In the last months I was engaged with the topic of text-generating algorithms and the possible impact for a scientific discourse (some first notices to this discussion you can find here (https://www.uffmm.org/2023/08/24/homo-sapiens-empirical-and-sustained-empirical-theories-emotions-and-machines-a-sketch/)). In this context it is important to clarify the role and structure of human actors as well as the concept of Intelligence. Meanwhile I have abandoned the word Intelligence completely because the inflationary use in today mainstream pulverises any meaning. Even in one discipline — like psychology — you can find many different concepts. In this context I have read the book of Stanovich et.al to have a prominent example of using the concept of intelligence, there combined with the concept of rationality, which is no less vague.
Introduction
The book “The Rationality Quotient” from 2016 represents not the beginning of a discourse but is a kind of summary of a long lasting discourse with many publications before. This makes this book interesting, but also difficult to read in the beginning, because the book is using nearly on every page theoretical terms, which are assumed to be known to the reader and cites other publications without giving sufficient explanations why exactly these cited publications are important. This is no argument against this book but sheds some light on the reader, who has to learn a lot to understand the text.
A text with the character of summing up its subject is good, because it has a confirmed meaning about the subject which enables a kind of clarity which is typical for that state of elaborated point of view.
In the following review it is not the goal to give a complete account of every detail of this book but only to present the main thesis and then to analyze the used methods and the applied epistemological framework.
Main Thesis of the Book
The reviewing starts with the basic assumptions and the main thesis.
FIGURE 1 : The beginning. Note: the number ‘2015’ has to be corrected to ‘2016’.
FIGURE 2 : First outline of cognition. Note: the number ‘2015’ has to be corrected to ‘2016’.
As mentioned in the introduction you will in the book not find a real overview about the history of psychological research dealing with the concept of Intelligence and also no overview about the historical discourse to the concept of Rationality, whereby the last concept has also a rich tradition in Philosophy. Thus, somehow you have to know it.
There are some clear warnings with regard to the fuzziness of the concept rationality (p.3) as well as to the concept of intelligence (p.15). From a point of view of Philosophy of Science it could be interesting to know what the circumstances are which are causing such a fuzziness, but this is not a topic of the book. The book talks within its own selected conceptual paradigm. Being in the dilemma, of what kind of intelligence paradigm one wants to use, the book has decided to work with the Cattell-Horn-Carroll (CTC) paradigm, which some call a theory. [1]
Directly from the beginning it is explained that the discussion of Intelligence is missing a clear explanation of the full human model of cognition (p.15) and that intelligence tests therefore are mostly measuring only parts of human cognitive functions. (p.21)
Thus let us have a more detailed look to the scenario.
[1] For a first look to the Cattell–Horn–Carroll theory see: https://en.wikipedia.org/wiki/Cattell%E2%80%93Horn%E2%80%93Carroll_theory, a first overview.
Which point of View?
The book starts with a first characterization of the concept of Rationality within a point of view which is not really clear. From different remarks one gets some hints to modern Cognitive Science (4,6), to Decision Theory (4) and Probability Calculus (9), but a clear description is missing.
And it is declared right from the beginning, that the main aim of the book is the Construction of a rational Thinking Test (4), because for the authors the used Intelligence Tests — later reduced to the Carroll-Horn-Carroll (CHC) type of intelligence test (16) — are too narrow in what they are measuring (15, 16, 21).
Related to the term Rationality the book characterizes some requirements which the term rationality should fulfill (e.g. ‘Rationality as a continuum’ (4), ’empirically based’ (4), ‘operationally grounded’ (4), a ‘strong definition’ (5), a ‘normative one’ (5), ‘normative model of optimum judgment’ (5)), but it is more or less open, what these requirements imply and what tacit assumptions have to be fulfilled, that this will work.
The two requirements ’empirically based’ as well as ‘operationally grounded’ point in the direction of an tacitly assumed concept of an empirical theory, but exactly this concept — and especially in association with the term cognitive science — isn’t really clear today.
Because the authors make in the next pages a lot of statements which claim to be serious, it seems to be important for the discussion in this review text to clarify the conditions of the ‘meaning of language expressions’ and of being classified as ‘being true’.
If we assume — tentatively — that the authors assume a scientific theory to be primarily a text whose expressions have a meaning which can transparently be associated with an empirical fact and if this is the case, then the expression will be understood as being grounded and classified as true, then we have characterized a normal text which can be used in everyday live for the communication of meanings which can become demonstrated as being true.
Is there a difference between such a ‘normal text’ and a ‘scientific theory’? And, especially here, where the context should be a scientific theory within the discipline of cognitive science: what distinguishes a normal text from a ‘scientific theory within cognitive science’?
Because the authors do not explain their conceptual framework called cognitive science we recur here to a most general characterization [2,3] which tells us, that cognitive science is not a single discipline but an interdisciplinary study which is taking from many different disciplines. It has not yet reached a state where all used methods and terms are embedded in one general coherent framework. Thus the relationship of the used conceptual frameworks is mostly fuzzy, unclear. From this follows directly, that the relationship of the different terms to each other — e.g. like ‘underlying preferences’ and ‘well ordered’ — is within such a blurred context rather unclear.
Even the simple characterization of an expression as ‘having an empirical meaning’ is unclear: what are the kinds of empirical subjects and the used terms? According to the list of involved disciplines the disciplines linguistics [4], psychology [5] or neuroscience [6] — besides others — are mentioned. But every of these disciplines is itself today a broad field of methods, not integrated, dealing with a multifaceted subject.
Using an Auxiliary Construction as a Minimal Point of Reference
Instead of becoming somehow paralyzed from these one-and-all characterizations of the individual disciplines one can try to step back and taking a look to basic assumptions about empirical perspectives.
If we take a group of HumanObservers which shall investigate these subjects we could make the following assumptions:
Empirical Linguistics is dealing with languages, spoken as well as written by human persons, within certain environments, and these can be observed as empirical entities.
Empirical Psychology is dealing with the behavior of human persons (a kind of biological systems) within certain environments, and these can be observed.
Empirical Neuroscience is dealing with the brain as part of a body which is located in some environment, and this all can be observed.
The empirical observations of certain kinds of empirical phenomena can be used to define more abstract concepts, relations, and processes. These more abstract concepts, relations, and processes have ‘as such’ no empirical meaning! They constitute a formal framework which has to become correlated with empirical facts to get some empirical meaning. As it is known from philosophy of science [7] the combination of empirical concepts within a formal framework of abstracts terms can enable ‘abstract meanings’ which by logical conclusions can produce statements which are — in the moment of stating them — not empirically true, because ‘real future’ has not yet happened. And on account of the ‘generality’ of abstract terms compared to the finiteness and concreteness of empirical facts it can happen, that the inferred statements never will become true. Therefore the mere usage of abstract terms within a text called scientific theory does not guarantee valid empirical statements.
And in general one has to state, that a coherent scientific theory including e.g. linguistics, psychology and neuroscience, is not yet in existence.
To speak of cognitive science as if this represents a clearly defined coherent discipline seems therefore to be misleading.
This raises questions about the project of a constructing a coherent rational thinking test (CART).
[2] See ‘cognitive science’ in wikipedia: https://en.wikipedia.org/wiki/Cognitive_science
[3] See too ‘cognitive science’ in the Stanford Encyclopedia of Philosophy: https://plato.stanford.edu/entries/cognitive-science/
[4] See ‘linguistics’ in wikipedia: https://en.wikipedia.org/wiki/Linguistics
[5] See ‘psychology’ in wikipedia: https://en.wikipedia.org/wiki/Psychology
[6] See ‘neuroscience’ in wikipedia: https://en.wikipedia.org/wiki/Neuroscience
[7] See ‘philosophy of science’ in wikipedia: https://en.wikipedia.org/wiki/Philosophy_of_science
‘CART’ TEST FRAMEWORK – A Reconstruction from the point of View of Philosophy of Science
Before I will dig deeper into the theory I try to understand the intended outcome of this theory as some point of reference. The following figure 3 gives some hints.
FIGURE 3 : Outline of the Test Framework based on the Appendix in Stanovich et.al 2016. This Outline is a Reconstruction by the author of this review.
It seems to be important to distinguish at least three main parts of the whole scientific endeavor:
The group of scientists which has decided to process a certain problem.
The generated scientific theory as a text.
The description of a CART Test, which describes a procedure, how the abstract terms of the theory can be associated with real facts.
From the group of scientists (Stanovich et al.) we know that they understand themselves as cognitive scientists (without having a clear characterization, what this means concretely).
The intended scientific theory as a text is here assumed to be realized in the book, which is here the subject of a review.
The description of a CART Test is here taken from the appendix of the book.
To understand the theory it is interesting to see, that in the real test the test system (assumed here as a human person) has to read (and hear?) a instruction, how to proceed with a task form, and then the test system (a human person) has to process the test form in the way it has understood the instructions and the test form as it is.
The result is a completed test form.
And it is then this completed test form which will be rated according to the assumed CART theory.
This complete paradigm raises a whole bunch of questions which to answer here in full is somehow out of range.
Mix-Up of Abstract Terms
Because the Test Scenario presupposes a CART theory and within this theory some kind of a model of intended test users it can be helpful to have a more closer look to this assumed CART model, which is located in a person.
FIGURE 4 : General outline of the logic behind CART according to Stanovich et al. (2016).
The presented cognitive architecture shall present a framework for the CART (Comprehensive Assessment of Rational Thinking), whereby this framework is including a model. The model is not only assumed to contextualize and classify heuristics and tasks, but it also presents Rationality in a way that one can deduce mental characteristics included in rationality.(cf. 37)
Because the term Rationality is not an individual empirical fact but an abstract term of a conceptual framework, this term has as such no meaning. The meaning of this abstract term has to be arranged by relations to other abstract terms which themselves are sufficiently related to concrete empirical statements. And these relations between abstract terms and empirical facts (represented as language expressions) have to be represented in a manner, that it is transparent how the the measured facts are related to the abstract terms.
Here Stanovich et al. is using another abstract term Mind, which is associated with characteristics called mental characteristics: Reflective mind, Algorithmic Level, and Mindware.
And then the text tells that Rationality is presenting mental characteristics. What does this mean? Is rationality different from the mind, who has some characteristics, which can be presented from rationality using somehow the mind, or is rationality nevertheless part of the mind and manifests themself in these mental characteristics? But what kind of the meaning could this be for an abstract term like rationality to be part of the mind? Without an explicit model associated with the term Mind which arranges the other abstract term Rationality within this model there exists no meaning which can be used here.
These considerations are the effect of a text, which uses different abstract terms in a way, which is rather unclear. In a scientific theory this should not be the case.
Measuring Degrees of Rationality
In the beginning of chapter 4 Stanovich et al. are looking back to chapter 1. Here they built up a chain of arguments which illustrate some general perspective (cf. 63):
Rationality has degrees.
These degrees of rationality can be measured.
Measurement is realized by experimental methods of cognitive science.
The measuring is based on the observable behavior of people.
The observable behavior can manifest whether the individual actor (a human person) follows assumed preferences related to an assumed axiom of choice.
Observable behavior which is classified as manifesting assumed internal preferences according to an assumed internal axiom of choice can show descriptive and procedural invariance.
Based on these deduced descriptive and procedural invariance, it can be inferred further, that these actors are behaving as if they are maximizing utility.
It is difficult to assess utility maximization directly.
It is much easier to assess whether one of the axioms of rational choice is being violated.
These statements characterize the Logic of the CART according to Stanovich et al. (cf.64)
A major point in this argumentation is the assumption, that observable behavior is such, that one can deduce from the properties of this behavior those attributes/ properties, which point (i) to an internal model of an axiom of choice, (ii) to internal processes, which manifest the effects of this internal model, (iii) to certain characteristics of these internal processes which allow the deduction of the property of maximizing utility or not.
These are very strong assumptions.
If one takes further into account the explanations from the pages 7f about the required properties for an abstract term axiom of choice (cf. figure 1) then these assumptions appear to be very demanding.
Can it be possible to extract the necessary meaning out of observable behavior in a way, which is clear enough by empirical standards, that this behavior shows property A and not property B ?
