a ‘new situation’ S* comes into being. Generally a given state can change if either language expression is ‘deleted’ from the description or ‘contributed’. Another possibility is realized if one of the given quantitative expressions changes its value. In the above simple situation the only change happens by changing the number of citizens by some growth effect. But, as other examples will demonstrate, everything is possible what is possible in the empirical world.
SOME MORE FEATURES
The basic schema of the oksimo paradigm assumes the following components:
The description of a ‘given situation’ as a ‘start state’.
The description of a ‘vision’ functioning as a ‘goal’ which allows a basic ‘Benchmarking’.
A list of ‘change rules’ which describe the assumed possible changes
An ‘inference engine’ called ‘simulator’: Depending from the number of wanted ‘simulation cycles’ (‘inferences’) the simulator applies the change rules onto a given state S and thereby it is producing a ‘follow up state’ S*, which becomes the new given state. The series of generated states represents the ‘history’ of a simulation. Every follow up state is an ‘inference’ and by definition also a ‘forecast’.
All these features (1) – (4) together constitute a full empirical theory in the sense of the mentioned theory post before.
Let us look to a real simulation.
A REAL SIMULATION
The following example has been run with Oksimo v2.0 (Pre-Release) (353e5). Hopefully we can finish the pre-release to a full release the next few weeks.
A VISION
Name: v2026
Expressions:
The Main-Kinzig County exists.
Math expressions:
YEAR>2025 and YEAR<2027
This simple goal assumes the existence of the Main-Kinzig County for the year 2026.
GIVEN START STATE
Name: StartSimple1
Expressions:
The Main-Kinzig County exists.
The number of citizens is known.
Comparing the number of different years one has computed a growth rate.
Math expressions:
YEAR=2018Number
CITIZENS=418950Amount
GROWTH=0.0023Percentage
The start state makes some simple statements which are assumed to be ‘valid’ in a ‘real given situation’ by the participating natural experts.
CHANGE RULES
In this example there is only one change rules (In principle there can be as many change rules as wanted).
Rule name: Growth1
Probability: 1.0
Conditions:
The Main-Kinzig County exists.
Math conditions:
CITIZENS < 450000
Effects plus:
Effects minus:
Effects math:
CITIZENS=CITIZENS+(CITIZENS*GROWTH)
YEAR=YEAR+1
This change rules is rather simple. It looks only to the fact whether the Main-Kinzig County exists and wether the number of citizens is still below 450000. If this is the case, then the year will be incremented and the number of citizens will be incremented according to an extremely simple formula.
For every named quantity in this simulation (YEAR, GROWTH, CITIZENS) the values are collected for every simulation cycle and therefore can be used for evaluations. In this simple case only the quantities of YEAR and CITIZENS have changes:
Simple linear graph for the quantity named YEARSimple linear graph for the quantity named CITIZENS
Here the quick log of simulation cycle round 7 – 9:
Round 7
State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2025Number
CITIZENS: 425741.8149741673Amount
GROWTH: 0.0023Percentage
50.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
None
Round 8
State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2026Number
CITIZENS: 426721.0211486079Amount
GROWTH: 0.0023Percentage
100.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
YEAR>2025 and YEAR<2027,
Round 9
State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2027Number
CITIZENS: 427702.4794972497Amount
GROWTH: 0.0023Percentage
50.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
None
In round 8 one can see, that the simulation announces:
“100.00 percent of your vision was achieved by reaching the following states: The Main-Kinzig County exists., And the following math visions: YEAR>2025 and YEAR<2027“
From this the natural expert can conclude that his requirements given in the vision are ‘fulfilled’/’satisfied’.
WHAT COMES NEXT?
In a loosely order more examples will follow. Here you find the next one.
This text is part of a philosophy of science analysis of the case of the oksimo software (oksimo.com). A specification of the oksimo software from an engineering point of view can be found in four consecutive posts dedicated to the HMI-Analysis for this software.[*]
THE BOOK: THEORY OF SETS
Covered under the pseudonym of N.Bourbaki [1] appeared 1970 the French edition of a book which 1968 already had been translated into English (reprinted 1970) called Theory of Sets.[2] This book is the first book of a series about ELEMENTS OF MATHEMATICS.
To classify this book about set theory as a book of Metamathematics and as such as a book in the perspective of Philosophy of Science will become clear if one starts reading the book.[3]
MATHEMATICS WITH ONE LANGUAGE
It is the basic conviction of the Bourbaki book, that “… it is known to be possible … to derive practically the whole of known mathematics from a single source the Theory of Sets.” (p.9) And from this Bourbaki concludes, that it will be sufficient “… to describe the principles of a single formalized language, to indicate how the Thory of Sets could be written in this language, and then to show how the various branches of mathematics … fit into this framework.”(p.9)
Thus, the content of mathematics — whatever it is — can according to Bourbaki be described in one single language [Lm] and the content will be called Theory of Sets [T] .
METAMATHEMATICS
Because the one single language Lm used to describe the Theory of Sets shall be a language with certain properties one has to define these properties with some other language, which is talking about Lm. As language for this job Bourbaki is using the ordinary language [Lo].(p.9) But the reasoning within which one is using this ordinary language is called metamathematics (cf. P.10f). Within the metamathematical point of view the language Lm under investigation is seen as a set of previously given objetcs without any kind of meaning, where only the assigned order is of importance.(cf. p.10): “… metamathematical ‘arguments’ usually assert that when a succession of operations has been performed on a text of a given type, then the final text will be of another given type.”(p.10)
What looks here at first glance as the complete formalization of mathematics it is not. Bourbaki states clearly that “formalized mathematics cannot in practice be written down in full“(p.11) There has to be assumed as ‘last resort’ the assumption of a common sense of the mathematician and the intuition of the reader. (cf. p.11)
COGNITIVE-SEMIOTIC TURN
This conflict between at one hand of the idea of a formalization of Mathematics by a formalized language Lm and on the other hand by the well known proof of Gödel [4] of the incompleteness of the axioms for classical arithmetic (cf. p.12) is not a real conflict as long as one takes into account — as Bourbaki points out — that the ‘content of mathematics’ is only given in different layers of languages (Lm, Lo, …) which again are embedded in a presupposed ‘common sense’ which is nothing else as the cognitive machinery of human persons including an embedded meaning function relating different kinds of knowledge into different kinds of — internal as well as external — expressions of some language L. Thus any kind of a ‘reduction of meaning’ seems never to be a ‘complete reduction’ but only a ‘technical reduction’ to introduce some ‘artificial (abstract) objetcs’ which can only work because of their embedding in some richer context.
This new perspective can be called the cognitive-semiotic turn which became possible by new insights of modern brain sciences in connection with pysychology and semiotics.
From this new point of view one can derive the idea of embedding metamathemics in a more advanced actor theory providing all the ingredients to make metamathematics more ‘rational’.
