(This text is a translation from the German blog of the author. The translation is supported by the deepL Software)
CONTEXT
The meaning of and adherence to moral values in the context of everyday actions has always been a source of tension, debate, and tangible conflict.
This text will briefly illuminate why this is so, and why it will probably never be different as long as we humans are the way we are.
FINITE-INFINITE WORLD
In this text it is assumed that the reality in which we ‘find’ ourselves from childhood is a ‘finite’ world. By this is meant that no phenomenon we encounter in this world – ourselves included – is ‘infinite’. In other words, all resources we encounter are ‘finite’. Even ‘solar energy’, which is considered ‘renewable’ in today’s parlance, is ‘finite’, although this finiteness outlasts the lifetimes of many generations of humans.
But this ‘finiteness’ is no contradiction to the fact that our finite world is continuously in a ‘process of change’ fed from many sides. An ‘itself-self-changing finiteness’ is with it, a something which in and in itself somehow ‘points beyond itself’! The ‘roots’ of this ‘immanent changeability’ are to a large extent perhaps still unclear, but the ‘effects’ of the ‘immanent changeability’ indicate that the respective ‘concrete finite’ is not the decisive thing; the ‘respective concrete finite’ is rather a kind of ‘indicator’ for an ‘immanent change cause’ which ‘manifests itself’ by means of concrete finites in change. The ‘forms of concrete manifestations of change’ can therefore perhaps be a kind of ‘expression’ of something that ‘works immanently behind’.
In physics there is the pair of terms ‘energy’ and ‘mass’, the latter as synonym for ‘matter’. Atomic physics and quantum mechanics have taught us that the different ‘manifestations of mass/matter’ can only be a ‘state form of energy’. The everywhere and always assumed ‘energy’ is that ‘enabling factor’, which can ‘manifest’ itself in all the known forms of matter. ‘Changing-matter’ can then be understood as a form of ‘information’ about the ‘enabling energy’.
If one sets what physics has found out so far about ‘energy’ as that form of ‘infinity’ which is accessible to us via the experiential world, then the various ‘manifestations of energy’ in diverse ‘forms of matter’ are forms of concrete finites, which, however, are ultimately not really finite in the context of infinite energy. All known material finites are only ‘transitions’ in a nearly infinite space of possible finites, which is ultimately grounded in ‘infinite energy’. Whether there is another ‘infinity’ ‘beside’ or ‘behind’ or ‘qualitatively again quite different to’ the ‘experienceable infinity’ is thus completely open.”[1]
EVERYDAY EXPERIENCES
Our normal life context is what we now call ‘everyday life’: a bundle of regular processes, often associated with characteristic behavioral roles. This includes the experience of having a ‘finite body’; that ‘processes take time in real terms’; that each process is characterized by its own ‘typical resource consumption’; that ‘all resources are finite’ (although there can be different time scales here (see the example with solar energy)).
But also here: the ’embeddedness’ of all resources and their consumption in a comprehensive variability makes ‘snapshots’ out of all data, which have their ‘truth’ not only ‘in the moment’, but in the ‘totality of the sequence’! In itself ‘small changes’ in the everyday life can, if they last, assume sizes and achieve effects which change a ‘known everyday life’ so far that long known ‘views’ and ‘long practiced behaviors’ are ‘no longer correct’ sometime: in that case the format of one’s own thinking and behavior can come into increasing contradiction with the experiential world. Then the point has come where the immanent infinity ‘manifests itself’ in the everyday finiteness and ‘demonstrates’ to us that the ‘imagined cosmos in our head’ is just not the ‘true cosmos’. In the end this immanent infinity is ‘truer’ than the ‘apparent finiteness’.
HOMO SAPIENS (WE)
Beside the life-free material processes in this finite world there are since approx. 3.5 billion years the manifestations, which we call ‘life’, and very late – quasi ‘just now’ – showed up in the billions of life forms one, which we call ‘Homo sapiens’. That is us.
The today’s knowledge of the ‘way’, which life has ‘taken’ in these 3.5 billion years, was and is only possible, because science has learned to understand the ‘seemingly finite’ as ‘snapshot’ of an ongoing process of change, which shows its ‘truth’ only in the ‘totality of the individual moments’. That we as human beings, as the ‘latecomers’ in this life-creation-process’, have the ability to ‘recognize’ successive ‘moments’ ‘individually’ as well as ‘in sequence’, is due to the special nature of the ‘brain’ in the ‘body’ and the way in which our body ‘interacts’ with the surrounding world. So, we don’t know about the ‘existence of an immanent infinity’ ‘directly’, but only ‘indirectly’ through the ‘processes in the brain’ that can identify, store, process and ‘arrange’ moments in possible sequences in a ‘neuronally programmed way’. So: our brain enables us on the basis of a given neuronal and physical structure to ‘construct’ an ‘image/model’ of a possible immanent infinity, which we assume to ‘represent’ the ‘events around us’ reasonably well.