As we know from the description of the CART in the appendix of the book (cf. figure 3) the real behavior assumed for an CART is the (i) reading (or hearing?) of an instruction communicated by ordinary English, and then (ii) a behavior deduced from the understanding of the instruction, which (iii) manifests themself in the reading of a form with a text and filling out this form in predefined positions in a required language.
This described procedure is quite common throughout psychology and similar disciplines. But it is well known, that the understanding of language instructions is very error-prone. Furthermore, the presentation of a task as a text is inevitably highly biased and additionally too very error-prone with regard to the understanding (this is a reason why in usability testing purely text-based tests are rather useless).
The point is, that the empirical basis is not given as a protocol of observations of language free behavior but of a behavior which is nearly completely embedded in the understanding and handling of texts. This points to the underlying processes of text understanding which are completelyinternal to the actor. There exists no prewired connection between the observable strings of signs constituting a text and the possible meaning which can be organized by the individual processes of text understanding.
Stopping Here
Having reached this point of reading and trying to understand I decided to stop here: to many questions on all levels of a scientific discourse and the relationships between main concepts and terms appear in the book of Stanovich et al. to be not clear enough. I feel therefore confirmed in my working hypothesis from the beginning, that the concept of intelligence today is far too vague, too ambiguous to contain any useful kernel of meaning any more. And concepts like Rationality, Mind (and many others) seem to do not better.
Chatting with chatGPT4
Since April 2023 I have started to check the ability of chatGPT4 to contribute to a philosophical and scientific discourse. The working hypothesis is, that chatGPT4 is good in summarizing the common concepts, which are used in public texts, but chatGPT is not able for critical evaluations, not for really new creative ideas and in no case for systematic analysis of used methods, used frameworks, their interrelations, their truth-conditons and much more, what it cannot. Nevertheless, it is a good ‘common sense check’. Until now I couldn’t learn anything new from these chats.
If you have read this review with all the details and open questions you will be perhaps a little bit disappointed about the answers from chatGPT4. But keep calm: it is a bit helpful.
Translation: This text is a translation from the German Version into English with the aid of the software deepL.com as well as with chatGPT4, moderated by the author. The style of the two translators is different. The author is not good enough to classify which translator is ‘better’.
CONTEXT
This text is the outcome of a conference held at the Technical University of Darmstadt (Germany) with the title: Discourses of disruptive digital technologies using the example of AI text generators ( https://zevedi.de/en/topics/ki-text-2/ ). A German version of this article will appear in a book from de Gruyter as open access in the beginning of 2024.
Collective human-machine intelligence and text generation. A transdisciplinary analysis.
Abstract
Based on the conference theme “AI – Text and Validity. How do AI text generators change scientific discourse?” as well as the special topic “Collective human-machine intelligence using the example of text generation”, the possible interaction relationship between text generators and a scientific discourse will be played out in a transdisciplinary analysis. For this purpose, the concept of scientific discourse will be specified on a case-by-case basis using the text types empirical theory as well as sustained empirical theory in such a way that the role of human and machine actors in these discourses can be sufficiently specified. The result shows a very clear limitation of current text generators compared to the requirements of scientific discourse. This leads to further fundamental analyses on the example of the dimension of time with the phenomenon of the qualitatively new as well as on the example of the foundations of decision-making to the problem of the inherent bias of the modern scientific disciplines. A solution to the inherent bias as well as the factual disconnectedness of the many individual disciplines is located in the form of a new service of transdisciplinary integration by re-activating the philosophy of science as a genuine part of philosophy. This leaves the question open whether a supervision of the individual sciences by philosophy could be a viable path? Finally, the borderline case of a world in which humans no longer have a human counterpart is pointed out.
AUDIO: Keyword Sound
STARTING POINT
This text takes its starting point from the conference topic “AI – Text and Validity. How do AI text generators change scientific discourses?” and adds to this topic the perspective of a Collective Human-Machine Intelligence using the example of text generation. The concepts of text and validity, AI text generators, scientific discourse, and collective human-machine intelligence that are invoked in this constellation represent different fields of meaning that cannot automatically be interpreted as elements of a common conceptual framework.
TRANSDISCIPLINARY
In order to be able to let the mentioned terms appear as elements in a common conceptual framework, a meta-level is needed from which one can talk about these terms and their possible relations to each other. This approach is usually located in the philosophy of science, which can have as its subject not only single terms or whole propositions, but even whole theories that are compared or possibly even united. The term transdisciplinary [1] , which is often used today, is understood here in this philosophy of science understanding as an approach in which the integration of different concepts is redeemed by introducing appropriate meta-levels. Such a meta-level ultimately always represents a structure in which all important elements and relations can gather.
[1] Jürgen Mittelstraß paraphrases the possible meaning of the term transdisciplinarity as a “research and knowledge principle … that becomes effective wherever a solely technical or disciplinary definition of problem situations and problem solutions is not possible…”. Article Methodological Transdisciplinarity, in LIFIS ONLINE, www.leibniz-institut.de, ISSN 1864-6972, p.1 (first published in: Technology Assessment – Theory and Practice No.2, 14.Jg., June 2005, 18-23). In his text Mittelstrass distinguishes transdisciplinarity from the disciplinary and from the interdisciplinary. However, he uses only a general characterization of transdisciplinarity as a research guiding principle and scientific form of organization. He leaves the concrete conceptual formulation of transdisciplinarity open. This is different in the present text: here the transdisciplinary theme is projected down to the concreteness of the related terms and – as is usual in philosophy of science (and meta-logic) – realized by means of the construct of meta-levels.
SETTING UP A STRUCTURE
Here the notion of scientific discourse is assumed as a basic situation in which different actors can be involved. The main types of actors considered here are humans, who represent a part of the biological systems on planet Earth as a kind of Homo sapiens, and text generators, which represent a technical product consisting of a combination of software and hardware.
It is assumed that humans perceive their environment and themselves in a species-typical way, that they can process and store what they perceive internally, that they can recall what they have stored to a limited extent in a species-typical way, and that they can change it in a species-typical way, so that internal structures can emerge that are available for action and communication. All these elements are attributed to human cognition. They are working partially consciously, but largely unconsciously. Cognition also includes the subsystem language, which represents a structure that on the one hand is largely species-typically fixed, but on the other hand can be flexibly mapped to different elements of cognition.
In the terminology of semiotics [2] the language system represents a symbolic level and those elements of cognition, on which the symbolic structures are mapped, form correlates of meaning, which, however, represent a meaning only insofar as they occur in a mapping relation – also called meaning relation. A cognitive element as such does not constitute meaning in the linguistic sense. In addition to cognition, there are a variety of emotional factors that can influence both cognitive processes and the process of decision-making. The latter in turn can influence thought processes as well as action processes, consciously as well as unconsciously. The exact meaning of these listed structural elements is revealed in a process model [3] complementary to this structure.
[2] See, for example, Winfried Nöth: Handbuch der Semiotik. 2nd, completely revised edition. Metzler, Stuttgart/Weimar, 2000
[3] Such a process model is presented here only in partial aspects.
SYMBOLIC COMMUNICATION SUB-PROCESS
What is important for human actors is that they can interact in the context of symbolic communication with the help of both spoken and written language. Here it is assumed – simplistically — that spoken language can be mapped sufficiently accurately into written language, which in the standard case is called text. It should be noted that texts only represent meaning if the text producers involved, as well as the text recipients, have a meaning function that is sufficiently similar. For texts by human text producers it is generally true that, with respect to concrete situations, statements as part of texts can be qualified under agreed conditions as now matching the situation (true) or as now not now matching the situation (false). However, a now-true can become a now-not-true again in the next moment and vice versa.
This dynamic fact refers to the fact that a punctual occurrence or non-occurrence of a statement is to be distinguished from a structural occurrence/ non-occurrence of a statement, which speaks about occurrence/ non-occurrence in context. This refers to relations which are only indirectly apparent in the context of a multitude of individual events, if one considers chains of events over many points in time. Finally, one must also consider that the correlates of meaning are primarily located within the human biological system. Meaning correlates are not automatically true as such, but only if there is an active correspondence between a remembered/thought/imagined meaning correlate and an active perceptual element, where an intersubjective fact must correspond to the perceptual element. Just because someone talks about a rabbit and the recipient understands what a rabbit is, this does not mean that there is also a real rabbit which the recipient can perceive.
TEXT-GENERATORS
When distinguishing between the two different types of actors – here biological systems of the type Homo sapiens and there technical systems of the type text-generators – a first fundamental asymmetry immediately strikes the eye: so-called text-generators are entities invented and built by humans; furthermore, it is humans who use them, and the essential material used by text-generators are furthermore texts, which are considered human cultural property, created and used by humans for a variety of discourse types, here restricted to scientific discourse.
In the case of text generators, let us first note that we are dealing with machines that have input and output, a minimal learning capability, and whose input and output can process text-like objects. Insofar as text generators can process text-like objects as input and process them again as output, an exchange of texts between humans and text generators can take place in principle.
At the current state of development (September 2023), text generators do not yet have an independent real-world perception within the scope of their input, and the entire text generator system does not yet have such processes as those that enable species-typical cognitions in humans. Furthermore, a text generator does not yet have a meaning function as it is given with humans.
From this fact it follows automatically that text generators cannot decide about selective or structural correctness/not correctness in the case of statements of a text. In general, they do not have their own assignment of meaning as with humans. Texts generated by text generators only have a meaning if a human as a recipient automatically assigns a meaning to a text due to his species-typical meaning relation, because this is the learned behavior of a human. In fact, the text generator itself has never assigned any meaning to the generated text. Salopp one could also formulate that a technical text generator works like a parasite: it collects texts that humans have generated, rearranges them combinatorially according to formal criteria for the output, and for the receiving human a meaning event is automatically triggered by the text in the human, which does not exist anywhere in the text generator. Whether this very restricted form of text generation is now in any sense detrimental or advantageous for the type of scientific discourse (with texts), that is to be examined in the further course.
SCIENTIFIC DISCOURSE
There is no clear definition for the term scientific discourse. This is not surprising, since an unambiguous definition presupposes that there is a fully specified conceptual framework within which terms such as discourse and scientific can be clearly delimited. However, in the case of a scientific enterprise with a global reach, broken down into countless individual disciplines, this does not seem to be the case at present (Sept 2023). For the further procedure, we will therefore fall back on core ideas of the discussion in philosophy of science since the 20th century [4]and we will introduce working hypotheses on the concept of empirical theory as well as sustainable empirical theory, so that a working hypothesis on the concept of scientific discourse will be possible, which has a minimal sharpness.
[4] A good place to start may be: F. Suppe, Editor. The Structure of Scientific Theories. University of Illinois Press, Urbana, 2 edition, 1979.
EMPIRICAL THEORY
The following assumptions are made for the notion of an empirical theory:
an empirical theory is basically a text, written in a language that all participants understand.
one part of the theory contains a description of an initial situation, the statements of which can be qualified by the theory users as now matching (true) or now not matching (false).
another part of the theory contains a text that lists all changes that, to the knowledge of the participants, occur in the context of the initial situation and can change parts of the initial situation.
changes in the initial situation are expressed by replacing certain statements of the initial situation with other statements. The resulting new text replaces the previous text.
through the possibility of generating new initial situations, there is the possibility of making predictions (forming expectations) by applying rules of change to an applicable initial situation several times (at least once) in succession. The texts that are generated in each case induce in the minds of the participants, on the basis of the available meaning functions, the idea of a situation that is demanded to become qualified as now matching with intersubjective reality, should it occur. In the case of occurrence, the situation must correspond to the conception in the mind via perception. Whether such a correspondence can be established, after how long time and to what extent, this is basically open in the presence of the prediction making (the formation of an expectation) (autonomy of the object!).
SCIENTIFIC DISCOURSE NEW
Even before the introduction of the notion of a sustainable empirical theory, the notion of an empirical theory just introduced allows for initial statements about what a scientific discourse could or should be. It is assumed here that the participants in a scientific discourse exchange their opinions about the world in the form of texts, all of which meet the requirements of an empirical theory.
WITH HUMANS
Insofar as the participants in the discourse are human beings, we know that they have basic species-typical abilities to generate subjective meanings by means of language, which can be decided as now true or now not true by reference to a real situation.