OUTLINE OF ACTOR THEORY
Figure 1: Actor theory first outline
The details of the Actor Theory [AT] can become quite complex. Here a first outline of the basic ideas and what this can mean for a metamathematical point of view of mathematics.
World is not World
The main idea is founded in the new insights of Biology and Neuro-Psychology of the handling of body-world interactions as exercised by humans. One of the main insights is rooted back to von Uexküll [5] more than 100 years ago, when he described how every biological organism perceives and handles some world outside of the body with the inner neuronal structures given! Thus different life forms in the same outside world W will peceive and act neuronally in different worlds! Brain X acts in world X which is somehow related to the outside world W as well as Brain Y acts in world Y which also is somehow related to the outside world W.
These basic insights relate as well to more developed life forms as such as humans are. We as humans do not perceive and think the world W outside of our bodies ‘as it is’ but only as our brain inside our body can process all the body states related to the outside world in the mode of the inside brain. Thus if the different human individuals would have different brains they would live in different worlds and their would be no chance of a simple communication. But as we know from physiological and behavioral studies humans can to some extend communicate successfully. Thus there exists inside of every human individual a human-processed world h(W) which is different from other life-forms like a rat, a worm, an octopus, etc.
From this basic insight it follows that if we speak about the worldW we do indeed not speak about the world W directly but about the world W as it is processed in a human-specific manner, the world h(W). This has many implications.
Because we know already that the world h(W) is not a static but a dynamic world depending from our learning history it can happen — and it happens all the time — that different individuals have different learning histories. This can result in quite strong differences of experience and knowledge attached to different individuals, which can prevent a simple understanding between such individuals: the learned world h1(W) can to some degree be different from the learned world h2(W) such that a simple and direct understanding will not be possible.
This difference between the outside world W and the processed inside world h(W) relates to the communication too! The spoken or written expressions E of some language L are belonging to the outside world. They have a counterpart in the inner world as inner expressions E*, which can be associated with all kinds of processed inner states of the inner world h(W) = W*. These possible — and learned — associations between inner expressions and inner states belonging to h(W) is assumed here to be that what commonly is called meaning. Thus one has to assume an internal meaning function μ which maps the internal expressions E* of some internal language L* into parts of the internally processed world h(W)=W* and vice versa. Thus we have μ: E* <—> W*. Thus μ(e*) would point to some part w* of the internally processed world W* as the ‘meaning’ of the internal expression e*.
This semiotic architecture of human beings enables a nearly infinite space of expressions as well as associated meanings definable during learning processes. This is powerful, but it is also very demanding for the speaker-hearer: to enable a succesful communication between different speaker-hearer these have to train their language usage under sufficient similar conditions thereby constructing individual meaning functions which work — hopefully — sufficiently similar. If not then communication can slow down, can produce lots of misunderstandings or can even break down completely. [6]
In the case of mathematics it is a long debated question whether mathematics can be reduced to the expressions Em of some mathematical language Lm or if mathematics has some mathematical objects on its own which are different from the expressions. If one would assume that mathematics has no objects on its own but only some expressions Em, then it would become difficult to argue whether exactly these expressions Em should be used and not some other expressions Ex. Moreover to classify expressions as ‘axioms’ or ‘theorems’ would be completely arbitrary. The only ‘anchor’ of non-arbitrariness would consist in some formal criteria of a formal consistency which would disable the formal generation of pairs of expressions {a,a*} where one is excluding the other. But even such a formal consistency presupposes some criteria which are beyond the expressions as such! Thus mathematics would need some criteria outside mathematics. This can be understood as an argument for metamathematics. But according to Bourbaki metamathematics is defined as a set of operations on given expressions without a specific meaning. This is not enough to establish formal consistency! Thus even metamathematics is pointing to something outside of given mathematical expressions. What can this be?
PART 2
To be continued …
COMMENTS
[*] More recent versions of the specification of the oksimo oftware can be found in the bolg oksimo.org. Unfortunately are the texts in that blog — at the time if this writing — still only in German. Hopefully this will change in the future.
[1] Bourbaki group in Wikipedia [EN]: https://en.wikipedia.org/wiki/Nicolas_Bourbaki
[2] N.Bourbaki (1970), Theory of Sets, Series: ELEMENTS OF MATHEMATICS, Springer, Berlin — Heidelberg — New York (Engl. Translation from the French edition 1970)
[3] The first time when the author of this text has encountered the book was some time between 1984 – 1987 while being a PhD-student at the Ludwig-Maximilians Univesty [LMU] in Munich. It was in a seminar with Prof. Peter Hinst about structural approaches to Philosophy of Science. The point of view at that time was completely different to the point of view applied in this text.
[4] Kurt Goedel.Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme, i.Monatshefte fuer Mathematik und Physik, 38:173–98, 1931.
[5] Jakob von Uexküll, 1909, Umwelt und Innenwelt der Tiere. Berlin: J.Springer.
[6] Probably everybody has made the experience in his life of being part of a situation where nobody speaks a language, which one is used to speak …
In the uffmm review section the different papers and books are discussed from the point of view of the oksimo paradigm. [2] Here the author reads the book “Logic. The Theory Of Inquiry” by John Dewey, 1938. [1]
DISCUSSION after the PREFACE DEWEY 1938/9
Following the description and interpretation of Dewey’s preface the author takes here the time for a short discussion how one can describe the first idea of Dewey about the view of inquiry as a continuum, as a process with some outcome.
FIGURE 1: Dewey’s view of an inquiry as a continuous process slightly interpreted
In the interpretation of Dewey the author takes the starting point with the view of Dewey of an inquiry as a continuous process.(cf. figure 1)
In his description of such an inquiry in the spirit of pragmatism Dewey claims that the process ends up in a situation which is caused by the preceding parts of the process. He calls the ‘end’ of such an inquiry process a consequence (or: consequences) which can be used as a test of the validity of the assumed propositions.
Validity of the proposition
Taking only the words of Dewey “validity of the … propositions” this can be interpreted in many ways. The author of this texts interprets these words with a conceptual framework based on the today knowledge about cognitive processing, which is also used in the oksimo paradigm.
In this modern framework of cognitive processing we know that one has at least to distinguish the dimension of the real world with real situations and as part of the real situation real objects, real actions (and more) on the one hand and inner states of an actor on the other hand.
As part of this overall scenario one has to distinguish at least the following main dimensions: (i) the overall observable realbehavior of an actor and realexpressions as part of the observable behavior, which can be classified (by learned knowledge) as expressions of some normal language, and (ii) the not-observable inner states of the actor reflecting in a special way the observable situation as such as well as the perceivable (by hearing, reading, …) expressions of the known language as part of the observable situation.