THINKING
One characteristic attributed to Homo Sapiens is called ‘thinking’; a term which until today is described only vaguely and very variously by different sciences. From another Homo Sapiens we learn about his thinking only by his way of ‘behaving’, and a special case of it is ‘linguistic communication’.
Linguistic communication is characterized by the fact that it basically works with ‘abstract concepts’, to which as such no single object in the real world directly corresponds (‘cup’, ‘house’, ‘dog’, ‘tree’, ‘water’ etc.). Instead, the human brain assigns ‘completely automatically’ (‘unconsciously’!) most different concrete perceptions to one or the other abstract concept in such a way that a human A can agree with a human B whether one assigns this concrete phenomenon there in front to the abstract concept ‘cup’, ‘house’, ‘dog’, ‘tree’, or ‘water’. At some point in everyday life, person A knows which concrete phenomena can be meant when person B asks him whether he has a ‘cup of tea’, or whether the ‘tree’ carries apples etc.
This empirically proven ‘automatic formation’ of abstract concepts by our brain is not only based on a single moment, but these automatic construction processes work with the ‘perceptual sequences’ of finite moments ’embedded in changes’, which the brain itself also automatically ‘creates’. ‘Change as such’ is insofar not a ‘typical object’ of perception, but is the ‘result of a process’ taking place in the brain, which constructs ‘sequences of single perceptions’, and these ‘calculated sequences’ enter as ‘elements’ into the formation of ‘abstract concepts’: a ‘house’ is from this point of view not a ‘static concept’, but a concept, which can comprise many single properties, but which is ‘dynamically generated’ as a ‘concept’, so that ‘new elements’ can be added or ‘existing elements’ may be ‘taken away’ again.
MODEL: WORLD AS A PROCESS
(The words are from the German text)
Although there is no universally accepted comprehensive theory of human thought to date, there are many different models (everyday term for the more correct term ‘theories’) that attempt to approximate important aspects of human thought.
The preceding image shows the outlines of a minimally simple model to our thinking.
This model assumes that the surrounding world – with ourselves as components of that world – is to be understood as a ‘process’ in which, at a chosen ‘point in time’, one can describe in an idealized way all the ‘observable phenomena’ that are important to the observer at that point in time. This description of a ‘section of the world’ is here called ‘situation description’ at time t or simply ‘situation’ at t.
Then one needs a ‘knowledge about possible changes’ of elements of the situation description in the way (simplified): ‘If X is element of situation description at t, then for a subsequent situation at t either X is deleted or replaced by a new X*’. There may be several alternatives for deletion or replacement with different probabilities. Such ‘descriptions of changes’ are here simplified called ‘change rules’.
Additionally, as part of the model, there is a ‘game instruction’ (classically: ‘inference term’), which explains when and how to apply a change rule to a given situation Sit at t in such a way that at the subsequent time t+1, there is a situation Sit* in which the changes have been made that the change rule describes.
Normally, there is more than one change rule that can be applied simultaneously with the others. This is also part of the game instructions.
This minimal model can and must be seen against the background of continuous change.
For this structure of knowledge it is assumed that one can describe ‘situations’, possible changes of such a situation, and that one can have a concept how to apply descriptions of recognized possible changes to a given situation.
With the recognition of an immanent infinity manifested in many concrete finite situations, it is immediately clear that the set of assumed descriptions of change should correspond with the observable changes, otherwise the theory has little practical use. Likewise, of course, it is important that the assumed situation descriptions correspond with the observable world. Fulfilling the correspondence requirements or checking that they are true is anything but trivial.
ABSTRACT – REAL – INDETERMINATE
To these ‘correspondence requirements’ here some additional considerations, in which the view of the everyday perspective comes up.
It is to be noted that a ‘model’ is not the environment itself, but only a ‘symbolic description’ of a section of the environment from the point of view and with the understanding of a human ‘author’! To which properties of the environment a description refers, only the author himself knows, who ‘links’ the chosen ‘symbols’ (text or language) ‘in his head’ with certain properties of the environment, whereby these properties of the environment must also be represented ‘in the head’, quasi ‘knowledge images’ of ‘perception events’, which have been triggered by the environmental properties. These ‘knowledge images in the head’ are ‘real’ for the respective head; compared to the environment, however, they are basically only ‘fictitious’; unless there is currently a connection between current fictitious ‘images in the head’ and the ‘current perceptions’ of ‘environmental events’, which makes the ‘concrete elements of perception’ appear as ‘elements of the fictitious images’. Then the ‘fictitious’ pictures would be ‘fictitious and real’.
Due to the ‘memory’, whose ‘contents’ are more or less ‘unconscious’ in the ‘normal state’, we can however ‘remember’ that certain ‘fictitious pictures’ were once ‘fictitious and real’ in the past. This can lead to a tendency in everyday life to ascribe a ‘presumed reality’ to fictional images that were once ‘real’ in the past, even in the current present. This tendency is probably of high practical importance in everyday life. In many cases these ‘assumptions’ also work. However, this ‘spontaneous-for-real-holding’ can often be off the mark; a common source of error.