WITH TEXT GENERATORS
Insofar as the discourse participants would be text generators lacking these elementary skills (meaning, reality reference), they drop out as independent actors for a scientific discourse. To illustrate this, here is a self-assessment of chatGPT4, a current text generator:
ChatGPT
Yes, your summary of our previous dialogue is accurate based on the points you’ve listed:
I cannot read a book in the same way a human does, meaning I don’t interpret or comprehend in the same manner.
My knowledge is up to September 2021, which means that I might not be aware of events, advancements, or publications after that point.
My “learning” is different from human learning. I don’t learn from new experiences or information after my last update. Instead, I generate responses based on patterns in the data I was trained on.
I don’t cite individual sources in my responses. I provide information based on the patterns recognized during my training.
Responses I give can vary slightly even if the same question is posed multiple times.
I can’t directly attribute my statements to specific, individual sources.
The specifics about my training data and the processes that determine my responses are not transparently provided in each interaction.
You’ve captured the essence of our prior conversation well. If you have further questions or need clarifications on any of these points, please let me know. [5]
[5] This text is from a chat experiment conducted by the author with chatGPT4 on August 31, 2023. The citation can be found in: Gerd Doeben-Henisch, CONTINUE EXPERIMENT WITH CHATGPT4: Science, Empirical Science, Philosophy of Science, uffmm, Integrating Engineering and the Human Factor, eJournal uffmm.org ISSN 2567-6458, 8/31/2023 in [https://www.uffmm.org/2023/08/31/continue-experiment-with-chatgpt4-science-empirical-science-philosophy-of-science/ ] (accessed 9/27/2023).
The question then arises whether (current) text generators, despite their severely limited capabilities, could nevertheless contribute to scientific discourse, and what this contribution means for human participants. Since text generators fail for the hard scientific criteria (decidable reality reference, reproducible predictive behavior, separation of sources), one can only assume a possible contribution within human behavior: since humans can understand and empirically verify texts, they would in principle be able to rudimentarily classify a text from a text generator within their considerations.
For hard theory work, these texts would not be usable, but due to their literary-associative character across a very large body of texts, the texts of text generators could – in the positive case – at least introduce thoughts into the discourse through texts as stimulators via the detour of human understanding, which would stimulate the human user to examine these additional aspects to see if they might be important for the actual theory building after all. In this way, the text generators would not participate independently in the scientific discourse, but they would indirectly support the knowledge process of the human actors as aids to them.[6]
[6] A detailed illustration of this associative role of a text generator can also be found in (Doeben-Henisch, 2023) on the example of the term philosophy of science and on the question of the role of philosophy of science.
CHALLENGE DECISION
The application of an empirical theory can – in the positive case — enable an expanded picture of everyday experience, in that, related to an initial situation, possible continuations (possible futures) are brought before one’s eyes. For people who have to shape their own individual processes in their respective everyday life, however, it is usually not enough to know only what one can do.Rather, everyday life requires deciding in each case which continuation to choose, given the many possible continuations. In order to be able to assert themselves in everyday life with as little effort as possible and with – at least imagined – as little risk as possible, people have adopted well-rehearsed behavior patterns for as many everyday situations as possible, which they follow spontaneously without questioning them anew each time. These well-rehearsed behavior patterns include decisions that have been made. Nevertheless, there are always situations in which the ingrained automatisms have to be interrupted in order to consciously clarify the question for which of several possibilities one wants to decide.
The example of an individual decision-maker can also be directly applied to the behavior of larger groups. Normally, even more individual factors play a role here, all of which have to be integrated in order to reach a decision. However, the characteristic feature of a decision situation remains the same: whatever knowledge one may have at the time of decision, when alternatives are available, one has to decide for one of many alternatives without any further, additional knowledge at this point. Empirical science cannot help here [7]: it is an indisputable basic ability of humans to be able to decide.
So far, however, it remains rather hidden in the darkness of not knowing oneself, which ultimately leads to deciding for one and not for the other. Whether and to what extent the various cultural patterns of decision-making aids in the form of religious, moral, ethical or similar formats actually form or have formed a helpful role for projecting a successful future appears to be more unclear than ever.[8]
[7] No matter how much detail she can contribute about the nature of decision-making processes.
[8] This topic is taken up again in the following in a different context and embedded there in a different solution context.
SUSTAINABLE EMPIRICAL THEORY
Through the newly flared up discussion about sustainability in the context of the United Nations, the question of prioritizing action relevant to survival has received a specific global impulse. The multitude of aspects that arise in this discourse context [9] are difficult, if not impossible, to classify into an overarching, consistent conceptual framework.
[9] For an example see the 17 development goals: [https://unric.org/de/17ziele/] (Accessed: September 27, 2023)
A rough classification of development goals into resource-oriented and actor-oriented can help to make an underlying asymmetry visible: a resource problem only exists if there are biological systems on this planet that require a certain configuration of resources (an ecosystem) for their physical existence. Since the physical resources that can be found on planet Earth are quantitatively limited, it is possible, in principle, to determine through thought and science under what conditions the available physical resources — given a prevailing behavior — are insufficient. Added to this is the factor that biological systems, by their very existence, also actively alter the resources that can be found.
So, if there should be a resource problem, it is exclusively because the behavior of the biological systems has led to such a biologically caused shortage. Resources as such are neither too much, nor too little, nor good, nor bad. If one accepts that the behavior of biological systems in the case of the species Homo sapiens can be controlled by internal states, then the resource problem is primarily a cognitive and emotional problem: Do we know enough? Do we want the right thing? And these questions point to motivations beyond what is currently knowable. Is there a dark spot in the human self-image here?
On the one hand, this questioning refers to the driving forces for a current decision beyond the possibilities of the empirical sciences (trans-empirical, meta-physical, …), but on the other hand, this questioning also refers to the center/ core of human competence. This motivates to extend the notion of empirical theory to the notion of a sustainable empirical theory. This does not automatically solve the question of the inner mechanism of a value decision, but it systematically classifies the problem. The problem thus has an official place. The following formulation is suggested as a characterization for the concept of a sustainable empirical theory:
a sustainable empirical theory contains an empirical theory as its core.
besides the parts of initial situation, rules of change and application of rules of change, a sustainable theory also contains a text with a list of such situations, which are considered desirable for a possible future (goals, visions, …).
under the condition of goals, it is possible to minimally compare each current situation with the available goals and thereby indicate the degree of goal achievement.
Stating desired goals says nothing about how realistic or promising it is to pursue those goals. It only expresses that the authors of this theory know these goals and consider them optimal at the time of theory creation. [10] The irrationality of chosen goals is in this way officially included in the domain of thought of the theory creators and in this way facilitates the extension of the rational to the irrational without already having a real solution. Nobody can exclude that the phenomenon of bringing forth something new, respectively of preferring a certain point of view in comparison to others, can be understood further and better in the future.
[10] Something can only be classified as optimal if it can be placed within an overarching framework, which allows for positioning on a scale. This refers to a minimal cognitive model as an expression of rationality. However, the decision itself takes place outside of such a rational model; in this sense, the decision as an independent process is pre-rational.
EXTENDED SCIENTIFIC DISCOURSE
If one accepts the concept of a sustainable empirical theory, then one can extend the concept of a scientific discourse in such a way that not only texts that represent empirical theories can be introduced, but also those texts that represent sustainable empirical theories with their own goals. Here too, one can ask whether the current text generators (September 2023) can make a constructive contribution. Insofar as a sustainable empirical theory contains an empirical theory as a hard core, the preceding observations on the limitations of text generators apply. In the creative part of the development of an empirical theory, they can contribute text fragments through their associative-combinatorial character based on a very large number of documents, which may inspire the active human theory authors to expand their view. But what about that part that manifests itself in the selection of possible goals? At this point, one must realize that it is not about any formulations, but about those that represent possible solution formulations within a systematic framework; this implies knowledge of relevant and verifiable meaning structures that could be taken into account in the context of symbolic patterns. Text generators fundamentally do not have these abilities. But it is – again – not to be excluded that their associative-combinatorial character based on a very large number of documents can still provide one or the other suggestion.
In retrospect of humanity’s history of knowledge, research, and technology, it is suggested that the great advances were each triggered by something really new, that is, by something that had never existed before in this form. The praise for Big Data, as often heard today, represents – colloquially speaking — exactly the opposite: The burial of the new by cementing the old.[11]
[11] A prominent example of the naive fixation on the old as a standard for what is right can be seen, for example, in the book by Seth Stephens-Davidowitz, Don’t Trust Your Gut. Using Data Instead of Instinct To Make Better Choices, London – Oxford New York et al., 2022.
EXISTENTIALLY NEW THROUGH TIME
The concept of an empirical theory inherently contains the element of change, and even in the extended concept of a sustainable empirical theory, in addition to the fundamental concept of change, there is the aspect of a possible goal. A possible goal itself is not a change, but presupposes the reality of changes! The concept of change does not result from any objects but is the result of a brain performance, through which a current present is transformed into a partially memorable state (memory contents) by forming time slices in the context of perception processes – largely unconsciously. These produced memory contents have different abstract structures, are networked differently with each other, and are assessed in different ways. In addition, the brain automatically compares current perceptions with such stored contents and immediately reports when a current perception has changed compared to the last perception contents. In this way, the phenomenon of change is a fundamental cognitive achievement of the brain, which thus makes the character of a feeling of time available in the form of a fundamental process structure. The weight of this property in the context of evolution is hardly to be overestimated, as time as such is in no way perceptible.
[12] The modern invention of machines that can generate periodic signals (oscillators, clocks) has been successfully integrated into people’s everyday lives. However, the artificially (technically) producible time has nothing to do with the fundamental change found in reality. Technical time is a tool that we humans have invented to somehow structure the otherwise amorphous mass of a phenomenon stream. Since structure itself shows in the amorphous mass, which manifest obviously for all, repeating change cycles (e.g., sunrise and sunset, moonrise and moonset, seasons, …), a correlation of technical time models and natural time phenomena was offered. From the correlations resulting here, however, one should not conclude that the amorphous mass of the world phenomenon stream actually behaves according to our technical time model. Einstein’s theory of relativity at least makes us aware that there can be various — or only one? — asymmetries between technical time and world phenomenon stream.
Assuming this fundamental sense of time in humans, one can in principle recognize whether a current phenomenon, compared to all preceding phenomena, is somehow similar or markedly different, and in this sense indicates something qualitatively new.[13]
[13] Ultimately, an individual human only has its individual memory contents available for comparison, while a collective of people can in principle consult the set of all records. However, as is known, only a minimal fraction of the experiential reality is symbolically transformed.
By presupposing the concept of directed time for the designation of qualitatively new things, such a new event is assigned an information value in the Shannonian sense, as well as the phenomenon itself in terms of linguistic meaning, and possibly also in the cognitive area: relative to a spanned knowledge space, the occurrence of a qualitatively new event can significantly strengthen a theoretical assumption. In the latter case, the cognitive relevance may possibly mutate to a sustainable relevance if the assumption marks a real action option that could be important for further progress. In the latter case, this would provoke the necessity of a decision: should we adopt this action option or not? Humans can accomplish the finding of qualitatively new things. They are designed for it by evolution. But what about text generators?
Text generators so far do not have a sense of time comparable to that of humans. Their starting point would be texts that are different, in such a way that there is at least one text that is the most recent on the timeline and describes real events in the real world of phenomena. Since a text generator (as of September 2023) does not yet have the ability to classify texts regarding their applicability/non-applicability in the real world, its use would normally end here. Assuming that there are people who manually perform this classification for a text generator [14] (which would greatly limit the number of possible texts), then a text generator could search the surface of these texts for similar patterns and, relative to them, for those that cannot be compared. Assuming that the text generator would find a set of non-comparable patterns in acceptable time despite a massive combinatorial explosion, the problem of semantic qualification would arise again: which of these patterns can be classified as an indication of something qualitatively new? Again, humans would have to become active.
[14] Such support of machines by humans in the field of so-called intelligent algorithms has often been applied (and is still being applied today, see: [https://www.mturk.com/] (Accessed: September 27, 2023)), and is known to be very prone to errors.