The main point here in the case of an actor of the life form homo sapiens is the fact that a homo sapiens actor is able to map the inner counterpart of the external expressions into the inner counterpart of the perceived real situation as part of a cognitive machinery (including memory) in a way that this internal mapping — here called meaning function — encodes part of the cognitive states into expressions (and vice versa).
Using this knowledge about the cognitive closure of expressions known as part of a learned language one can understand, why arbitrary aspects of the observable real situation can be encoded by the (built-in as well as learned ) meaning function into certain expressions in a way, that a hearer-reader of these expressions can decode these expressions (with his individual meaning function) to some extend into the inner cognitive states corresponding to the perceivable world.
In the light of this modern cognitive framework can a proposition be interpreted as part of the inner cognitive states corresponding either actually to some perceived real situation (then it is qualified as being valid) or not. And because the meaning function can encode such propositions with some expressions we can have external expressions as a real counterpart to such propositions.
Inquiry as a process
Thus inquiry understood by Dewey as a continuous process starts with some starting real situation which can be accompanied by appropriate (encoded) expressions of the selected language. During the course of inquiry the situation can change caused by actions which after some finite period of time lead to a final situation (‘final’ is not an absolute’ category here; it depends from the decision of the researchers what they think has to be understood as ‘final’).
While the possible process of inquiry in the beginning is quite unclear, open, undefined, turns the real process of actions (including speaking/ writing expressions) this undefined/ possibly infinite situation step by step into some real defined finite process by making decisions which enable selections of concrete actions/ things out of many options.
Test of the validity
Dewey speaks about the end of an inquiry process as a consequence which can be seen as a test of the validity of the propositions. If the ‘validity of a proposition’ is a qualification of the relation between a proposition as a cognitive counterpart of some perceivable real situation and this real situation then the wording ‘test of’ could be interpreted in the way that the reached situation by an inquiry process is in a sufficient agreement with an assumed proposition. But this would require that the researchers have in the beginning of their research have an idea of the intended/ wanted outcome. This sounds a bit strange: Why doing some inquiry if I already have an idea of the outcome?
This leads to the everyday life situation where we encounter permanently the following situations: (i) We know of situations which we qualify as being unsatisfying by some reasons (‘Gerd is hungry’, ‘Peter is tired’, ‘Ada is unhappy’, ‘John needs some money’, ‘Mary has a question’, ‘Bill looks for some new flat’, …); and (ii) some kind of visions/ goals, which we want to achieve. At the moment of having a vision/ goal within our inner cognitive states we can decide to achieve it through a real process of real actions. In some cases (being hungry) we probably have some options how to accomplish the goal by starting a series of concrete actions to get some food. And then the food is a consequence of the preceding process of searching and at the same time an answer to the triggering proposition. In other cases (‘being unhappy’ it can be difficult to find a good answer: what really is missing? What can I do? If Ada would decide to clarify her state it could happen that she tries a lot of options eventually lasting a long time (days, weeks, months, …). But nevertheless one day it can happen that she suddenly has the feeling, that she is no longer unhappy. In that case she can qualify the reached situation as a consequence of her preceding process of inquiry and indeed as an answer to the triggering proposition of being unhappy. In this case ‘feeling happy’ as an answer to ‘feeling unhappy’ has not been a clear expectation in the beginning, but a causing proposition which has lead Ada into a search process which finally produced a situation which enabled this new feeling of ‘being happy’ which — perhaps –is a quite ‘new’ feeling which nevertheless is understood by her as an ‘answer’.
Goals: defined and undefined
These simple examples point at the fact that homo sapiens actors can start inquiries either by somehow clearly defined goals or with ‘undefined goals‘ but caused by a ‘defined problem‘.
While the wording ‘undefined goal’ seems a little bit ‘fuzzy’ in the beginning, it is of great importance for the case of inquiry. This has to do with the concept of a possible future.
While the actual real world — and even those parts of it, which we have memorized somehow — is something we can perceive and where we can point at, is ‘future’ a non-object: we have strictly no chance to perceive directly any kind of future. Future is the radical unknown. What we can do — and in our everyday life we do it often — is, that we try to imagine by our past knowledge to get some hints out of the past for some patterns, regularities which can be used as ‘hints’ what perhaps can happen again with some probability as an upcoming situation because there exist some hidden mechanism in the real world which is causing a repetition (e.g. we have learned about phenomena which we call ‘gravity’ which we use as a cognitive tool to make some forecasts). But such learned patterns of the past do not explain everything and there is no absolute guarantee that these patterns will work ever. Moreover, we are living in a world which is maximal complex because of a multitude of patterns simultaneously at work, and there are many patterns (the behavior of biological systems) which are inherently non-linear, nondeterministic.
Thus doing inquiries into future states which are caused by defined problems where the answer isnot yet known are radically different to inquiries with defined problems already accompanied with a clear goal. Although defined problems with defined goals can be quite difficult (e.g. searching for better material, better production processes etc. to get a better electrical battery for everyday usage) the case of an undefined goal is much more demanding. This case is the standard case for real research (as in the case of Ada: What makes her happy?).
COMMENTS
[1] John Dewey, Logic. The Theory Of Inquiry, New York, Henry Holt and Company, 1938 (see: https://archive.org/details/JohnDeweyLogicTheTheoryOfInquiry with several formats; I am using the kindle (= mobi) format: https://archive.org/download/JohnDeweyLogicTheTheoryOfInquiry/%5BJohn_Dewey%5D_Logic_-_The_Theory_of_Inquiry.mobi . This is for the direct work with a text very convenient. Additionally I am using a free reader ‘foliate’ under ubuntu 20.04: https://github.com/johnfactotum/foliate/releases/). Additionally I am using a free reader ‘foliate’ under ubuntu 20.04: https://github.com/johnfactotum/foliate/releases/). The page numbers in the text of the review — like (p.13) — are the page numbers of the ebook as indicated in the ebook-reader foliate.(There exists no kindle-version for linux (although amazon couldn’t work without linux servers!))
[2] Gerd Doeben-Henisch, 2021, uffmm.org, THE OKSIMO PARADIGM An Introduction (Version 2), https://www.uffmm.org/wp-content/uploads/2021/03/oksimo-v1-part1-v2.pdf
Here is another talk completely unplugged about Dewey’s Logic. It’s focus is on a hypothetical conceptual framework for the wording of ‘valid propositions’ in the context of an inquiry.
This text is part of a philosophy of science analysis of the case of the oksimo software (oksimo.com). A specification of the oksimo software from an engineering point of view can be found in four consecutive posts dedicated to the HMI-Analysis for this software.
POPPERs POSITION IN THE CHAPTERS 1-17
In my reading of the chapters 1-17 of Popper’s The Logic of Scientific Discovery [1] I see the following three main concepts which are interrelated: (i) the concept of a scientific theory, (ii) the point of view of a meta-theory about scientific theories, and (iii) possible empirical interpretations of scientific theories.