The ‘spontaneous-for-real-holding’ can be disadvantageous for many reasons. For example, the fictional images (as inescapably abstract images) may in themselves be only ‘partially appropriate’. The context of the application may have changed. In general, the environment is ‘in flux’: facts that were given yesterday may be different today.
The reasons for the persistent changes are different. Besides such changes, which we could recognize by our experience as an ‘identifiable pattern’, there are also changes, which we could not assign to a pattern yet; these can have a ‘random character’ for us. Finally there are also the different ‘forms of life’, which are basically ‘not determined’ by their system structure in spite of all ‘partial determinateness’ (one can also call this ‘immanent freedom’). The behavior of these life forms can be contrary to all other recognized patterns. Furthermore, life forms behave only partially ‘uniformly’, although everyday structures with their ‘rules of behavior’ – and many other factors – can ‘push’ life forms with their behavior into a certain direction.
If one remembers at this point again the preceding thoughts about the ‘immanent infinity’ and the view that the single, finite moments are only understandable as ‘part of a process’, whose ‘logic’ is not decoded to a large extent until today, then it is clear, that any kind of ‘modeling’ within the comprehensive change processes can only have a preliminary approximation character, especially since it is aggravated by the fact that the human actors are not only ‘passively receiving’, but at the same time always also ‘actively acting’, and thereby they influence the change process by their actions! These human influences result from the same immanent infinity as those which cause all other changes. The people (like the whole life) are thus inevitably real ‘co-creative’ …. with all the responsibilities which result from it.
MORALITY ABOVE ALL
What exactly one has to understand by ‘morality’, one has to read out of many hundreds – or even more – different texts. Every time – and even every region in this world – has developed different versions.
In this text it is assumed that with ‘moral’ such ‘views’ are meant, which should contribute to the fact that an individual person (or a group or …) in questions of the ‘decision’ of the kind “Should I rather do A or B?” should get ‘hints’, how this question can be answered ‘best’.
If one remembers at this point what was said before about that form of thinking which allows ‘prognoses’ (thinking in explicit ‘models’ or ‘theories’), then there should be an ‘evaluation’ of the ‘possible continuations’ independent of a current ‘situation description’ and independent of the possible ‘knowledge of change’. So there must be ‘besides’ the description of a situation as it ‘is’ at least a ‘second level’ (a ‘meta-level’), which can ‘talk about’ the elements of the ‘object-level’ in such a way that e.g. it can be said that an ‘element A’ from the object-level is ‘good’ or ‘bad’ or ‘neutral’ or with a certain gradual ‘tuning’ ‘good’ or ‘bad’ or ‘neutral’ at the meta-level. This can also concern several elements or whole subsets of the object level. This can be done. But for it to be ‘rationally acceptable’, these valuations would have to be linked to ‘some form of motivation’ as to ‘why’ this valuation should be accepted. Without such a ‘motivation of evaluations’ such an evaluation would appear as ‘pure arbitrariness’.
At this point the ‘air’ becomes quite ‘thin’: in the history so far no convincing model for a moral justification became known, which is in the end not dependent from the decision of humans to set certain rules as ‘valid for all’ (family, village, tribe, …). Often the justifications can still be located in the concrete ‘circumstances of life’, just as often the concrete circumstances of life ‘recede into the background’ in the course of time and instead abstract concepts are introduced, which one endows with a ‘normative power’, which elude a more concrete analysis. Rational access is then hardly possible, if at all.
In a time like in the year 2023, in which the available knowledge is sufficient to be able to recognize the interdependencies of literally everybody from everybody, in addition the change dynamics, which can threaten with the components ‘global warming’ the ‘sustainable existence of life on earth’ substantially, ‘abstractly set normative terms’ appear not only ‘out of time’, no, they are highly dangerous, since they can substantially hinder the preservation of life in the further future.
META-MORAL (Philosophy)
The question then arises whether this ‘rational black hole’ of ‘justification-free normative concepts’ marks the end of human thinking or whether thinking should instead just begin here?
Traditionally, ‘philosophy’ understands itself as that attitude of thinking, in which every ‘given’ – including any kind of normative concepts – can be made an ‘object of thinking’. And just the philosophical thinking has produced exactly this result in millennia of struggle: there is no point in thinking, from which all ought/all evaluating can be derived ‘just like that’.
In the space of philosophical thinking, on the meta-moral level, it is possible to ‘thematize’ more and more aspects of our situation as ‘mankind’ in a dynamic environment (with man himself as part of this environment), to ‘name’ them, to place them in a ‘potential relations’, to make ‘thinking experiments’ about ‘possible developments’, but this philosophical meta-moral knowledge is completely transparent and always identifiable. The inferences about why something seems ‘better’ than something else are always ’embedded’, ‘related’. The demands for an ‘autonomous morality’, for an ‘absolute morality’ besides philosophical thinking appear ‘groundless’, ‘arbitrary’, ‘alien’ to the ‘matter’ against this background. A rational justification is not possible.