As before, the verdict is mixed: left to itself, a text generator will not be able to solve this task, but in cooperation with humans, it may possibly provide important auxiliary services, which could ultimately be of existential importance to humans in search of something qualitatively new despite all limitations.
THE IMMANENT PREJUDICE OF THE SCIENCES
A prejudice is known to be the assessment of a situation as an instance of a certain pattern, which the judge assumes applies, even though there are numerous indications that the assumed applicability is empirically false. Due to the permanent requirement of everyday life that we have to make decisions, humans, through their evolutionary development, have the fundamental ability to make many of their everyday decisions largely automatically. This offers many advantages, but can also lead to conflicts.
Daniel Kahneman introduced in this context in his book [15] the two terms System 1 and System 2 for a human actor. These terms describe in his concept of a human actor two behavioral complexes that can be distinguished based on some properties.[16] System 1 is set by the overall system of human actor and is characterized by the fact that the actor can respond largely automatically to requirements by everyday life. The human actor has automatic answers to certain stimuli from his environment, without having to think much about it. In case of conflicts within System 1 or from the perspective of System 2, which exercises some control over the appropriateness of System 1 reactions in a certain situation in conscious mode, System 2 becomes active. This does not have automatic answers ready, but has to laboriously work out an answer to a given situation step by step. However, there is also the phenomenon that complex processes, which must be carried out frequently, can be automated to a certain extent (bicycling, swimming, playing a musical instrument, learning language, doing mental arithmetic, …). All these processes are based on preceding decisions that encompass different forms of preferences. As long as these automated processes are appropriate in the light of a certain rational model, everything seems to be OK. But if the corresponding model is distorted in any sense, then it would be said that these models carry a prejudice.
[15] Daniel Kahnemann, Thinking Fast and Slow, Pinguin Boooks Random House, UK, 2012 (zuerst 2011)
[16] See the following Chapter 1 in Part 1 of (Kahnemann, 2012, pages 19-30).
In addition to the countless examples that Kahneman himself cites in his book to show the susceptibility of System 1 to such prejudices, it should be pointed out here that the model of Kahneman himself (and many similar models) can carry a prejudice that is of a considerably more fundamental nature. The division of the behavioral space of a human actor into a System 1 and 2, as Kahneman does, obviously has great potential to classify many everyday events. But what about all the everyday phenomena that fit neither the scheme of System 1 nor the scheme of System 2?
In the case of making a decision, System 1 comments that people – if available – automatically call up and execute an available answer. Only in the case of conflict under the control of System 2 can there be lengthy operations that lead to other, new answers.
In the case of decisions, however, it is not just about reacting at all, but there is also the problem of choosing between known possibilities or even finding something new because the known old is unsatisfactory.
Established scientific disciplines have their specific perspectives and methods that define areas of everyday life as a subject area. Phenomena that do not fit into this predefined space do not occur for the relevant discipline – methodically conditioned. In the area of decision-making and thus the typical human structures, there are not a few areas that have so far not found official entry into a scientific discipline. At a certain point in time, there are ultimately many, large phenomenon areas that really exist, but methodically are not present in the view of individual sciences. For a scientific investigation of the real world, this means that the sciences, due to their immanent exclusions, are burdened with a massive reservation against the empirical world. For the task of selecting suitable sustainable goals within the framework of sustainable science, this structurally conditioned fact can be fatal. Loosely formulated: under the banner of actual science, a central principle of science – the openness to all phenomena – is simply excluded, so as not to have to change the existing structure.
For this question of a meta-reflection on science itself, text generators are again only reduced to possible abstract text delivery services under the direction of humans.
SUPERVISION BY PHILOSOPHY
The just-described fatal dilemma of all modern sciences is to be taken seriously, as without an efficient science, sustainable reflection on the present and future cannot be realized in the long term. If one agrees that the fatal bias of science is caused by the fact that each discipline works intensively within its discipline boundaries, but does not systematically organize communication and reflection beyond its own boundaries with a view to other disciplines as meta-reflection, the question must be answered whether and how this deficit can be overcome.
There is only one known answer to this question: one must search for that conceptual framework within which these guiding concepts can meaningfully interact both in their own right and in their interaction with other guiding concepts, starting from those guiding concepts that are constitutive for the individual disciplines.
This is genuinely the task of philosophy, concretized by the example of the philosophy of science. However, this would mean that each individual science would have to use a sufficiently large part of its capacities to make the idea of the one science in maximum diversity available in a real process.
For the hard conceptual work hinted at here, text generators will hardly be able to play a central role.
COLLECTIVE INTELLIGENCE
Since so far there is no concept of intelligence in any individual science that goes beyond a single discipline, it makes little sense at first glance to apply the term intelligence to collectives. However, looking at the cultural achievements of humanity as a whole, and here not least with a view to the used language, it is undeniable that a description of the performance of an individual person, its individual performance, is incomplete without reference to the whole.
So, if one tries to assign an overarching meaning to the letter combination intelligence, one will not be able to avoid deciphering this phenomenon of the human collective in the form of complex everyday processes in a no less complex dynamic world, at least to the extent that one can identify a somewhat corresponding empirical something for the letter combination intelligence, with which one could constitute a comprehensible meaning.
Of course, this term should be scalable for all biological systems, and one would have to have a comprehensible procedure that allows the various technical systems to be related to this collective intelligence term in such a way that direct performance comparisons between biological and technical systems would be possible.[17]
[17] The often quoted and popular Turing Test (See: Alan M. Turing: Computing Machinery and Intelligence. In: Mind. Volume LIX, No. 236, 1950, 433–460, [doi:10.1093/mind/LIX.236.433] (Accessed: Sept 29, 2023) in no way meets the methodological requirements that one would have to adhere to if one actually wanted to come to a qualified performance comparison between humans and machines. Nevertheless, the basic idea of Turing in his meta-logical text from 1936, published in 1937 (see: A. M. Turing: On Computable Numbers, with an Application to the Entscheidungsproblem. In: Proceedings of the London Mathematical Society. s2-42. Volume, No. 1, 1937, 230–265 [doi:10.1112/plms/s2-42.1.230] (Accessed: Sept 29, 2023) seems to be a promising starting point, since he, in trying to present an alternative formulation to Kurt Gödel’s (1931) proof on the undecidability of arithmetic, leads a meta-logical proof, and in this context Turing introduces the concept of a machine that was later called Universal Turing Machine.
Already in this proof approach, it can be seen how Turing transforms the phenomenon of a human bookkeeper at a meta-level into a theoretical concept, by means of which he can then meta-logically examine the behavior of this bookkeeper in a specific behavioral space. His meta-logical proof not only confirmed Gödel’s meta-logical proof, but also indirectly indicates how ultimately any phenomenal complexes can be formalized on a meta-level in such a way that one can then argue formally demanding with it.
CONCLUSION STRUCTURALLY
The idea of philosophical supervision of the individual sciences with the goal of a concrete integration of all disciplines into an overall conceptual structure seems to be fundamentally possible from a philosophy of science perspective based on the previous considerations. From today’s point of view, specific phenomena claimed by individual disciplines should no longer be a fundamental obstacle for a modern theory concept. This would clarify the basics of the concept of Collective Intelligence and it would surely be possible to more clearly identify interactions between human collective intelligence and interactive machines. Subsequently, the probability would increase that the supporting machines could be further optimized, so that they could also help in more demanding tasks.
CONCLUSION SUBJECTIVELY
Attempting to characterize the interactive role of text generators in a human-driven scientific discourse, assuming a certain scientific model, appears to be somewhat clear from a transdisciplinary (and thus structural) perspective. However, such scientific discourse represents only a sub-space of the general human discourse space. In the latter, the reception of texts from the perspective of humans inevitably also has a subjective view [18]: People are used to suspecting a human author behind a text. With the appearance of technical aids, texts have increasingly become products, which increasingly gaining formulations that are not written down by a human author alone, but by the technical aids themselves, mediated by a human author. With the appearance of text generators, the proportion of technically generated formulations increases extremely, up to the case that ultimately the entire text is the direct output of a technical aid. It becomes difficult to impossible to recognize to what extent a controlling human share can still be spoken of here. The human author thus disappears behind a text; the sign reality which does not prevent an existential projection of the inner world of the human reader into a potential human author, but threatens to lose itself or actually loses itself in the real absence of a human author in the face of a chimeric human counterpart. What happens in a world where people no longer have human counterparts?
[18] There is an excellent analysis on this topic by Hannes Bajohr titled “Artifizielle und postartifizielle Texte. Über Literatur und Künstliche Intelligenz” (Artificial and Post-Artificial Texts: On Literature and Artificial Intelligence). It was the Walter-Höllerer-Lecture 2022, delivered on December 8, 2022, at the Technical University of Berlin. The lecture can be accessed here [ https://hannesbajohr.de/wp-content/uploads/2022/12/Hoellerer-Vorlesung-2022.pdf ] (Accessed: September 29, 2023). The reference to this lecture was provided to me by Jennifer Becker.
The following text is a confluence of ideas that have been driving me for many months. Parts of it can be found as texts in all three blogs (Citizen Science 2.0 for Sustainable Development, Integrated Engineering and the Human Factor (this blog), Philosophy Now. In Search for a new Human Paradigm). The choice of the word ‘grammar’ [1] for the following text is rather unusual, but seems to me to reflect the character of the reflections well.
Sustainability for populations
The concept of sustainable development is considered here in the context of ‘biological populations’. Such populations are dynamic entities with many ‘complex properties’. For the analysis of the ‘sustainability’ of such populations, there is one aspect that seems ‘fundamental’ for a proper understanding. It is the aspect whether and how the members of a population – the actors – are interconnected or not.
An ‘unconnected’ set
If I have ‘actors’ of a ‘population’, which are in no direct ‘interaction’ with each other, then also the ‘acting’ of these actors is isolated from each other. In a wide area they probably do not ‘get in each other’s way’; in a narrow area they could easily hinder each other or even fight each other, up to mutual destruction.
It should be noted that even such disconnected actors must have minimal ‘knowledge’ about themselves and the environment, also minimal ’emotions’, in order to live at all.
Without direct interaction, an unconnected population will nevertheless die out relatively quickly as a population.
A ‘connected’ set
A ‘connected set’ exists if the actors of a population have a sufficient number of direct interactions through which they could ‘coordinate’ their knowledge about themselves and the world, as well as their emotions, to such an extent that they are capable of ‘coordinated action’. Thereby the single, individual actions become related to their possible effect to a ‘common (= social) action’ which can effect more than each of them would have been able to do individually.
The ’emotions’ involved must rather be such that they do not so much ‘delimit/exclude’, but rather ‘include/recognize’.
The ‘knowledge’ involved must be rather that it is not ‘static’ and not ‘unrealistic’, but rather ‘open’, ‘learning’ and ‘realistic’.
The ‘survival’ of a connected population is basically possible if the most important ‘factors’ of a survival are sufficiently fulfilled.
Transitions from – to
The ‘transition’ from an ‘unconnected’ to a ‘connected’ state of a population is not inevitable. The primary motive may simply be the ‘will to survive’ (an emotion), and the growing ‘insight’ (= knowledge) that this is only possible with ‘minimal cooperation’. An individual, however, can live in a state of ‘loner’ for the duration of his life, because he does not have to experience his individual death as a sufficient reason to ally with others. A population as such, however, can only survive if a sufficient number of individuals survive, interacting minimally with each other. The history of life on planet Earth suggests the working hypothesis that for 3.5 billion years there have always been sufficient members of a population in biological populations (including the human population) to counter the ‘self-destructive tendencies’ of individuals with a ‘constructive tendency’.
The emergence and the maintenance of a ‘connected population’ needs a minimum of ‘suitable knowledge’ and ‘suitable emotions’ to succeed.
It is a permanent challenge for all biological populations to shape their own emotions in such a way that they tend not to exclude, to despise, but rather to include and to recognize. Similarly, knowledge must be suitable for acquiring a realistic picture of oneself, others, and the environment so that the behavior in question is ‘factually appropriate’ and tends to be more likely to lead to ‘success’.
As the history of the human population shows, both the ‘shaping of emotions’ and the ‘shaping of powerful knowledge’ are usually largely underestimated and poorly or not at all organized. The necessary ‘effort’ is shied away from, one underestimates the necessary ‘duration’ of such processes. Within knowledge there is additionally the general problem that the ‘short time spans’ within an individual life are an obstacle to recognize and form such processes where larger time spans require it (this concerns almost all ‘important’ processes).