Scientific Theory
A scientific theory is according to Popper a collection of universal statements AX, accompanied by a concept of logical inference ⊢, which allows the deduction of a certain theorem t if one makes some additional concrete assumptions H.
Example: Theory T1 = <AX1,⊢>
AX1= {Birds can fly}
H1= {Peter is a bird}
⊢: Peter can fly
Because there exists a concrete object which is classified as a bird and this concrete bird with the name ‘Peter’ can fly one can infer that the universal statement could be verified by this concrete bird. But the question remains open whether all observable concrete objects classifiable as birds can fly.
One could continue with observations of several hundreds of concrete birds but according to Popper this would not prove the theory T1 completelytrue. Such a procedure can only support a numerical universality understood as a conjunction of finitely many observations about concrete birds like ‘Peter can fly’ & ‘Mary can fly’ & …. &’AH2 can fly’.(cf. p.62)
The only procedure which is applicable to a universal theory according to Popper is to falsify a theory by only one observation like ‘Doxy is a bird’ and ‘Doxy cannot fly’. Then one could construct the following inference:
AX1= {Birds can fly}
H2= {Doxy is a bird, Doxy cannot fly}
⊢: ‘Doxy can fly’ & ~’Doxy can fly’
If a statement A can be inferred and simultaneously the negation ~A then this is called a logical contradiction:
{AX1, H2} ⊢‘Doxy can fly’ & ~’Doxy can fly’
In this case the set {AX1, H2} is called inconsistent.
If a set of statements is classified as inconsistent then you can derive from this set everything. In this case you cannot any more distinguish between true or false statements.
Thus while the increase of the number of confirmed observations can only increase the trust in the axioms of a scientific theory T without enabling an absolute proof a falsification of a theory T can destroy the ability of this theory to distinguish between true and false statements.
Another idea associated with this structure of a scientific theory is that the universal statements using universal concepts are strictly speaking speculative ideas which deserve some faith that these concepts will be provable every time one will try it.(cf. p.33, 63)
Meta Theory, Logic of Scientific Discovery, Philosophy of Science
Talking about scientific theories has at least two aspects: scientific theories as objects and those who talk about these objects.
Those who talk about are usually Philosophers of Science which are only a special kind of Philosophers, e.g. a person like Popper.
Reading the text of Popper one can identify the following elements which seem to be important to describe scientific theories in a more broader framework:
A scientific theory from a point of view of Philosophy of Science represents a structure like the following one (minimal version):
MT=<S, A[μ], E, L, AX, ⊢, ET, E+, E-, true, false, contradiction, inconsistent>
In a shared empirical situation S there are some human actors A as experts producing expressions E of some language L. Based on their built-in adaptive meaning function μ the human actors A can relate properties of the situation S with expressions E of L. Those expressions E which are considered to be observable and classified to be true are called true expressions E+, others are called false expressions E-. Both sets of expressions are true subsets of E: E+ ⊂ E and E- ⊂ E. Additionally the experts can define some special set of expressions called axioms AX which are universal statements which allow the logical derivation of expressions called theorems of the theory T ET which are called logically true. If one combines the set of axioms AX with some set of empirically true expressions E+ as {AX, E+} then one can logically derive either only expressions which are logically true and as well empirically true, or one can derive logically true expressions which are empirically true and empirically false at the same time, see the example from the paragraph before:
{AX1, H2} ⊢‘Doxy can fly’ & ~’Doxy can fly’
Such a case of a logically derived contradiction A and ~A tells about the set of axioms AX unified with the empirical true expressions that this unified set confronted with the known true empirical expressions is becoming inconsistent: the axioms AX unified with true empirical expressions can not distinguish between true and false expressions.
Popper gives some general requirements for the axioms of a theory (cf. p.71):
Axioms must be free from contradiction.
The axioms must be independent , i.e . they must not contain any axiom deducible from the remaining axioms.
The axioms should be sufficient for the deduction of all statements belonging to the theory which is to be axiomatized.
While the requirements (1) and (2) are purely logical and can be proved directly is the requirement (3) different: to know whether the theory covers all statements which are intended by the experts as the subject area is presupposing that all aspects of an empirical environment are already know. In the case of true empirical theories this seems not to be plausible. Rather we have to assume an open process which generates some hypothetical universal expressions which ideally will not be falsified but if so, then the theory has to be adapted to the new insights.
Empirical Interpretation(s)
Popper assumes that the universal statements of scientific theories are linguistic representations, and this means they are systems of signs or symbols. (cf. p.60) Expressions as such have no meaning. Meaning comes into play only if the human actors are using their built-in meaning function and set up a coordinated meaning function which allows all participating experts to map properties of the empirical situation S into the used expressions as E+ (expressions classified as being actually true), or E- (expressions classified as being actually false) or AX (expressions having an abstract meaning space which can become true or false depending from the activated meaning function).
Examples:
Two human actors in a situation S agree about the fact, that there is ‘something’ which they classify as a ‘bird’. Thus someone could say ‘There is something which is a bird’ or ‘There is some bird’ or ‘There is a bird’. If there are two somethings which are ‘understood’ as being a bird then they could say ‘There are two birds’ or ‘There is a blue bird’ (If the one has the color ‘blue’) and ‘There is a red bird’ or ‘There are two birds. The one is blue and the other is red’. This shows that human actors can relate their ‘concrete perceptions’ with more abstract concepts and can map these concepts into expressions. According to Popper in this way ‘bottom-up’ only numerical universal concepts can be constructed. But logically there are only two cases: concrete (one) or abstract (more than one). To say that there is a ‘something’ or to say there is a ‘bird’ establishes a general concept which is independent from the number of its possible instances.
These concrete somethings each classified as a ‘bird’ can ‘move’ from one position to another by ‘walking’ or by ‘flying’. While ‘walking’ they are changing the position connected to the ‘ground’ while during ‘flying’ they ‘go up in the air’. If a human actor throws a stone up in the air the stone will come back to the ground. A bird which is going up in the air can stay there and move around in the air for a long while. Thus ‘flying’ is different to ‘throwing something’ up in the air.
The expression ‘A bird can fly’ understood as an expression which can be connected to the daily experience of bird-objects moving around in the air can be empirically interpreted, but only if there exists such a mapping called meaning function. Without a meaning function the expression ‘A bird can fly’ has no meaning as such.
To use other expressions like ‘X can fly’ or ‘A bird can Y’ or ‘Y(X)’ they have the same fate: without a meaning function they have no meaning, but associated with a meaning function they can be interpreted. For instance saying the the form of the expression ‘Y(X)’ shall be interpreted as ‘Predicate(Object)’ and that a possible ‘instance’ for a predicate could be ‘Can Fly’ and for an object ‘a bird’ then we could get ‘Can Fly(a Bird)’ translated as ‘The object ‘a Bird’ has the property ‘can fly” or shortly ‘A Bird can fly’. This usually would be used as a possible candidate for the daily meaning function which relates this expression to those somethings which can move up in the air.