A ‘rationally unknowable’ may exist, exists even inescapably, but this rationally unknowable is our sheer existence, the actual real occurrence, for which so far there is no rational ‘explanation’, more precisely: not yet. But this is not a ‘free pass’ for irrationality. In ‘irrationality’ everything disappears, even the ‘rationally unrecognizable’, and this belongs to the most important ‘facts’ in the world of life.
COMMENTS
[1] The different forms of ‘infinity’, which have been introduced into mathematics with the works of Georg Cantor and have been intensively further investigated, have nothing to do with the experienceable finiteness/ infinity described in the text: https://en.wikipedia.org/wiki/Georg_Cantor . However, if one wants to ‘describe’ the ‘experience’ of real finiteness/ infinity, then one will possibly want to fall back on descriptive means of mathematics. But it is not a foregone conclusion whether the mathematical concepts ‘harmonize’ with the empirical experience standing to the matter.
(First: June 9, 2023 – Last change: June 10, 2023)
Comment: This post is a translation from a German text in my blog ‘cognitiveagent.org’ with the aid of the deepL software
CONTEXT
The current phase of my thinking continues to revolve around the question how the various states of knowledge relate to each other: the many individual scientific disciplines drift side by side; philosophy continues to claim supremacy, but cannot really locate itself convincingly; and everyday thinking continues to run its course unperturbed with the conviction that ‘everything is clear’, that you just have to look at it ‘as it is’. Then the different ‘religious views’ come around the corner with a very high demand and a simultaneous prohibition not to look too closely. … and much more.
INTENTION
In the following text three fundamental ways of looking at our present world are outlined and at the same time they are put in relation to each other. Some hitherto unanswered questions can possibly be answered better, but many new questions arise as well. When ‘old patterns of thinking’ are suspended, many (most? all?) of the hitherto familiar patterns of thinking have to be readjusted. All of a sudden they are simply ‘wrong’ or strongly ‘in need of repair’.
Unfortunately it is only a ‘sketch’.[1]
THOUGHTS IN EVERYDAY
FIG. 1: In everyday thinking, every human being (a ‘homo sapiens’ (HS)) assumes that what he knows of a ‘real world’ is what he ‘perceives’. That there is this real world with its properties, he is – more or less – ‘aware’ of, there is no need to discuss about it specially. That, what ‘is, is’.
… much could be said …
PHILOSOPHICAL THINKING
FIG. 2: Philosophical thinking starts where one notices that the ‘real world’ is not perceived by all people in ‘the same way’ and even less ‘imagined’ in the same way. Some people have ‘their ideas’ about the real world that are strikingly ‘different’ from other people’s ideas, and yet they insist that the world is exactly as they imagine it. From this observation in everyday life, many new questions can arise. The answers to these questions are as manifold as there were and are people who gave or still give themselves to these philosophical questions.
… famous examples: Plato’s allegory of the cave suggests that the contents of our consciousness are perhaps not ‘the things themselves’ but only the ‘shadows’ of what is ultimately ‘true’ … Descartes‘ famous ‘cogito ergo sum’ brings into play the aspect that the contents of consciousness also say something about himself who ‘consciously perceives’ such contents …. the ‘existence of the contents’ presupposes his ‘existence as thinker’, without which the existence of the contents would not be possible at all …what does this tell us? … Kant’s famous ‘thing in itself’ (‘Ding an sich’) can be referred to the insight that the concrete, fleeting perceptions can never directly show the ‘world as such’ in its ‘generality’. This lies ‘somewhere behind’, hard to grasp, actually not graspable at all? ….
… many things could be said …
EMPIRICAL-THEORETICAL THINKING
FIG. 3: The concept of an ’empirical theory’ developed very late in the documented history of man on this planet. On the one hand philosophically inspired, on the other hand independent of the widespread forms of philosophy, but very strongly influenced by logical and mathematical thinking, the new ’empirical theoretical’ thinking settled exactly at this breaking point between ‘everyday thinking’ and ‘theological’ as well as ‘strongly metaphysical philosophical thinking’. The fact that people could make statements about the world ‘with the chest tone of conviction’, although it was not possible to show ‘common experiences of the real world’, which ‘corresponded’ with the expressed statements, inspired individual people to investigate the ‘experiential (empirical) world’ in such a way that everyone else could have the ‘same experiences’ with ‘the same procedure’. These ‘transparent procedures’ were ‘repeatable’ and such procedures became what was later called ’empirical experiment’ or then, one step further, ‘measurement’. In ‘measuring’ one compares the ‘result’ of a certain experimental procedure with a ‘previously defined standard object’ (‘kilogram’, ‘meter’, …).
This procedure led to the fact that – at least the experimenters – ‘learned’ that our knowledge about the ‘real world’ breaks down into two components: there is the ‘general knowledge’ what our language can articulate, with terms that do not automatically have to have something to do with the ‘experiential world’, and such terms that can be associated with experimental experiences, and in such a way that other people, if they engage in the experimental procedure, can also repeat and thereby confirm these experiences. A rough distinction between these two kinds of linguistic expressions might be ‘fictive’ expressions with unexplained claims to experience, and ’empirical’ expressions with confirmed claims to experience.