We must also note that ‘connected states’ of populations can also collapse again at any time, if those behaviors that make them possible are weakened or disappear altogether. Connections in the realm of biological populations are largely ‘undetermined’! They are based on complex processes within and between the individual actors. Whole societies can ‘topple overnight’ if an event destroys ‘trust in context’. Without trust no context is possible. The emergence and the passing away of trust should be part of the basic concern of every society in a state of interconnectedness.
Political rules of the game
‘Politics’ encompasses the totality of arrangements that members of a human population agree to organize jointly binding decision-making processes.[2] On a rough scale, one could place two extremes: (i) On the one hand, a population with a ‘democratic system’ [3] and a population with a maximally un-democratic system.[4]
As already noted in general for ‘connected systems’: the success of democratic systems is in no way determinate. Enabling and sustaining it requires the total commitment of all participants ‘by their own conviction’.
Basic reality ‘corporeality’
Biological populations are fundamentally characterized by a ‘corporeality’ which is determined through and through by ‘regularities’ of the known material structures. In their ‘complex formations’ biological systems manifest also ‘complex properties’, which cannot be derived simply from their ‘individual parts’, but the respective identifiable ‘material components’ of their ‘body’ together with many ‘functional connections’ are fundamentally subject to a multiplicity of ‘laws’ which are ‘given’. To ‘change’ these is – if at all – only possible under certain limited conditions.
All biological actors consist of ‘biological cells’ which are the same for all. In this, human actors are part of the total development of (biological) life on planet Earth. The totality of (biological) life is also called ‘biome’ and the total habitat of a biome is also called ‘biosphere’. [5] The population of homo sapiens is only a vanishingly small part of the biome, but with the homo sapiens typical way of life it claims ever larger parts of the biosphere for itself at the expense of all other life forms.
(Biological) life has been taking place on planet Earth for about 3.5 billion years.[6] Earth, as part of the solar system [7], has had a very eventful history and shows strong dynamics until today, which can and does have a direct impact on the living conditions of biological life (continental plate displacement, earthquakes, volcanic eruptions, magnetic field displacement, ocean currents, climate, …).
Biological systems generally require a continuous intake of material substances (with energy potentials) to enable their own metabolic processes. They also excrete substances. Human populations need certain amounts of ‘food’, ‘water’, ‘dwellings’, ‘storage facilities’, ‘means of transport’, ‘energy’, … ‘raw materials’, … ‘production processes’, ‘exchange processes’ … As the sheer size of a population grows, the material quantities required (and also wastes) multiply to orders of magnitude that can destroy the functioning of the biosphere.
Predictive knowledge
If a coherent population does not want to leave possible future states to pure chance, then it needs a ‘knowledge’ which is suitable to construct ‘predictions’ (‘prognoses’) for a possible future (or even many ‘variants of future’) from the knowledge about the present and about the past.
In the history of homo sapiens so far, there is only one form of knowledge that has been demonstrably demonstrated to be suitable for resilient sustainable forecasts: the knowledge form of empirical sciences. [8] This form of knowledge is so far not perfect, but a better alternative is actually not known. At its core, ’empirical knowledge’ comprises the following elements: (i) A description of a baseline situation that is assumed to be ’empirically true’; (ii) A set of ‘descriptions of change processes’ that one has been able to formulate over time, and from which one knows that it is ‘highly probable’ that the described changes will occur again and again under known conditions; (iii) An ‘inference concept’ that describes how to apply to the description of a ‘given current situation’ the known descriptions of change processes in such a way that one can modify the description of the current situation to produce a ‘modified description’ that describes a new situation that can be considered a ‘highly probable continuation’ of the current situation in the future. [9]
The just sketched ‘basic idea’ of an empirical theory with predictive ability can be realized concretely in many ways. To investigate and describe this is the task of ‘philosophy of science’. However, the vagueness found in dealing with the notion of an ’empirical theory’ is also found in the understanding of what is meant by ‘philosophy of science.'[9]
In the present text, the view is taken that the ‘basic concept’ of an empirical theory can be fully realized in normal everyday action using everyday language. This concept of a ‘General Empirical Theory’ can be extended by any special languages, methods and sub-theories as needed. In this way, the hitherto unsolved problem of the many different individual empirical disciplines could be solved almost by itself.[10]
Sustainable knowledge
In the normal case, an empirical theory can, at best, generate forecasts that can be said to have a certain empirically based probability. In ‘complex situations’ such a prognosis can comprise many ‘variants’: A, B, …, Z. Now which of these variants is ‘better’ or ‘worse’ in the light of an ‘assumable criterion’ cannot be determined by an empirical theory itself. Here the ‘producers’ and the ‘users’ of the theory are asked: Do they have any ‘preferences’ why e.g. variant ‘B’ should be preferred to variant ‘C”: “Bicycle, subway, car or plane?” , “Genetic engineering or not?”, “Pesticides or not?”, “Nuclear energy or not?”, “Uncontrolled fishing or not?” …
The ‘evaluation criteria’ to be applied actually themselves require ‘explicit knowledge’ for the estimation of a possible ‘benefit’ on the one hand, on the other hand the concept of ‘benefit’ is anchored in the feeling and wanting of human actors: Why exactly do I want something? Why does something ‘feel good’? …
Current discussions worldwide show that the arsenal of ‘evaluation criteria’ and their implementation offer anything but a clear picture.
COMMENTS
[1] For the typical use of the term ‘grammar’ see the English Wikipedia: https://en.wikipedia.org/wiki/Grammar. In the text here in the blog I transfer this concept of ‘language’ to that ‘complex process’ in which the population of the life form ‘homo sapiens’ tries to achieve an ‘overall state’ on planet earth that allows a ‘maximally good future’ for as much ‘life’ as possible (with humans as a sub-population). A ‘grammar of sustainability’ presupposes a certain set of basic conditions, factors, which ‘interact’ with each other in a dynamic process, in order to realize as many states as possible in a ‘sequence of states’, which enable as good a life as possible for as many as possible.
[2] For the typical usage of the term politics, see the English Wikipedia: https://en.wikipedia.org/wiki/Politics . This meaning is also assumed in the present text here.
[4] Of course, one could also choose completely different basic concepts for a scale. However, the concept of a ‘democratic system’ (with all its weaknesses) seems to me to be the ‘most suitable’ system in the light of the requirements for sustainable development; at the same time, however, it makes the highest demands of all systems on all those involved. That it came to the formation of ‘democracy-like’ systems at all in the course of history, actually borders almost on a miracle. The further development of such democracy-like systems fluctuates constantly between preservation and decay. Positively, one could say that the constant struggle for preservation is a kind of ‘training’ to enable sustainable development.
[5] For typical uses of the terms ‘biome’ and ‘biosphere’, see the corresponding entries in the English Wikipedia: ‘biome’: https://en.wikipedia.org/wiki/Biome, ‘biosphere’: https://en.wikipedia.org/wiki/Biosphere
[6] Some basic data for planet Earth: https://en.wikipedia.org/wiki/Earth
[7] Some basic data for the solar system: https://en.wikipedia.org/wiki/Solar_System
[8] If you will search for he term ‘Empirical Science’ you ill be disappointed, because the English Wikipedia (as well as the German Version) does not provide such a term. You have either to accept the term ‘Science’ ( https://en.wikipedia.org/wiki/Science ) or the term ‘Empiricism’ (https://en.wikipedia.org/wiki/Empiricism), but both do not cover the general properties of an Empirical theory.
[9] If you have a clock with hour and minute hands, which currently shows 11:04h, and you know from everyday experience that the minute hand advances by one stroke every minute, then you can conclude with a fairly high probability that the minute hand will advance by one stroke ‘very soon’. The initial description ‘The clock shows 11:04h’ would then be changed to that of the new description ‘The clock shows 11:05h’. Before the ’11:05h event’ the statement ‘The clock shows 11:05h’ would have the status of a ‘forecast’.
[10] A single discipline (physics, chemistry, biology, psychology, …) cannot conceptually grasp ‘the whole’ ‘out of itself’; it does not have to. The various attempts to ‘reduce’ any single discipline to another (physics is especially popular here) have all failed so far. Without a suitable ‘meta-theory’ no single discipline can free itself from its specialization. The concept of a ‘General Empirical Theory’ is such a meta-theory. Such a meta-theory fits into the concept of a modern philosophical thinking.
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))
Integrating Engineering and the Human Factor (info@uffmm.org) eJournal uffmm.org ISSN 2567-6458, February 25, 2021
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.deLast change: March 16, 2021 (Some minor corrections)
Since January 2021 an intense series of posts has been published how the new ideas manifested in the new software published in this journal can adequately be reflected in the DAAI theoretical framework. Because these ideas included in the beginning parts of philosophy, philosophy of science, philosophy of engineering, these posts have been first published in the German Blog of the author (cognitiveagent.org). This series of posts started with an online lecture for students of the University of Leipzig together with students of the ‘Hochschule für Technik, Wirtschaft und Kultur (HTWK)’ January 12, 2021. Here is the complete list of posts:
HMI analysis for the CM:MI paradigm illustrated with the oksimo software concept
As described in the original DAAI theory paper the whole topic of HMI is here understood as a job within the systems engineering paradigm.
The specification process is a kind of a ‘test’ whether the DAAI format of the HMI analysis works with this new application too.
To remember, the main points of the integrated engineering concept are the following ones:
A philosophical framework (Philosophy of Science, Philosophy of Engineering, …), which gives the fundamentals for such a process.
The engineering process as such where managers and engineers start the whole process and do it.
After the clarification of the problem to be solved and a minimal vision, where to go, it is the job of the HMI analysis to clarify which requirements have to be fulfilled, to find an optimal solution for the intended product/ service. In modern versions of the HMI analysis substantial parts of the context, i.e. substantial parts of the surrounding society, have to be included in the analysis.
Based on the HMI analysis in the logical design phase a mathematical structure has to be identified, which integrates all requirements sufficiently well. This mathematical structure has to be ‘map-able’ into a set of algorithms written in appropriate programming languages running on an appropriate platform (the mentioned phases Problem, Vision, HMI analysis, Logical Design are in reality highly iterative).
During the implementation phase the algorithms will be translated into a real working system.
Which Kinds of Experts?
While the original version of the DAAI paper is assuming as ‘experts’ only the typical manager and engineers of an engineering process including all the typical settings, the new extended version under the label CM:MI (Collective Man-Machine Intelligence) has been generalized to any kind of human person as an expert, which allows a maximum of diversity. No one is the ‘absolute expert’.
Collective Intelligence
As ‘intelligence’ is understood here the whole of knowledge, experience, and motivations which can be the moving momentum inside of a human person. As ‘collective’ is meant the situation, where more than one person is communicating with other persons to share it’s intelligence.
Man-Machine Symbiosis
Today there are discussions going around about the future of man and (intelligent) machines. Most of these discussions are very weak because they are lacking clear concepts of intelligent machines as well of what is a human person. In the CM:MI paradigm the human person (together with all other biological systems) is seen at the center of the future (by reasons based on modern theories of biological evolution) and the intelligent machines are seen as supporting devices (although it is assumed here to use ‘strong’ intelligence compared to the actual ‘weak’ machine intelligence today).
CM:MI by Design
Although we know, that groups of many people are ‘in principal’ capable of sharing intelligence to define problems, visions, constructing solutions, testing the solutions etc., we know too, that the practical limits of the brains and the communication are quite narrow. For special tasks a computer can be much, much better. Thus the CM:MI paradigm provides an environment for groups of people to do the shared planning and testing in a new way, only using normal language. Thus the software is designed to enable new kinds of shared knowledge about shared common modes of future worlds. Only with such a truly general framework the vision of a sustainable society as pointed out by the United Nations since 1992 can become real.
Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, Nov 8, 2020
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de
In daily life we experience today a multitude of perspectives in all areas. While our bodies are embedded in real world scenarios our minds are filled up with perceptions, emotions, ideas, memories of all kinds. What links us to each other is language. Language gives us the power to overcome the isolation of our individual brains located in individual bodies. And by this, our language, we can distribute and share the inner states of our brains, pictures of life as we see it. And it is this open web of expressions which spreads to the air, to the newspapers and books, to the data bases in which the different views of the world are manifested.