Axioms and Empirical Interpretations
The basic idea with a system of axioms AX is — according to Popper — that the axioms as universal expressions represent a system of equations where the general terms should be able to be substituted by certain values. The set of admissible values is different from the set of inadmissible values. The relation between those values which can be substituted for the terms is called satisfaction: the values satisfy the terms with regard to the relations! And Popper introduces the term ‘model‘ for that set of admissible terms which can satisfy the equations.(cf. p.72f)
But Popper has difficulties with an axiomatic system interpreted as a system of equations since it cannot be refuted by the falsification of its consequences ; for these too must be analytic.(cf. p.73) His main problem with axioms is, that “the concepts which are to be used in the axiomatic system should be universal names, which cannot be defined by empirical indications, pointing, etc . They can be defined if at all only explicitly, with the help of other universal names; otherwise they can only be left undefined. That some universal names should remain undefined is therefore quite unavoidable; and herein lies the difficulty…” (p.74)
On the other hand Popper knows that “…it is usually possible for the primitive concepts of an axiomatic system such as geometry to be correlated with, or interpreted by, the concepts of another system , e.g . physics …. In such cases it may be possible to define the fundamental concepts of the new system with the help of concepts which were originally used in some of the old systems .”(p.75)
But the translation of the expressions of one system (geometry) in the expressions of another system (physics) does not necessarily solve his problem of the non-empirical character of universal terms. Especially physics is using also universal or abstract terms which as such have no meaning. To verify or falsify physical theories one has to show how the abstract terms of physics can be related to observable matters which can be decided to be true or not.
Thus the argument goes back to the primary problem of Popper that universal names cannot not be directly be interpreted in an empirically decidable way.
As the preceding examples (1) – (4) do show for human actors it is no principal problem to relate any kind of abstract expressions to some concrete real matters. The solution to the problem is given by the fact that expressions E of some language L never will be used in isolation! The usage of expressions is always connected to human actors using expressions as part of a language L which consists together with the set of possible expressions E also with the built-in meaning function μ which can map expressions into internal structures IS which are related to perceptions of the surrounding empirical situation S. Although these internal structures are processed internally in highly complex manners and are — as we know today — no 1-to-1 mappings of the surrounding empirical situation S, they are related to S and therefore every kind of expressions — even those with so-called abstract or universal concepts — can be mapped into something real if the human actors agree about such mappings!
Example:
Lets us have a look to another example.
If we take the system of axioms AX as the following schema: AX= {a+b=c}. This schema as such has no clear meaning. But if the experts interpret it as an operation ‘+’ with some arguments as part of a math theory then one can construct a simple (partial) model m as follows: m={<1,2,3>, <2,3,5>}. The values are again given as a set of symbols which as such must not ave a meaning but in common usage they will be interpreted as sets of numbers which can satisfy the general concept of the equation. In this secondary interpretation m is becoming a logically true (partial) model for the axiom Ax, whose empirical meaning is still unclear.
It is conceivable that one is using this formalism to describe empirical facts like the description of a group of humans collecting some objects. Different people are bringing objects; the individual contributions will be reported on a sheet of paper and at the same time they put their objects in some box. Sometimes someone is looking to the box and he will count the objects of the box. If it has been noted that A brought 1 egg and B brought 2 eggs then there should according to the theory be 3 eggs in the box. But perhaps only 2 could be found. Then there would be a difference between the logically derivedforecast of the theory 1+2 = 3 and the empirically measured value 1+2 = 2. If one would define all examples of measurement a+b=c’ as contradiction in that case where we assume a+b=c as theoretically given and c’ ≠ c, then we would have with ‘1+2 = 3′ & ~’1+2 = 3’ a logically derived contradiction which leads to the inconsistency of the assumed system. But in reality the usual reaction of the counting person would not be to declare the system inconsistent but rather to suggest that some unknown actor has taken against the agreed rules one egg from the box. To prove his suggestion he had to find this unknown actor and to show that he has taken the egg … perhaps not a simple task … But what will the next authority do: will the authority belief the suggestion of the counting person or will the authority blame the counter that eventually he himself has taken the missing egg? But would this make sense? Why should the counter write the notes how many eggs have been delivered to make a difference visible? …
Thus to interpret some abstract expression with regard to some observable reality is not a principal problem, but it can eventually be unsolvable by purely practical reasons, leaving questions of empirical soundness open.
SOURCES
[1] Karl Popper, The Logic of Scientific Discovery, First published 1935 in German as Logik der Forschung, then 1959 in English by Basic Books, New York (more editions have been published later; I am using the eBook version of Routledge (2002))
This text is part of a philosophy of science analysis of the case of the oksimo software (oksimo.com). A specification of the oksimo software from an engineering point of view can be found in four consecutive posts dedicated to the HMI-Analysis for this software.
STARTING WITH SOMETHING ‘REAL’
A basic idea of the oksimo behavior space is to bring together differenthuman actors, let them share their knowledge and experience of some real part of their world and then they are invited to think about, how one can improve this part.
What sounds so common — some real part of their world — isn’t necessarily easy to define.
As has been discussed in the preceding post to make language expressions decidable this is only possible if certain practical requirements are fulfilled. The ‘practical recipe’
makedecidable : S x Ahum x E —> E x {true, false}
given in the preceding post claims that you — if you want to know whether an expression E is concrete and can be classified as ‘true’ or ‘false’ — have to ask a human actor Ahum , which is part of the same concrete situation S as you, and he/ she should confirm or disclaim whether the expression E can be interpreted as being ‘true’ or ‘false’ in this situation S.
Usually, if there is a real concrete situation S with you and some other human actor A, then you both will have a perception of the situation, you will both have internal abstraction processes with abstract states, you will have mappings from such abstracted states into some expressions of your internal language Lint and you and the other human actor A can exchange external expressions corresponding to the inner expressions and thereby corresponding to the internal abstracted states of the situation S. Even if the used language expressions E — like for instance ‘There is a white wooden table‘ — will contain abstract expressions/ universal expressions like ‘white’, ‘wooden’, ‘table’, even then you and the other human actor will be able to decide whether there are properties of the concrete situation which are fitting as accepted instances the universal parts of the language expression ‘There is a white wooden table‘.
Thus being in a real situation S with the other human actors enables usually all participants of the situation to decide language expressions which are related to the situation.
But what consequences does it have if you are somehow abroad, if you are not actually part of the situation S? Usually — if you are hearing or reading an expression like ‘There is a white wooden table‘ — you will be able to get an idea of the intended meaning only by your learned meaning function φ which maps the external expression into an internal expression and further maps the internal expression into the learned abstracted states. While the expressions ‘white’ and ‘wooden’ are perhaps rather ‘clear’ the expression ‘table’ is today associated with many, many different possible concrete matters and only by hearing or reading it is not possible to decide which of all these are the intended concrete matter. Thus although if you would be able to decided in the real situation S which of these many possible instances are given in the real situation, with the expression only disconnected from the situation, you are not able to decide whether the expression is true or not. Thus the expression has the cognitive status that it perhaps can be true but actually you cannot decide.