Since the beginning of the new empirical-theoretical way of thinking in the 17th century, it took at least 300 years until the concept of an ’empirical theory’ was consolidated to such an extent that it became a defining paradigm in many areas of science. However, many methodological questions remained controversial or even ‘unsolved’.
DATA and THEORY
For many centuries, the ‘misuse of everyday language’ for enabling ’empirically unverifiable statements’ was directly chalked up to this everyday language and the whole everyday language was discredited as ‘source of untruths’. A liberation from this ‘ monster of everyday language’ was increasingly sought in formal artificial languages or then in modern axiomatized mathematics, which had entered into a close alliance with modern formal logic (from the end of the 19th century). The expression systems of modern formal logic or then of modern formal mathematics had as such (almost) no ‘intrinsic meaning’. They had to be introduced explicitly on a case-by-case basis. A ‘formal mathematical theory’ could be formulated in such a way that it allowed ‘logical inferences’ even without ‘explicit assignment’ of an ‘external meaning’, which allowed certain formal expressions to be called ‘formally true’ or ‘formally false’.
This seemed very ‘reassuring’ at first sight: mathematics as such is not a place of ‘false’ or ‘foisted’ truths.
The intensive use of formal theories in connection with experience-based experiments, however, then gradually made clear that a single measured value as such does not actually have any ‘meaning’ either: what is it supposed to ‘mean’ that at a certain ‘time’ at a certain ‘place’ one establishes an ‘experienceable state’ with certain ‘properties’, ideally comparable to a previously agreed ‘standard object’? ‘Expansions’ of bodies can change, ‘weight’ and ‘temperature’ as well. Everything can change in the world of experience, fast, slow, … so what can a single isolated measured value say?
It dawned to some – not only to the experience-based researchers, but also to some philosophers – that single measured values only get a ‘meaning’, a possible ‘sense’, if one can at least establish ‘relations’ between single measured values: Relations ‘in time’ (before – after), relations at/in place (higher – lower, next to each other, …), ‘interrelated quantities’ (objects – areas, …), and that furthermore the different ‘relations’ themselves again need a ‘conceptual context’ (single – quantity, interactions, causal – non-causal, …).
Finally, it became clear that single measured values needed ‘class terms’, so that they could be classified somehow: abstract terms like ‘tree’, ‘plant’, ‘cloud’, ‘river’, ‘fish’ etc. became ‘collection points’, where one could deliver ‘single observations’. With this, hundreds and hundreds of single values could then be used, for example, to characterize the abstract term ‘tree’ or ‘plant’ etc.
This distinction into ‘single, concrete’ and ‘abstract, general’ turns out to be fundamental. It also made clear that the classification of the world by means of such abstract terms is ultimately ‘arbitrary’: both ‘which terms’ one chooses is arbitrary, and the assignment of individual experiential data to abstract terms is not unambiguously settled in advance. The process of assigning individual experiential data to particular terms within a ‘process in time’ is itself strongly ‘hypothetical’ and itself in turn part of other ‘relations’ which can provide additional ‘criteria’ as to whether date X is more likely to belong to term A or more likely to belong to term B (biology is full of such classification problems).
Furthermore, it became apparent that mathematics, which comes across as so ‘innocent’, can by no means be regarded as ‘innocent’ on closer examination. The broad discussion of philosophy of science in the 20th century brought up many ‘artifacts’ which can at least easily ‘corrupt’ the description of a dynamic world of experience.
Thus it belongs to formal mathematical theories that they can operate with so-called ‘all- or particular statements’. Mathematically it is important that I can talk about ‘all’ elements of a domain/set. Otherwise talking becomes meaningless. If I now choose a formal mathematical system as conceptual framework for a theory which describes ’empirical facts’ in such a way that inferences become possible which are ‘true’ in the sense of the theory and thus become ‘predictions’ which assert that a certain fact will occur either ‘absolutely’ or with a certain probability X greater than 50%, then two different worlds unite: the fragmentary individual statements about the world of experience become embedded in ‘all-statements’ which in principle say more than empirical data can provide.
At this point it becomes visible that mathematics, which appears to be so ‘neutral’, does exactly the same job as ‘everyday language’ with its ‘abstract concepts’: the abstract concepts of everyday language always go beyond the individual case (otherwise we could not say anything at all in the end), but just by this they allow considerations and planning, as we appreciate them so much in mathematical theories.
Empirical theories in the format of formal mathematical theories have the further problem that they as such have (almost) no meanings of their own. If one wants to relate the formal expressions to the world of experience, then one has to explicitly ‘construct a meaning’ (with the help of everyday language!) for each abstract concept of the formal theory (or also for each formal relation or also for each formal operator) by establishing a ‘mapping’/an ‘assignment’ between the abstract constructs and certain provable facts of experience. What may sound so simple here at first sight has turned out to be an almost unsolvable problem in the course of the last 100 years. Now it does not follow that one should not do it at all; but it does draw attention to the fact that the choice of a formal mathematical theory need not automatically be a good solution.