SORTING IDEAS SCIENTIFICALLY
While our bodies touching reality outside the bodies, our brains are organizing different kinds of order, finally expressed — only some part of it — in expressions of some language. While our daily talk is following mostly automatically some naive patterns of ordering does empirical science try to order the expressions more consciously following some self-defined rules called methods, called scientific procedures to enable transparency, repeatability, decidability of the hypothesized truth of is symbolic structures.
But because empirical science wants to be rational by being transparent, repeatable, measurable, there must exist an open discourse which is dealing with science as an object: what are the ingredients of science? Under which conditions can science work? What does it mean to ‘measure’ something? And other questions like these.
PHILOSOPHY OF SCIENCE
That discipline which is responsible for such a discourse about science is not science itself but another instance of thinking and speaking which is called Philosophy ofScience. Philosophy of science deals with all aspects of science from the outside of science.
PHILOSOPHY
Philosophy of Science dealing with empirical sciences as an object has a special focus and it can be reflected too from another point of view dealing with Philosophy of Science as an object. This relationship reflects a general structure of human thinking: every time we have some object of our thinking we are practicing a different point of view talking about the actual object. While everyday thinking leads us directly to Philosophy as our active point of view an object like empirical science does allow an intermediate point of view called Philosophy of Science leading then to Philosophy again.
Philosophy is our last point of reflection. If we want to reflect the conditions of our philosophical thinking than our thinking along with the used language tries to turn back on itself but this is difficult. The whole history of Philosophy shows this unending endeavor as a consciousness trying to explain itself by being inside itself. Famous examples of this kind of thinking are e.g. Descartes, Kant, Fichte, Schelling, Hegel, and Husserl.
These examples show there exists no real way out.
PHILOSOPHY ENHANCED BY EMPIRICAL SCIENCES ? !
At a first glance it seems contradictory that Philosophy and Empirical Sciences could work ‘hand in hand’. But history has shown us, that this is to a certain extend possible; perhaps it is a major break through for the philosophical understanding of the world, especially also of men themselves.
Modern empirical sciences like Biology and Evolutionary Biology in cooperation with many other empirical disciplines have shown us, that the actual biological systems — including homo sapiens — are products of a so-called evolutionary process. And supported by modern empirical disciplines like Ethology, Psychology, Physiology, and Brain Sciences we could gain some first knowledge how our body works, how our brain, how our observable behavior is connected to this body and its brain.
While Philosopher like Kant or Hegel could investigate their own thinking only from the inside of their consciousness, the modern empirical sciences can investigate the human thinking from the outside. But until now there is a gap: We have no elaborated theory about the relationship between the inside of the consciousness and the outside knowledge about body and brain.
Thus what we need is a hybrid theory mapping the inside to the outside and revers. There are some first approaches headed under labels like Neuro-Psychology or Neuro-Phenomenology, but these are not yet completely clarified in their methodology in their relationship to Philosophy.
If one can describe to some extend the Phenomena of the consciousness from the inside as well as the working of the brain translated to its behavioral properties, then one can start first mappings like those, which have been used in this blog to establish the theory for the komega software.
SOCIOLOGY
Sociology is only one empirical discipline between many others. Although the theory of this blog is using many disciplines simultaneously Sociology is of special interest because it is that kind of empirical disciplines which is explicitly dealing with human societies with subsystems called cities.
The komega software which we are developing is understood here as enabling a system of interactions as part of a city understood as a system. If we understand Sociology as an empirical science according to some standard view of empirical science then it is possible to describe a city as an input-output system whose dynamics can become influenced by this komega software if citizens are using this software as part of their behavior.
STANDARD VIEW OF EMPIRICAL SCIENCE
Without some kind of a Standard View of Empirical Science it is not possible to design a discipline — e.g. Sociology — as an empirical discipline. Although it seems that everybody thinks that we have such a ‘Standard View of Empirical Science’, in the real world of today one must state that we do not have such a view. In the 80ties of the20th century it looked for some time as if we have it, but if you start searching the papers, books and schools today You will perceive a very fuzzy field called Philosophy of Science and within the so-called empirical sciences you will not found any coherent documented view of a ‘Standard View of Empirical Science’.
Because it is difficult to see how a process can look like which enables such a ‘Standard View of Empirical Science’ again, we will try to document the own assumptions for our theory as good as possible. Inevitably this will mostly have the character of only a ‘fragment’, an ‘incomplete outline’. Perhaps there will again be a time where sciences is back to have a commonly accepted view how science should look like to be called empirical science.
The context for this text is the whole block dedicated to the AAI (Actor-Actor Interaction) paradigm. The aim of this text is to give the big picture of all dimensions and components of this subject as it shows up during April 2019.
The first dimension introduced is the historical dimension, because this allows a first orientation in the course of events which lead to the actual situation. It starts with the early days of real computers in the thirties and forties of the 20 century.
The second dimension is the engineering dimension which describes the special view within which we are looking onto the overall topic of interactions between human persons and computers (or machines or technology or society). We are interested how to transform a given problem into a valuable solution in a methodological sound way called engineering.
The third dimension is the whole of society because engineering happens always as some process within a society. Society provides the resources which can be used and spends the preferences (values) what is understood as ‘valuable’, as ‘good’.
The fourth dimension is Philosophy as that kind of thinking which takes everything into account which can be thought and within thinking Philosophy clarifies conditions of thinking, possible tools of thinking and has to clarify when some symbolic expression becomes true.
HISTORY
In history we are looking back in the course of events. And this looking back is in a first step guided by the concepts of HCI (Human-Computer Interface) and HMI (Human-Machine Interaction).
It is an interesting phenomenon how the original focus of the interface between human persons and the early computers shifted to the more general picture of interaction because the computer as machine developed rapidly on account of the rapid development of the enabling hardware (HW) the enabling software (SW).
Within the general framework of hardware and software the so-called artificial intelligence (AI) developed first as a sub-topic on its own. Since the last 10 – 20 years it became in a way productive that it now seems to become a normal part of every kind of software. Software and smart software seem to be interchangeable. Thus the new wording of augmented or collective intelligence is emerging intending to bridge the possible gap between humans with their human intelligence and machine intelligence. There is some motivation from the side of society not to allow the impression that the smart (intelligent) machines will replace some day the humans. Instead one is propagating the vision of a new collective shape of intelligence where human and machine intelligence allows a symbiosis where each side gives hist best and receives a maximum in a win-win situation.
What is revealing about the actual situation is the fact that the mainstream is always talking about intelligence but not seriously about learning! Intelligence is by its roots a static concept representing some capabilities at a certain point of time, while learning is the more general dynamic concept that a system can change its behavior depending from actual external stimuli as well as internal states. And such a change includes real changes of some of its internal states. Intelligence does not communicate this dynamics! The most demanding aspect of learning is the need for preferences. Without preferences learning is impossible. Today machine learning is a very weak example of learning because the question of preferences is not a real topic there. One assumes that some reward is available, but one does not really investigate this topic. The rare research trying to do this job is stating that there is not the faintest idea around how a general continuous learning could happen. Human society is of no help for this problem while human societies have a clash of many, often opposite, values, and they have no commonly accepted view how to improve this situation.
ENGINEERING
Engineering is the art and the science to transform a given problem into a valuable and working solution. What is valuable decides the surrounding enabling society and this judgment can change during the course of time. Whether some solution is judged to be working can change during the course of time too but the criteria used for this judgment are more stable because of their adherence to concrete capabilities of technical solutions.
While engineering was and is always a kind of an art and needs such aspects like creativity, innovation, intuition etc. it is also and as far as possible a procedure driven by defined methods how to do things, and these methods are as far as possible backed up by scientific theories. The real engineer therefore synthesizes art, technology and science in a unique way which can not completely be learned in the schools.
In the past as well as in the present engineering has to happen in teams of many, often many thousands or even more, people which coordinate their brains by communication which enables in the individual brains some kind of understanding, of emerging world pictures, which in turn guide the perception, the decisions, and the concrete behavior of everybody. And these cognitive processes are embedded — in every individual team member — in mixtures of desires, emotions, as well as motivations, which can support the cognitive processes or obstruct them. Therefore an optimal result can only be reached if the communication serves all necessary cognitive processes and the interactions between the team members enable the necessary constructive desires, emotions, and motivations.
If an engineering process is done by a small group of dedicated experts — usually triggered by the given problem of an individual stakeholder — this can work well for many situations. It has the flavor of a so-called top-down approach. If the engineering deals with states of affairs where different kinds of people, citizens of some town etc. are affected by the results of such a process, the restriction to a small group of experts can become highly counterproductive. In those cases of a widespread interest it seems promising to include representatives of all the involved persons into the executing team to recognize their experiences and their kinds of preferences. This has to be done in a way which is understandable and appreciative, showing esteem for the others. This manner of extending the team of usual experts by situative experts can be termed bottom-up approach. In this usage of the term bottom-up this is not the opposite to top-down but is reflecting the extend in which members of a society are included insofar they are affected by the results of a process.
SOCIETY
Societies in the past and the present occur in a great variety of value systems, organizational structures, systems of power etc. Engineering processes within a society are depending completely on the available resources of a society and of its value systems.
The population dynamics, the needs and wishes of the people, the real territories, the climate, housing, traffic, and many different things are constantly producing demands to be solved if life shall be able and continue during the course of time.
The self-understanding and the self-management of societies is crucial for their ability to used engineering to improve life. This deserves communication and education to a sufficient extend, appropriate public rules of management, otherwise the necessary understanding and the freedom to act is lacking to use engineering in the right way.
PHILOSOPHY
Without communication no common constructive process can happen. Communication happens according to many implicit rules compressed in the formula who when can speak how about what with whom etc. Communication enables cognitive processes of for instance understanding, explanations, lines of arguments. Especially important for survival is the ability to make true descriptions and the ability to decide whether a statement is true or not. Without this basic ability communication will break down, coordination will break down, life will break down.
The basic discipline to clarify the rules and conditions of true communication, of cognition in general, is called Philosophy. All the more modern empirical disciplines are specializations of the general scope of Philosophy and it is Philosophy which integrates all the special disciplines in one, coherent framework (this is the ideal; actually we are far from this ideal).
Thus to describe the process of engineering driven by different kinds of actors which are coordinating themselves by communication is primarily the task of philosophy with all their sub-disciplines.
Thus some of the topics of Philosophy are language, text, theory, verification of a theory, functions within theories as algorithms, computation in general, inferences of true statements from given theories, and the like.
In this text I apply Philosophy as far as necessary. Especially I am introducing a new process model extending the classical systems engineering approach by including the driving actors explicitly in the formal representation of the process. Learning machines are included as standard tools to improve human thinking and communication. You can name this Augmented Social Learning Systems (ASLS). Compared to the wording Augmented Intelligence (AI) (as used for instance by the IBM marketing) the ASLS concept stresses that the primary point of reference are the biological systems which created and create machine intelligence as a new tool to enhance biological intelligence as part of biological learning systems. Compared to the wording Collective Intelligence (CI) (as propagated by the MIT, especially by Thomas W.Malone and colleagues) the spirit of the CI concept seems to be similar, but perhaps only a weak similarity.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the fourth chapter dealing with the epistemology of actors within an AAI analysis process.
EPISTEMOLOGY AND THE EMPIRICAL SCIENCES
Epistemology is a sub-discipline of general philosophy. While a special discipline in empirical science is defined by a certain sub-set of the real world RW by empirical measurement methods generating empirical data which can be interpreted by a formalized theory, philosophy is not restricted to a sub-field of the real world. This is important because an empirical discipline has no methods to define itself. Chemistry e.g. can define by which kinds of measurement it is gaining empirical data and it can offer different kinds of formal theories to interpret these data including inferences to forecast certain reactions given certain configurations of matters, but chemistry is not able to explain the way how a chemist is thinking, how the language works which a chemist is using etc. Thus empirical science presupposes a general framework of bodies, sensors, brains, languages etc. to be able to do a very specialized — but as such highly important — job. One can define ‘philosophy’ then as that kind of activity which tries to clarify all these conditions which are necessary to do science as well as how cognition works in the general case.