REALITY SUPPORTERS
Between the two cases (i) being part of he real situation S or (ii) being disconnected from the real situation S there are many variants of situations which can be understood as giving some additional support to decide whether an expression E is rather true or not.
The main weakness for not being able to decide is the lack of hints to narrow down the set of possible interpretations of learned meanings by counter examples. Thus while a human actor could have learned that the expression ‘table’ can be associated with for instance 25 different concrete matters, then he/ she needs some hints/ clues which of these possibilities can be ruled out and thereby the actor could narrow down the set of possible learned meanings to then only for instance left possibly 5 of 25.
While the real situation S can not be send along with the expression it is possible to send for example a drawing of the situation S or a photo. If properties are involved which deserve different senses like smelling or hearing or touching or … then a photo would not suffice.
Thus to narrow down the possible interpretations of an expression for someone who is not part of the situation it can be of help to give additional ‘clues’ if possible, but this is not always possible and moreover it is always more or less incomplete.
This text is part of a philosophy of science analysis of the case of the oksimo software (oksimo.com). A specification of the oksimo software from an engineering point of view can be found in four consecutive posts dedicated to the HMI-Analysis for this software.
THE OKSIMO EVENT SPACE
The characterization of the oksimo software paradigm starts with an informal characterization of the oksimo software event space.
EVENT SPACE
An event space is a space which can be filled up by observable events fitting to the species-specific internal processed environment representations [1], [2] here called internal environments [ENVint]. Thus the same externalenvironment [ENV] can be represented in the presence of 10 different species in 10 different internal formats. Thus the expression ‘environment’ [ENV] is an abstract concept assuming an objective reality which is common to all living species but indeed it is processed by every species in a species-specific way.
In a human culture the usual point of view [ENVhum] is simultaneous with all the other points of views [ENVa] of all the other other species a.
In the ideal case it would be possible to translate all species-specific views ENVa into a symbolic representation which in turn could then be translated into the human point of view ENVhum. Then — in the ideal case — we could define the term environment [ENV] as the sum of all the different species-specific views translated in a human specific language: ∑ENVa = ENV.
But, because such a generalized view of the environment is until today not really possible by practical reasons we will use here for the beginning only expressions related to the human specific point of view [ENVhum] using as language an ordinary language [L], here the English language [LEN]. Every scientific language — e.g. the language of physics — is understood here as a sub language of the ordinary language.
EVENTS
An event [EV] within an event space [ENVa] is a change[X] which can be observed at least from the members of that species [SP] a which is part of that environment ENV which enables a species-specific event space [ENVa]. Possibly there can be other actors around in the environment ENV from different species with their specific event space [ENVa] where the content of the different event spaces can possible overlap with regard to certain events.
A behavior is some observable movement of the body of some actor.
Changes X can be associated with certain behavior of certain actors or with non-actor conditions.
Thus when there are some human or non-human actors in an environment which are moving than they show a behavior which can eventually be associated with some observable changes.
CHANGE
Besides being associated with observable events in the (species specific) environment the expression change is understood here as a kind of inner state in an actor which can comparepast (stored) states Spast with an actual state Snow. If the past and actual state differ in some observable aspect Diff(Spast, Snow) ≠ 0, then there exists some change X, or Diff(Spast, Snow) = X. Usually the actor perceiving a change X will assume that this internal structure represents something external to the brain, but this must not necessarily be the case. It is of help if there are other human actors which confirm such a change perception although even this does not guarantee that there really is a change occurring. In the real world it is possible that a whole group of human actors can have a wrong interpretation.
SYMBOLIC COMMUNICATION AND MEANING
It is a specialty of human actors — to some degree shared by other non-human biological actors — that they not only can built up internal representations ENVint of the reality external to the brain (the body itself or the world beyond the body) which are mostly unconscious, partially conscious, but also they can built up structures of expressions of an internal language Lint which can be mimicked to a high degree by expressions in the body-external environment ENV called expressions of an ordinary language L.
For this to work one has to assume that there exists an internal mapping from internal representations ENVint into the expressions of the internal language Lint as
meaning : ENVint <—> Lint.
and
speaking: Lint —> L
hearing: Lint <— L
Thus human actors can use their ordinary language L to activate internal encodings/ decodings with regard to the internal representations ENVint gained so far. This is called here symbolic communication.
NO SPEECH ACTS
To classify the occurrences of symbolic expressions during a symbolic communication is a nearly infinite undertaking. First impressions of the unsolvability of such a classification task can be gained if one reads the Philosophical Investigations of Ludwig Wittgenstein. [5] Later trials from different philosophers and scientists — e.g. under the heading of speech acts [4] — can not fully convince until today.
Instead of assuming here a complete scientific framework to classify occurrences of symbolic expressions of an ordinary language L we will only look to some examples and discuss these.
KINDS OF EXPRESSIONS
In what follows we will look to some selected examples of symbolic expressions and discuss these.
(Decidable) Concrete Expressions [(D)CE]
It is assumed here that two human actors A and B speaking the same ordinary language L are capable in a concrete situation S to describeobjects OBJ and properties PROP of this situation in a way, that the hearer of a concrete expression E can decide whether the encoded meaning of that expression produced by the speakeris part of the observable situation S or not.
Thus, if A and B are together in a room with a wooden white table and there is a enough light for an observation then B can understand what A is saying if he states ‘There is a white wooden table.‘
To understand means here that both human actors are able to perceive the wooden white table as an object with properties, their brains will transform these external signals into internal neural signals forming an inner — not 1-to-1 — representationENVint which can further be mapped by the learned meaning function into expressions of the inner language Lint and mapped further — by the speaker — into the external expressions of the learned ordinary language L and if the hearer can hear these spoken expressions he can translate the external expressions into the internal expressions which can be mapped onto the learned internal representations ENVint. In everyday situations there exists a high probability that the hearer then can respond with a spoken ‘Yes, that’s true’.
If this happens that some human actor is uttering a symbolic expression with regard to some observable property of the external environment and the other human actor does respond with a confirmation then such an utterance is called here a decidable symbolic expression of the ordinary language L. In this case one can classify such an expression as being true. Otherwise the expression is classified as being not true.
The case of being not true is not a simple case. Being not true can mean: (i) it is actually simply not given; (ii) it is conceivable that the meaning could become true if the external situation would be different; (iii) it is — in the light of the accessible knowledge — not conceivable that the meaning could become true in any situation; (iv) the meaning is to fuzzy to decided which case (i) – (iii) fits.