… many things could still be said …
INFERENCE and TRUTH
A formal mathematical theory can derive certain statements as formally ‘true’ or ‘false’ from certain ‘assumptions’. This is possible because there are two basic assumptions: (i) All formal expressions have an ‘abstract truth value’ as ‘abstractly true’ or just as ‘abstractly not true’. Furthermore, there is a so-called ‘formal notion of inference’ which determines whether and how one can ‘infer’ other formal expressions from a given ‘set of formal expressions’ with agreed abstract truth values and a well-defined ‘form’. This ‘derivation’ consists of ‘operations over the signs of the formal expressions’. The formal expressions are here ‘objects’ of the notion of inference, which is located on a ‘level higher’, on a ‘meta-level 1’. The inference term is insofar a ‘formal theory’ of its own, which speaks about certain ‘objects of a deeper level’ in the same way as the abstract terms of a theory (or of everyday language) speak about concrete facts of experience. The interaction of the notion of inference (at meta-level 1) and the formal expressions as objects presupposes its own ‘interpretive relation’ (ultimately a kind of ‘mapping’), which in turn is located at yet another level – meta-level 2. This interpretive relation uses both the formal expressions (with their truth values!) and the inference term as ‘objects’ to install an interpretive relation between them. Normally, this meta-level 2 is handled by the everyday language, and the implicit interpretive relation is located ‘in the minds of mathematicians (actually, in the minds of logicians)’, who assume that their ‘practice of inference’ provides enough experiential data to ‘understand’ the ‘content of the meaning relation’.
It had been Kurt Gödel [2], who in 1930/31 tried to formalize the ‘intuitive procedure’ of meta-proofs itself (by means of the famous Gödelization) and thus made the meta-level 3 again a new ‘object’, which can be discussed explicitly. Following Gödel’s proof, there were further attempts to formulate this meta-level 3 again in a different ways or even to formalize a meta-level 4. But these approaches remained so far without clear philosophical result.
It seems to be clear only that the ability of the human brain to open again and again new meta-levels, in order to analyze and discuss with it previously formulated facts, is in principle unlimited (only limited by the finiteness of the brain, its energy supply, the time, and similar material factors).
An interesting special question is whether the formal inference concept of formal mathematics applied to experience facts of a dynamic empirical world is appropriate to the specific ‘world dynamics’ at all? For the area of the ‘apparently material structures’ of the universe, modern physics has located multiple phenomena which simply elude classical concepts. A ‘matter’, which is at the same time ‘energy’, tends to be no longer classically describable, and quantum physics is – despite all ‘modernity’ – in the end still a ‘classical thinking’ within the framework of a formal mathematics, which does not possess many properties from the approach, which, however, belong to the experienceable world.
This limitation of a formal-mathematical physical thinking shows up especially blatantly at the example of those phenomena which we call ‘life’. The experience-based phenomena that we associate with ‘living (= biological) systems’ are, at first sight, completely material structures, however, they have dynamic properties that say more about the ‘energy’ that gives rise to them than about the materiality by means of which they are realized. In this respect, implicit energy is the real ‘information content’ of living systems, which are ‘radically free’ systems in their basic structure, since energy appears as ‘unbounded’. The unmistakable tendency of living systems ‘out of themselves’ to always ‘enable more complexity’ and to integrate contradicts all known physical principles. ‘Entropy’ is often used as an argument to relativize this form of ‘biological self-dynamics’ with reference to a simple ‘upper bound’ as ‘limitation’, but this reference does not completely nullify the original phenomenon of the ‘living’.
It becomes especially exciting if one dares to ask the question of ‘truth’ at this point. If one locates the meaning of the term ‘truth’ first of all in the situation in which a biological system (here the human being) can establish a certain ‘correspondence’ between its abstract concepts and such concrete knowledge structures within its thinking, which can be related to properties of an experiential world through a process of interaction, not only as a single individual but together with other individuals, then any abstract system of expression (called ‘language’) has a ‘true relation to reality’ only to the extent that there are biological systems that can establish such relations. And these references further depend on the structure of perception and the structure of thought of these systems; these in turn depend on the nature of bodies as the context of brains, and bodies in turn depend on both the material structure and dynamics of the environment and the everyday social processes that largely determine what a member of a society can experience, learn, work, plan, and do. Whatever an individual can or could do, society either amplifies or ‘freezes’ the individual’s potential. ‘Truth’ exists under these conditions as a ‘free-moving parameter’ that is significantly affected by the particular process environment. Talk of ‘cultural diversity’ can be a dangerous ‘trivialization’ of massive suppression of ‘alternative processes of learning and action’ that are ‘withdrawn’ from a society because it ‘locks itself in’. Ignorance tends not to be a good advisor. However, knowledge as such does not guarantee ‘right’ action either. The ‘process of freedom’ on planet Earth is a ‘galactic experiment’, the seriousness and extent of which is hardly seen so far.