Given this one can imagine that philosophy is in principle a nearly ‘infinite’ task. To get not lost in this conceptual infinity it is recommended to start with concrete processes of communications which are oriented to generate those kinds of texts which can be shown as ‘related to parts of the empirical world’ in a decidable way. This kind of texts is here called ’empirically sound’ or ’empirically true’. It is to suppose that there will be texts for which it seems to be clear that they are empirically sound, others will appear ‘fuzzy’ for such a criterion, others even will appear without any direct relation to empirical soundness.
In empirical sciences one is using so-called empirical measurement procedures as benchmarks to decided whether one has empirical data or not, and it is commonly assumed that every ‘normal observer’ can use these data as every other ‘normal observer’. But because individual, single data have nearly no meaning on their own one needs relations, sets of relations (models) and even more complete theories, to integrate the data in a context, which allows some interpretation and some inferences for forecasting. But these relations, models, or theories can not directly be inferred from the real world. They have to be created by the observers as ‘working hypotheses’ which can fit with the data or not. And these constructions are grounded in highly complex cognitive processes which follow their own built-in rules and which are mostly not conscious. ‘Cognitive processes’ in biological systems, especially in human person, are completely generated by a brain and constitute therefore a ‘virtual world’ on their own. This cognitive virtual world is not the result of a 1-to-1 mapping from the real world into the brain states. This becomes important in that moment where the brain is mapping this virtual cognitive world into some symbolic language L. While the symbols of a language (sounds or written signs or …) as such have no meaning the brain enables a ‘coding’, a ‘mapping’ from symbolic expressions into different states of the brain. In the light’ of such encodings the symbolic expressions have some meaning. Besides the fact that different observers can have different encodings it is always an open question whether the encoded meaning of the virtual cognitive space has something to do with some part of the empirical reality. Empirical data generated by empirical measurement procedures can help to coordinate the virtual cognitive states of different observers with each other, but this coordination is not an automatic process. Empirically sound language expressions are difficult to get and therefore of a high value for the survival of mankind. To generate empirically sound formal theories is even more demanding and until today there exists no commonly accepted concept of the right format of an empirically sound theory. In an era which calls itself ‘scientific’ this is a very strange fact.
EPISTEMOLOGY OF THE AAI-EXPERTS
Applying these general considerations to the AAI experts trying to construct an actor story to describe at least one possible path from a start state to a goal state, one can pick up the different languages the AAI experts are using and asking back under which conditions these languages have some ‘meaning’ and under which conditions these meanings can be called ’empirically sound’?
In this book three different ‘modes’ of an actor story will be distinguished:
A textual mode using some ordinary everyday language, thus using spoken language (stored in an audio file) or written language as a text.
A pictorial mode using a ‘language of pictures’, possibly enhanced by fragments of texts.
A mathematical mode using graphical presentations of ‘graphs’ enhanced by symbolic expressions (text) and symbolic expressions only.
For every mode it has to be shown how an AAI expert can generate an actor story out of the virtual cognitive world of his brain and how it is possible to decided the empirical soundness of the actor story.
Enhanced version of the ‘Better World Project’ Idea by making explicit the engineering part touching all other aspects
The online-book project published on the uffmm.org website has to be seen within a bigger idea which can be named ‘The better world project’.
As outlined in the figure above you can see that the AAIwSE theory is the nucleus of a project which intends to enable a global learning space which connects individual persons as well as schools, universities, cities as well as companies, and even more if wanted.
There are other ideas around using the concept ‘better world’, butt these other concepts are targeting other subjects. In this view here the engineering perspective is laying the ground to build new more effective systems to enhance all aspects of life.
As you already can detect in the AAAIwSE theory published so far there exists a new and enlarged vision of the acting persons, the engineers as the great artists of the real world. Taking this view seriously there will be a need for a new kind of spirituality too which is enabling the acting persons to do all this with a vital interest in the future of life in the universe.
Actually the following websites are directly involved in the ‘Better World Project Idea’: this site ‘uffmm.org’ (in English) and (in German) ‘cognitiveagent.org‘ and ‘Kommunalpolitik & eGaming‘. The last link points to an official project of the Frankfurt University of Applied Sciences (FRA-UAS) which will apply the AAI-Methods to all communities in Germany (about 11.000).
Update 4.July 2018 (Chapter 4 Actor Model; improving the terminology of environments with actors, actors as input-output systems, basic and real interface, a first typology of input-output systems…)
Update 20.July 2018 (Disentanglement of chapter ‘Simulation & Verification’ into two independent chapters; corrections in the chapter ‘Introduction’; corrections in chapter ‘AAI Analysis’; extracting ‘Simulation’ from chapter ‘Actor Story’ to new chapter ‘Simulation’; New chapter ‘Simulation’; Rewriting of chapter ‘Looking Forward’)
Update 22.July 2018 (Rewriting the beginning of the chapter ‘Actor Story (AS)’, not completed; converting chapter ‘AS+AM Summary’ to ‘AS and AM Philosophy’, not completed)
Update 23.July 2018 (Attaching a new chapter with a Case Study illustrating an actor story (AS). This case study is still unfinished. It is a case study of a real project!)
Update 8.August 2018 (Modifying chapter AS as Text, Comic, Graph; especially section about the textual mode and the pictorial mode; first sketch for a mapping from the textual mode into the pictorial mode)
Update 9.August 2018 (Modification of the section ‘Mathematical Actor Story (MAS) in chapter 4).
Update 11.August 2018 (Improving chapter 3 ‘Actor Story; nearly complete rewriting of chapter 4 ‘AS as text, comic, graph’.)
Update 13.August 2018 (I am still catched by the chapters 3+4. In chapter the cognitive structure of the actors has been further enhanced; in chapter 4 a complete example of a mathematical actor story could now been attached.)
Update 14.August 2018 (minor corrections to chapter 4 + 5; change-statements define for each state individual combinatorial spaces (a little bit like a quantum state); whether and how these spaces will be concretized/ realized depends completely from the participating actors)
Update 15.August 2018 (Canceled the appendix with the case study stub and replaced it with an overview for a supporting software tool which is needed for the real usage of this theory. At the moment it is open who will write the software.)
Update 2.October 2018 (Configuring the whole book now with 3 parts: I. Theory, II. Application, III. Software. Gerd has his focus on part I, Zeynep will focus on part II and ‘somebody’ will focus on part III (in the worst case we will — nevertheless — have a minimal version :-)). For a first quick overview about everything read the ‘Preface’ and the ‘Introduction’.
Update 4.November 2018 (Rewriting the Introduction (and some minor corrections in the Preface). The idea of the rewriting was to address all the topics which will be discussed in the book and pointing out to the logical connections between them. This induces some wrong links in the following chapters, which are not yet updated. Some chapters are yet completely missing. But to improve the clearness of the focus and the logical inter-dependencies helps to elaborate the missing texts a lot. Another change is the wording of the title. Until now it is difficult to find a title which is exactly matching the content. The new proposal shows the focus ‘AAI’ but lists the keywords of the main topics within AAA analysis because these topics are usually not necessarily associated with AAI.)
ACTOR-ACTOR INTERACTION [AAI]. An Actor Centered Approach to Problem Solving. Combining Engineering and Philosophy
by
GERD DOEBEN-HENISCH in cooperation with LOUWRENCE ERASMUS, ZEYNEP TUNCER
PRE-VIEW: NEW EXPANDED AAI THEORY 23.January 2019: Outline of the new expanded AAI Paradigm. Before re-writing the main text with these ideas the new advanced AAI theory will first be tested during the summer 2019 within a lecture with student teams as well as in several workshops outside the Frankfurt University of Applied Sciences with members of different institutions.
1 History: From HCI to AAI …
2 Different Views …
3 Philosophy of the AAI-Expert …
4 Problem (Document) …
5 Check for Analysis …
6 AAI-Analysis …
6.1 Actor Story (AS) . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.1 Textual Actor Story (TAS) . . . . . . . . . . . . . . .
6.1.2 Pictorial Actor Story (PAT) . . . . . . . . . . . . . .
6.1.3 Mathematical Actor Story (MAS) . . . . . . . . . . .
6.1.4 Simulated Actor Story (SAS) . . . . . . . . . . . . .
6.1.5 Task Induced Actor Requirements (TAR) . . . . . . .
6.1.6 Actor Induced Actor Requirements (UAR) . . . . . .
6.1.7 Interface-Requirements and Interface-Design . . . .
6.2 Actor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2.1 Actor and Actor Story . . . . . . . . . . . . . . . . .
6.2.2 Actor Model . . . . . . . . . . . . . . . . . . . . . .
6.2.3 Actor as Input-Output System . . . . . . . . . . . .
6.2.4 Learning Input-Output Systems . . . . . . . . . . . .
6.2.5 General AM . . . . . . . . . . . . . . . . . . . . . .
6.2.6 Sound Functions . . . . . . . . . . . . . . . . . . .
6.2.7 Special AM . . . . . . . . . . . . . . . . . . . . . .
6.2.8 Hypothetical Model of a User – The GOMS Paradigm
6.2.9 Example: An Electronically Locked Door . . . . . . .
6.2.10 A GOMS Model Example . . . . . . . . . . . . . . .
6.2.11 Further Extensions . . . . . . . . . . . . . . . . . .
6.2.12 Design Principles; Interface Design . . . . . . . . .
6.3 Simulation of Actor Models (AMs) within an Actor Story (AS) .
6.4 Assistive Actor-Demonstrator . . . . . . . . . . . . . . . . . .
6.5 Approaching an Optimum Result . . . . .
7 What Comes Next: The Real System
7.1 Logical Design, Implementation, Validation . . . .
7.2 Conceptual Gap In Systems Engineering? . . .
8 The AASE-Paradigm …
References
Abstract
This text is based on the the paper “AAI – Actor-Actor Interaction. A Philosophy of Science View” from 3.Oct.2017 and version 11 of the paper “AAI – Actor-Actor Interaction. An Example Template” and it transforms these views in the new paradigm ‘Actor- Actor Systems Engineering’ understood as a theory as well as a paradigm for and infinite set of applications. In analogy to the slogan ’Object-Oriented Software Engineering (OO SWE)’ one can understand the new acronym AASE as a systems engineering approach where the actor-actor interactions are the base concepts for the whole engineering process. Furthermore it is a clear intention to view the topic AASE explicitly from the point of view of a theory (as understood in Philosophy of Science) as well as from the point of view of possible applications (as understood in systems engineering). Thus the classical term of Human-Machine Interaction (HMI) or even the older Human-Computer Interaction (HCI) is now embedded within the new AASE approach. The same holds for the fuzzy discipline of Artificial Intelligence (AI) or the subset of AI called Machine Learning (ML). Although the AASE-approach is completely in its beginning one can already see how powerful this new conceptual framework is.
1 Problem ….. 3
2 AAI-Check ….. 3
3 Actor-Story (AS) ….. 3
3.1 AS as a Text . . . . . . . . . . . . . . . . . .3
3.2 Translation of a Textual AS into a Formal AS …… 4
3.3 AS as a Formal Expression . . . . . . . . . .4
3.4 Translation of a Formal AS into a Pictorial AS… 5
4 Actor-Model (AM) ….. 5
4.1 AM for the User as a Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6
4.2 AM for the System as a Text . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
5 Combined AS and AM as a Text ….. 6
5.1 AM as an Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
6 Simulation ….. 7
6.1 Simulating the AS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
6.2 Simulating the AM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7
6.3 Simulating AS with AM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
7 Appendix: Formalisms ….. 8
7.1 Set of Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9
7.2 Predicate Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
8 Appendix: The Meaning of Expressions …11
8.1 States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.2 Changes by Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Abstract
Following the general concepts of the paper ’AAI – Actor-Actor Interaction. A Philosophy of Science View’ from 3.Oct.2017 this paper illustrates a simple application where the difference as well as the
interaction between an actor story and several actor models is shown. The details of interface-design as well as the usability-testing are not part of this example.(This example replaces the paper with the title
’AAI – Case Study Actor Story with Actor Model. Simple Grid-Environment’ from 15.Nov.2017). One special point is the meaning of the formal expressions of the actor story.