Cognitive Abstraction Processes
Before we talk about (Undecidable) Universal Expressions [(U)UE] it has to clarified that the internal mappings in a human actor are not only non-1-to-1 mappings but they are additionally automatic transformation processes of the kind that concrete perceptions of concrete environmental matters are automatically transformed by the brain into different kinds of states which are abstracted states using the concrete incoming signals as a trigger either to start a new abstracted state or to modify an existing abstracted state. Given such abstracted states there exist a multitude of other neural processes to process these abstracted states further embedded in numerous different relationships.
Thus the assumed internal language Lint does not map the neural processes which are processing the concrete events as such but the processed abstracted states! Language expressions as such can never be related directly to concrete material because this concrete material has no direct neural basis. What works — completely unconsciously — is that the brain can detect that an actual neural pattern nn has some similarity with a given abstracted structure NN and that then this concrete pattern nn is internally classified as an instance of NN. That means we can recognize that a perceived concrete matter nn is in ‘the light of’ our available (unconscious) knowledge an NN, but we cannot argue explicitly why. The decision has been processed automatically (unconsciously), but we can become aware of the result of this unconscious process.
Universal (Undecidable) Expressions [U(U)E]
Let us repeat the expression ‘There is a white wooden table‘ which has been used before as an example of a concrete decidable expression.
If one looks to the different parts of this expression then the partial expressions ‘white’, ‘wooden’, ‘table’ can be mapped by a learned meaning function φ into abstracted structures which are the result of internal processing. This means there can be countable infinite many concrete instances in the external environment ENV which can be understood as being white. The same holds for the expressions ‘wooden’ and ‘table’. Thus the expressions ‘white’, ‘wooden’, ‘table’ are all related to abstracted structures and therefor they have to be classified as universal expressions which as such are — strictly speaking — not decidable because they can be true in many concrete situations with different concrete matters. Or take it otherwise: an expression with a meaning function φ pointing to an abstracted structure is asymmetric: one expression can be related to many different perceivable concrete matters but certain members of a set of different perceived concrete matters can be related to one and the same abstracted structure on account of similarities based on properties embedded in the perceived concrete matter and being part of the abstracted structure.
In a cognitive point of view one can describe these matters such that the expression — like ‘table’ — which is pointing to a cognitive abstracted structure ‘T’ includes a set of properties Π and every concrete perceived structure ‘t’ (caused e.g. by some concrete matter in our environment which we would classify as a ‘table’) must have a ‘certain amount’ of properties Π* that one can say that the properties Π* are entailed in the set of properties Π of the abstracted structure T, thus Π* ⊆ Π. In what circumstances some speaker-hearer will say that something perceived concrete ‘is’ a table or ‘is not’ a table will depend from the learning history of this speaker-hearer. A child in the beginning of learning a language L can perhaps call something a ‘chair’ and the parents will correct the child and will perhaps say ‘no, this is table’.
Thus the expression ‘There is a white wooden table‘ as such is not true or false because it is not clear which set of concrete perceptions shall be derived from the possible internal meaning mappings, but if a concrete situation S is given with a concrete object with concrete properties then a speaker can ‘translate’ his/ her concrete perceptions with his learned meaning function φ into a composed expression using universal expressions. In such a situation where the speaker is part of the real situation S he/ she can recognize that the given situation is an instance of the abstracted structures encoded in the used expression. And recognizing this being an instanceinterprets the universal expression in a way that makes the universal expression fitting to a real given situation. And thereby the universal expression is transformed by interpretation with φ into a concrete decidable expression.
SUMMING UP
Thus the decisive moment of turning undecidable universal expressions U(U)E into decidable concrete expressions (D)CE is a human actor A behaving as a speaker-hearer of the used language L. Without a speaker-hearer every universal expressions is undefined and neither true nor false.
makedecidable : S x Ahum x E —> E x {true, false}
This reads as follows: If you want to know whether an expression E is concrete and as being concrete is ‘true’ or ‘false’ then ask a human actor Ahum which is part of a concrete situation S and the human actor shall answer whether the expression E can be interpreted such that E can be classified being either ‘true’ or ‘false’.
The function ‘makedecidable()’ is therefore the description (like a ‘recipe’) of a real process in the real world with real actors. The important factors in this description are the meaning functions inside the participating human actors. Although it is not possible to describe these meaning functions directly one can check their behavior and one can define an abstract model which describes the observable behavior of speaker-hearer of the language L. This is an empirical model and represents the typical case of behavioral models used in psychology, biology, sociology etc.
SOURCES
[1] Jakob Johann Freiherr von Uexküll (German: [ˈʏkskʏl])(1864 – 1944) https://en.wikipedia.org/wiki/Jakob_Johann_von_Uexk%C3%BCll
[2] Jakob von Uexküll, 1909, Umwelt und Innenwelt der Tiere. Berlin: J. Springer. (Download: https://ia802708.us.archive.org/13/items/umweltundinnenwe00uexk/umweltundinnenwe00uexk.pdf )
[3] Wikipedia EN, Speech acts: https://en.wikipedia.org/wiki/Speech_act
[4] Ludwig Josef Johann Wittgenstein ( 1889 – 1951): https://en.wikipedia.org/wiki/Ludwig_Wittgenstein
[5] Ludwig Wittgenstein, 1953: Philosophische Untersuchungen [PU], 1953: Philosophical Investigations [PI], translated by G. E. M. Anscombe /* For more details see: https://en.wikipedia.org/wiki/Philosophical_Investigations */
Integrating Engineering and the Human Factor (info@uffmm.org) eJournal uffmm.org ISSN 2567-6458, Nov 23-28, 2020
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de
Applying the original P-V-Pref Document structure to real cases it became clear that the everyday logic behind the classification of facts into problems [P] or visions [V] follows a kind of logic hidden in the semantic space of the used expressions. This text explains this hidden logic and what this means for our application.
While the general view of the whole process is quite clear there arose some hot debate about the everyday situation of the experts (here: citizens) and the concepts ‘reality [R]‘, ‘vision [V] (imagination of a state which is not yet real)’, ‘problem [P]‘, and ‘preference [Pref]‘. The members of my zevedi-working group (located at the INM (Frankfurt, Hessen, Germany) as well as a citizen from Dieburg (Hessen, Germany) associated with ‘reality’ also the different kinds of emotions being active in a person and they classified an imagination about a future state also as being real in a concrete person. With such a setting of the concepts it became difficult to motivate the logic illustrated in the video. The video — based on the preceding paper — talks about a vision v, which can turn a reality r into a problem p, and thereby generating a preference Pref = (v,r). A preference can possibly become a trigger of some change process.