COMMENTS
[1] References are omitted here. Many hundreds of texts would have to be mentioned. No sketch can do that.
[2] See for the ‘incompleteness theorems’ of Kurt Gödel (1930, published 1931): https://en.wikipedia.org/wiki/Kurt_G%C3%B6del#Incompleteness_theorems
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
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:
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.
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]
PREFACE DEWEY 1938/9
If one looks to the time span between Dewey’s first published book from 1887 (Psychology) until 1938 (Logic) we have 51 years of experience. Thus this book about logic can be seen as a book digesting a manifold knowledge from a very special point of view: from Logic as a theory of inquiry.
And because Dewey is qualified as one of the “primary figures associated with the philosophy of pragmatism” [3] it is of no surprise that he in his preface to the book ‘Logic …’ [1] mentions not only as one interest the ” … interpretation of the forms and formal relations that constitute the standard material of logical tradition”(cf. p.1), but within this perspective he underlines the attention particularly to “… the principle of the continuum of inquiry”(cf. p.1).
If one sees like Dewey the “basic conception of inquiry” as the “determination of an indeterminate situation” (cf. p.1) then the implicit relations can enable “a coherent account of the different propositional forms to be given”. This provides a theoretical interface to logical thinking as thinking in inferences as well as an philosophical interface to pragmatism as a kind of inquiry which sees strong relations between the triggering assumptions and the possible consequences created by agreed procedures leading from the given and expected to the final consequences.
Dewey himself is very skeptical about the term ‘Pragmatism’, because
“… the word lends itself [perhaps] to misconception”, thus “that it seemed advisable to avoid its use.” (cf. p.2) But Dewey does not stay with a simple avoidance; he gives a “proper interpretation” of the term ‘pragmatic’ in the way that “the function of consequences” can be interpreted as “necessary tests of the validity of propositions, provided these consequences are operationally instituted and are such as to resolve the specific problem evoking the operations.”(cf. p.2)
Thus Dewey assumes the following elements of a pragmatic minded process of inquiry:
A pragmatic inquiry is a process leading to some consequences.
These consequences can be seen as tests of the validity of propositions.
As a necessary condition that a consequence can be qualified as a test of assumed propositions one has to assume that “these consequences are operationally instituted and are such as to resolve the specific problem”.
That consequences, which are different to the assumed propositions [represented by some expressions] can be qualified as confirming an assumed validity of the assumed propositions, requires that the assumed validity can be represented as an expectation of possible outcomes which are observably decidable.
In other words: some researchers are assuming that some propositions represented by some expressions are valid, because they are convinced about this by their commonly shared observations of the propositions. They associate these assumed propositions with an expected outcome represented by some expressions which can be interpreted by the researchers in a way, that they are able to decide whether an upcoming situation can be judged as that situation which is expected as a valid outcome (= consequence). Then there must exist some agreed procedures (operationally instituted) whose application to the given starting situation produces the expected outcome (=consequences). Then the whole process of a start situation with an given expectation as well as given procedures can generate a sequence of situations following one another with an expected outcome after finitely many situation.
If one interprets these agreed procedures as inference rules and the assumed expressions as assumptions and expectations then the whole figure can be embedded in the usual pattern of inferential logic, but with some strong extensions.
Dewey is quite optimistic about the conformity of this pragmatic view of an inquiry and a view of logic: “I am convinced that acceptance of the general principles set forth will enable a more complete and consistent set of symbolizations than now exists to be made.”(cf. p.2) But he points to one aspect, which would be necessary for a pragmatically inspired view of logic which is in ‘normal logic’ not yet realized: “the need for development of a general theory of language in which form and matter are not separated.” This is a very strong point because the architecture of modern logic is fundamentally depending on the complete abandonment of meaning of language; the only remaining shadow of meaning resides in the assumptions of the property of being ‘true’ or ‘false’ related to expressions (not propositions!). To re-introduce ‘meaning’ into logic by the usage of ‘normal language’ would be a complete rewriting of the whole of modern logic.
At the time of writing these lines by Dewey 1938 there was not the faintest idea in logic how such a rewriting of the whole logic could look like.
With the new oksimo paradigm there could perhaps exist a slight chance to do it. Why? Here are the main arguments:
The oksimo paradigm assumes an inference process leading from some assumed starting situation to some consequences generated by the application of some agreed change-rules.
All situations are assumed to have a twofold nature: (i) primarily they are given as expressions of some language (it can be a normal language!); (ii) secondarily these expressions are part of the used normal language, where every researches is assumed to have a ‘built-in’ meaning function which has during his/her individual learning collected enough ‘meaning’, which allows a symbolically enabled cooperation with other researchers.
Every researcher can judge every time whether a given or inferred situation is in agreement with his interpretation of the expressions and their relation to the given or considered possible situation.
If the researchers assume in the beginning additionally an expectation (goal/ vision) of a possible outcome (possible consequence), then it is possible at every point of the sequence to judge to which degree the actual situation corresponds to the expected situation.