Clearly, one can debate whether a ‘toy-example’ makes sens, but the complexity of the concepts in this AAI-approach is to great to illustrate these in the beginning with a realistic example without loosing the idea. The author of the paper has tried many — also very advanced — versions in the last years and this is the first time that he himself has the feeling that at least the idea is now clear enough. And from teaching students it is very clear, if you cannot explain an idea in a toy-example you never will be able to apply it to real big problems…
On the cover page of this blog you find a first general view on the subject matter of an integrated engineering approach for the future. Here we give a short description of the main idea of the analysis phase of systems engineering how this will be realized within the actor-actor interaction paradigm as described in this text.
INTRODUCTION
Overview of the analysis phase of systems engineering as realized within an actor-actor interaction paradigm
As you can see in figure Nr.1 there are the following main topics within the Actor-Actor Interaction (AAI) paradigm as used in this text (Comment: The more traditional formula is known as Human-Machine Interaction (HMI)):
Triggered by a problem document D_p from the problem phase (P) of the engineering process the AAI-experts have to analyze, what are the potential requirements following from this document, all the time also communicating with the stakeholder to keep in touch with the hidden intentions of the stakeholder.
The idea is to identify at least one task (T) with at least one goal state (G) which shall be arrived after running a task.
A task is assumed to represent a sequence of states (at least a start state and a goal state) which can have more than one option in every state, not excluding repetitions.
Every task presupposes some context (C) which gives the environment for the task.
The number of tasks and their length is in principle not limited, but their can be certain constraints (CS) given which have to be fulfilled required by the stakeholder or by some other important rules/ laws. Such constraints will probably limit the number of tasks as well as their length.
Actor Story
Every task as a sequence of states can be viewed as a story which describes a process. A story is a text (TXT) which is static and hides the implicit meaning in the brains of the participating actors. Only if an actor has some (learned) understanding of the used language then the actor is able to translate the perceptions of the process in an appropriate text and vice versa the text into corresponding perceptions or equivalently ‘thoughts’ representing the perceptions.
In this text it is assumed that a story is describing only the observable behavior of the participating actors, not their possible internal states (IS). For to describe the internal states (IS) it is further assumed that one describes the internal states in a new text called actor model (AM). The usual story is called an actor story (AS). Thus the actor story (AS) is the environment for the actor models (AM).
In this text three main modes of actor stories are distinguished:
An actor story written in some everyday language L_0 called AS_L0 .
A translation of the everyday language L_0 into a mathematical language L_math which can represent graphs, called AS_Lmath.
A translation of the hidden meaning which resides in the brains of the AAI-experts into a pictorial language L_pict (like a comic strip), called AS_Lpict.
To make the relationship between the graph-version AS_Lmath and the pictorial version AS_Lpict visible one needs an explicit mapping Int from one version into the other one, like: Int : AS_Lmath <—> AS_Lpict. This mapping Int works like a lexicon from one language into another one.
From a philosophy of science point of view one has to consider that the different kinds of actor stories have a meaning which is rooted in the intended processes assumed to be necessary for the realization of the different tasks. The processes as such are dynamic, but the stories as such are static. Thus a stakeholder (SH) or an AAI-expert who wants to get some understanding of the intended processes has to rely on his internal brain simulations associated with the meaning of these stories. Because every actor has its own internal simulation which can not be perceived from the other actors there is some probability that the simulations of the different actors can be different. This can cause misunderstandings, errors, and frustrations.(Comment: This problem has been discussed in [DHW07])
One remedy to minimize such errors is the construction of automata (AT) derived from the math mode AS_Lmath of the actor stories. Because the math mode represents a graph one can derive Der from this version directly (and automatically) the description of an automaton which can completely simulate the actor story, thus one can assume Der(AS_Lmath) = AT_AS_Lmath.
But, from the point of view of Philosophy of science this derived automaton AT_AS_Lmath is still only a static text. This text describes the potential behavior of an automaton AT. Taking a real computer (COMP) one can feed this real computer with the description of the automaton AT AT_AS_Lmath and make the real computer behave like the described automaton. If we did this then we have a real simulation (SIM) of the theoretical behavior of the theoretical automaton AT realized by the real computer COMP. Thus we have SIM = COMP(AT_AS_Lmath). (Comment: These ideas have been discussed in [EDH11].)
Such a real simulation is dynamic and visible for everybody. All participating actors can see the same simulation and if there is some deviation from the intention of the stakeholder then this can become perceivable for everybody immediately.
Actor Model
As mentioned above the actor story (AS) describes only the observable behavior of some actor, but not possible internal states (IS) which could be responsible for the observable behavior.
If necessary it is possible to define for every actor an individual actor model; indeed one can define more than one model to explore the possibilities of different internal structures to enable a certain behavior.
The general pattern of actor models follows in this text the concept of input-output systems (IOSYS), which are in principle able to learn. What the term ‘learning’ designates concretely will be explained in later sections. The same holds of the term ‘intelligent’ and ‘intelligence’.
The basic assumptions about input-output systems used here reads a follows:
Def: Input-Output System (IOSYS)
IOSYS(x) iff x=< I, O, IS, phi>
phi : I x IS —> IS x O
I := Input
O := Output
IS := Internal
As in the case of the actor story (AS) the primary descriptions of actor models (AM) are static texts. To make the hidden meanings of these descriptions ‘explicit’, ‘visible’ one has again to convert the static texts into descriptions of automata, which can be feed into real computers which in turn then simulate the behavior of these theoretical automata as a real process.
Combining the real simulation of an actor story with the real simulations of all the participating actors described in the actor models can show a dynamic, impressive process which is full visible to all collaborating stakeholders and AAI-experts.
Testing
Having all actor stories and actor models at hand, ideally implemented as real simulations, one has to test the interaction of the elaborated actors with real actors, which are intended to work within these explorative stories and models. This is done by actor tests (former: usability tests) where (i) real actors are confronted with real tasks and have to perform in the intended way; (ii) real actors are interviewed with questionnaires about their subjective feelings during their task completion.
Every such test will yield some new insights how to change the settings a bit to gain eventually some improvements. Repeating these cycles of designing, testing, and modifying can generate a finite set of test-results T where possibly one subset is the ‘best’ compared to all the others. This can give some security that this design is probably the ‘relative best design’ with regards to T.
[DHW07] G. Doeben-Henisch and M. Wagner. Validation within safety critical systems engineering from a computation semiotics point of view.
Proceedings of the IEEE Africon2007 Conference, pages Pages: 1 – 7, 2007.
[EDH11] Louwrence Erasmus and Gerd Doeben-Henisch. A theory of the
system engineering process. In ISEM 2011 International Conference. IEEE, 2011.
RESTART OF UFFMM AS SCIENTIFIC WORKPLACE.
For the Integrated Engineering of the Future (SW4IEF)
Campaining the Actor-Actor Systems Engineering (AASE) paradigm
Last Update June-22, 2018, 15:32 CET. See below: Case Studies — Templates – AASE Micro Edition – and Scheduling 2018 —
RESTART
This is a complete new restart of the old uffmm-site. It is intended as a working place for those people who are interested in an integrated engineering of the future.
SYSTEMS ENGINEERING
A widely known and useful concept for a general approach to the engineering of problems is systems engineering (SE).
Open for nearly every kind of a possible problem does a systems engineering process (SEP) organize the process how to analyze the problem, and turn this analysis into a possible design for a solution. This proposed solution will be examined by important criteria and, if it reaches an optimal version, it will be implemented as a real working system. After final evaluations this solution will start its carrier in the real world.
PHILOSOPHY OF SCIENCE
In a meta-scientific point of view the systems engineering process can become itself the object of an analysis. This is usually done by a discipline called philosophy of science (PoS). Philosophy of science is asking, e.g., what the ‘ingredients’ of an systems-engineering process are, or how these ingredients do interact? How can such a process ‘fail’? ‘How can such a process be optimized’? Therefore a philosophy of science perspective can help to make a systems engineering process more transparent and thereby supports an optimization of these processes.
A core idea of the philosophy of science perspective followed in this text is the assumption, that a systems engineering process is primarily based on different kinds of actors (AC) whose interactions enable and direct the whole process. These assumptions are also valid in that case, where the actors are not any more only biological systems like human persons and non-biological systems called machines, but also in that case where the traditional machines (M) are increasingly replaced by ‘intelligent machines (IM)‘. Therefore the well know paradigm of human-machine interaction (HMI) — or earlier ‘human-computer interaction (HCI)’ will be replaced in this text by the new paradigm of Actor-Actor Interaction (AAI). In this new version the main perspective is not the difference of man on one side and machines on the other but the kind of interactions between actors of all kind which are necessary and possible.
INTELLIGENT MACHINES
The concept of intelligent machines (IM) is understood here as a special case of the general Actor (A) concept which includes as other sub-cases biological systems, predominantly humans as instantiations of the species Homo Sapiens. While until today the question of biological intelligence and machine intelligence is usually treated separately and differently it is intended in this text to use one general concept of intelligence for all actors. This allows then more direct comparisons and evaluations. Whether biological actors are in some sense better than the non-biological actors or vice versa can seriously only be discussed when the used concept of intelligence is the same.
ACTOR STORY AND ACTOR MODELS
And, as it will be explained in the following sections, the used paradigm of actor-actor interactions uses the two main concepts of actor story (AS) as well as actor model (AM). Actor models are embedded in the actor stories. Whether an actor model describes biological or non-biological actors does not matter. Independent of the inner structures of an actor model (which can be completely different) the actor story is always completely described in terms of observable behavior which are the same for all kinds of actors (Comment: The major scientific disciplines for the analysis of behavior are biology, psychology, and sociology).
AASE PARADIGM
In analogy to the so-called ‘Object-Oriented (OO) approach in Software-Engineering (SWE)’ we campaign here the ‘Actor-Actor (AA) Systems Engineering (SE)’ approach. This takes the systems Engineering approach as a base concepts and re-works the whole framework from the point of view of the actor-actor paradigm. AASE is seen here as a theory as well as an domain of applications.
Figure: Ontologies of the AASE paradigm
To understand the different perspectives of the used theory it can help to the figure ‘AASE-Paradigm Ontologies’. Within the systems engineering process (SEP) we have AAI-experts as acting actors. To describe these we need a ‘meta-level’ realized by a ‘philosophy of the actor’. The AAI-experts themselves are elaborating within an AAI-analysis an actor story (AS) as framework for different kinds of intended actors. To describe the inner structures of these intended actors one needs different kinds of ‘actor models’. The domain of actor-model structures overlaps with the domain of ‘machine learning (ML)’ and with ‘artificial intelligence (AI)’.
SOFTWARE
What will be described and developed separated from these theoretical considerations is an appropriate software environment which allows the construction of solutions within the AASE approach including e.g. the construction of intelligent machines too. This software environment is called in this text emerging-mind lab (EML) and it will be another public blog as well.
THEORY MICRO EDITION & CASE STUDIES
How we proceed
Because the overall framework of the intended integrated theory is too large to write it down in one condensed text with all the necessary illustrating examples we decided in Dec 2017 to follow a bottom-up approach by writing primarily case studies from different fields. While doing this we can introduce stepwise the general theory by developing a Micro Edition of the Theory in parallel to the case studies. Because the Theory Micro Edition has gained a sufficient minimal completeness already in April 2018 we do not need anymore a separate template for case studies. We will use the Theory Micro Edition as ‘template’ instead.
To keep the case studies readable as far as possible all needed mathematical concepts and formulas will be explained in a separate appendix section which is central for all case studies. This allows an evolutionary increase in the formal apparatus used for the integrated theory.
Here you can find the actual version of the theory which will continuously be updated and extended by related topics.
At the end of the text you find a list of ToDos where everybody is invited to collaborate. The main editor is Gerd Doeben-Henisch deciding whether the proposal fits into the final text or not.
This sections describes main developments in the history from HCI to AAI.
SCHEDULE 2018
The Milestone for a first outline in a book format has been reached June-22, 2018. The milestone for a first final version is scheduled for October-4, 2018.
Integrating Engineering and the Human Factor (info@uffmm.org) eJournal uffmm.org ISSN 2567-6458