Looking ahead
Before clarifying this discussion let as have a look ahead to the overall change process which constitutes the heart of the komega-software. Beginning with October 18, 2020 the idea of this overall change process has been described in this blog. Having some given situation S, the komega software allows the construction of change rules X, which can be applied onto a given situation S and a builtin simulator [sim] will generate a follow up situation S’ like sim(X,S)=S’ — or short: X(S) = S’ –, a process which can be repeated by using the output S’ as new input for a new cycle. At any time of this cyclic process one can ask whether the actual output S’ can be classified as successful. What is called ‘successful’ depends from the applied criteria. For the komega software at least two criteria are used. The most basic one looks to the ectual end state S’ of the simulation and computes the difference between the occurences of vision statements V in S’ and the occurrences of real statements R having been declared at the beginning as problems P as part of the start situation S. Ideally the real statements classified as problems should have been disappeared and the vision statements should be present. If the difference is bigger than some before agreed threshold theta than the actual end state S’ will be classified as a success, as a goal state in the light of the visions of the preferences, which triggered the change process.
Vision statement
In the context of the whole change process a vision statement is an expression e associated with some everyday language L and which describes in the understanding of the experts a state, which is in our mindes conceivable, imaginable, which is not given as a real state, but can eventually become a real state in some future. This disctinction presupposes that the expert can distinguish between an idea in his consciousness which is associated with some real state outside his consciousness — associated with a real state — and an idea, which is only inside his consciousness — associated with an imaginated state –. Looking from a second person to the expert this second person can observe the body of the expert and the world surrounding the body and can speak of the real world and the real body of the expert, but the inner states of the expert are hidden for this second person. Thus from the point of view of this second person there are no real imaginations, no real future states. But the expert can utter some expression e which has a meaning describing some state, which as such is not yet real, but which possibly could become real if one would change the actual reality (the actual everyday life, the actual city …) accordingly. Thus a vision statement is understood here as an expression e from the everyday language L uttered by some expert having a meaning which can be understood by the other persons describing some imginated state, which is not yet real but could eventually become real in some future ahead.
Creating problems, composing preferences
If at least one vision statement v is known by some experts, then it can happen, that an expert does relate this vision with some given reality r as part of the everyday life or with some absent reality r. Example: if an expert classifies some part of the city as having too much traffic (r1) and he has the vision of changing this into a situation where the traffic is lowered down by X% (v1), then this vision statement v1 can help to understand other experts to interpret the reality r1 in the light of the visiin v1 as a problemv1(r1) = p1. Classifying some reality r1 into a problem p1 is understood in the context of the komega software as making the reality r1 a candidate for a possible change in the sense that r1 should be replaced by v1. Having taken this stance — seeing the reality r1 as a problem p1 by the vision v1 –, than the experts have created a so-called preference Pref = (v1, p1) saying that the experts are preferring the imaginated possibly future state v1 more than the actual problem p1.
There is the special case, that an expert has uttered a vision statement v but there is no given reality which can be stated in a real statement r. Example: A company thinks that it can produce some vaccine against the disease Y in two years from now, like v2=’there is a vaccine against disease Y in yy’. Actually there exists no vaccine, but a disease is attacking the people. Because it is known, that the people can be made immune against the disease by an appropriate vaccine it makes sense to state r2=’There is no vaccine against the disease Y available’. Having the vision v2 this can turn the reality r2 into a problem p2 allowing the preference Pref=(v2,p2).
Triggering actions
If a group of experts generated a vision v — by several and different reaons (including emotions) –, having associated this with some given eality r, and they decided to generate by v(r)=p a preference Pr =(v,p), then it can happen , that these experts decide to start a change process beginning now with the given problem p and ending up with a situation in some future where the problem p disappeared and the vision has become real.
Summing up
The komega software allows the planning and testing of change processes if the acting experts have at least one preference Pref based on at least one vision statement v and at least one real statement r.
BITS OF PHILOSOPHY
Philosophical point of view
The above video (in German, DE) and the following lengthy remark after the video how to understand the basic concepts vision statement [v], real statement [r], problem statement [p], as well as preference [Pref] presuppose both a certain kind of philosophy. This philosophical point of view is outlined above in a simple drawing.
Basically there is a real human person (an actor) with a real brain embedded in some everyday world. The person can perceive parts of the every day world at every point of time. The most important reference point in time is the actual moment called NOW.
Inside the brain the human person can generate some cognitive structure triggered by perception, by memory and by some thinking. Having learned some everyday language L the human person can map the cognitive structure into an expression E associated with the language L. If the cognitive structure correlates with some real situation outside the body then the meaning of the expression E is classified as being a real statement, here named E1. But the brain can generate also cognitive structures and mapping these in expressions E without being actually correlated with some real situation outside. Such a statement is here called a vision statement, here named E2. A vision statement can eventually become correlated with some real situation outside in some future. In that case the vision statement transforms into a real statement E2, while the before mentioned real statement E1 can lose its correlation with a real situation.
FURTHER DISCUSSIONS
For further discussions have a look to this page too.
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.
The two papers of Tarski, which I do discuss here, have been published in 1936. Occasionally I have already read these paper many years ago but at that time I could not really work with these papers. Formally they seemed to be ’correct’, but in the light of my ’intuition’ the message appeared to me somehow ’weird’, not really in conformance with my experience of how knowledge and language are working in the real world. But at that time I was not able to explain my intuition to myself sufficiently. Nevertheless, I kept these papers – and some more texts of Tarski – in my bookshelves for an unknown future when my understanding would eventually change…
This happened the last days.
The collection of papers in the Case Studies Section deals with the
possible applications of the general concept of a GCA Generative Cul- tural Anthropology to all kinds of cultural processes. The GCA paradigm
has been derived from the formalized DAAI Distributed Actor-Actor In- teraction theory, which in turn is a development based on the common HMI Human Machine Interaction paradigm reformulated within the Sys- tems Engineering paradigm. The GCA is a very general and strong theory
paradigm, but, saying this, it is for most people difficult to understand,
because it is highly interdisciplinary, and it needs some formal technical
skills, which are not too common. During the work in the last three
months it became clear, that the original HMI and DAAI approach can
also be understood as the case of something which one could call ACA Applied Cultural Anthropology as part of an GCA. The concept of ACA
is more or less directly understandable for most people.
To work within the Generative Cultural Anthropology [GCA] Theory one needs a practical tool which allows the construction of dynamic world models, the storage of these models, their usage within a simulation game environment together with an evaluation tool. To prepare a simulation game within a Hybrid Simulation Game Environment [HSGE] one needs an
iterative development process which is described below.
CASE STUDY – SIMULATION GAMES – PHASE 1: Iterative Development of a Dynamic World Model – Part of the Generative Cultural Anthropology [GCA] Theory
Contents
1 Overview of the Whole Development Process
2 Cognitive Aspects of Symbolic Expressions
3 Symbolic Representations and Transformations
4 Abstract-Concrete Concepts
5 Implicit Structures Embedded in Experience
5.1 Example 1