The second requirement of Dewey for the usage of logic for a pragmatic inquiry was given in the statement “that an adequate set of symbols depends upon prior institution of valid ideas of the conceptions and relations that are symbolized.”(cf. p.2)
Thus not only the usage of normal language is required but also some presupposed knowledge. Within the oksimo paradigm it is possible to assume as much presupposed knowledge as needed.
RESULTS SO FAR
After reading the preface to the book it seems that the pragmatic view of inquiry combined with some idea of modern logic can directly be realized within the oksimo paradigm.
The following posts will show whether this is a good working hypothesis or not.
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/). 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
[3] John Dewey, Wikipedia [EN]: https://en.wikipedia.org/wiki/John_Dewey
Here some spontaneous recording of the author, talking ‘unplugged’ into a microphone how he would describe the content of the text above in a few words. It’s not perfect, but it’s ‘real’: we all are real persons not being perfect, but we have to fight for ‘truth’ and a better life while being ‘imperfect’ …. take it as ‘fun’ 🙂
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 THORY PARADIGM
The following text is a short illustration how the general theory concept as extracted from the text of Popper can be applied to the oksimo simulation software concept.
The starting point is the meta-theoetical schema as follows:
MT=<S, A[μ], E, L, AX, ⊢, ET, E+, E-, true, false, contradiction, inconsistent>
In the oksimo case we have also a given empirical context S, a non-epty set of human actors A[μ] whith a built-in meaning function for the expressions E of some language L, some axioms AX as a subset of the expressions E, an inference concept ⊢, and all the other concepts.
The human actors A[μ] can write some documents with the expressions E of language L. In one document S_U they can write down some universal facts they belief that these are true (e.g. ‘Birds can fly’). In another document S_E they can write down some empirical facts from the given situation S like ‘There is something named James. James is a bird’. And somehow they wish that James should be able to fly, thus they write down a vision text S_V with ‘James can fly’.
The interesting question is whether it is possible to generate a situation S_E.i in the future, which includes the fact ‘James can fly’.
With the knowledge already given they can built the change rule: IF it is valid, that {Birds can fly. James is a bird} THEN with probability π = 1 add the expression Eplus = {‘James can fly’} to the actual situation S_E.i. EMinus = {}. This rule is then an element of the set of change rules X.
The simulator ⊢X works according to the schema S’ = S – Eminus + Eplus.
Because we have S=S_U + S_E we are getting
S’ = {Birds can fly. Something is named James. James is a bird.} – Eminus + Eplus
S’ = {Birds can fly. Something is named James. James is a bird.} – {}+ {James can fly}
S’ = {Birds can fly. Something is named James. James is a bird. James can fly}
With regard to the vision which is used for evaluation one can state additionally:
|{James can fly} ⊆ {Birds can fly. Something is named James. James is a bird. James can fly}|= 1 ≥ 1
Thus the goal has been reached with 1 meaning with 100%.
THE ROLE OF MEANING
What makes a certain difference between classical concepts of an empirical theory and the oksimo paradigm is the role of meaning in the oksimo paradigm. While the classical empirical theory concept is using formal (mathematical) languages for their descriptions with the associated — nearly unsolvable — problem how to relate these concepts to the intended empirical world, does the oksimo paradigm assume the opposite: the starting point is always the ordinary language as basic language which on demand can be extended by special expressions (like e.g. set theoretical expressions, numbers etc.).
Furthermore it is in the oksimo paradigm assumed that the human actors with their built-in meaning function nearly always are able to decided whether an expression e of the used expressions E of the ordinary language L is matching certain properties of the given situation S. Thus the human actors are those who have the authority to decided by their meaning whether some expression is actually true or not.
The same holds with possible goals (visions) and possible inference rules (= change rules). Whether some consequence Y shall happen if some condition X is satisfied by a given actual situation S can only be decided by the human actors. There is no other knowledge available then that what is in the head of the human actors. [1] This knowledge can be narrow, it can even be wrong, but human actors can only decide with that knowledge what is available to them.
If they are using change rules (= inference rules) based on their knowledge and they derive some follow up situation as a theorem, then it can happen, that there exists no empiricial situation S which is matching the theorem. This would be an undefined truth case. If the theorem t would be a contradiction to the given situation S then it would be clear that the theory is inconsistent and therefore something seems to be wrong. Another case cpuld be that the theorem t is matching a situation. This would confirm the belief on the theory.
COMMENTS
[1] Well known knowledge tools are since long libraries and since not so long data-bases. The expressions stored there can only be of use (i) if a human actor knows about these and (ii) knows how to use them. As the amount of stored expressions is increasing the portion of expressions to be cognitively processed by human actors is decreasing. This decrease in the usable portion can be used for a measure of negative complexity which indicates a growng deterioration of the human knowledge space. The idea that certain kinds of algorithms can analyze these growing amounts of expressions instead of the human actor themself is only constructive if the human actor can use the results of these computations within his knowledge space. By general reasons this possibility is very small and with increasing negativ complexity it is declining.
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.