Category Archives: machine

Collective human-machine intelligence and text generation. A transdisciplinary analysis.

Author: Gerd Doeben-Henisch

Email: info@uffmm.org

Time: Sept 25, 2023 – Oct 3, 2023

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:

  1. an empirical theory is basically a text, written in a language that all participants understand.
  2. 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).
  3. 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.
  4. 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.
  5. 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:

  1. I cannot read a book in the same way a human does, meaning I don’t interpret or comprehend in the same manner.
  2. My knowledge is up to September 2021, which means that I might not be aware of events, advancements, or publications after that point.
  3. 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.
  4. I don’t cite individual sources in my responses. I provide information based on the patterns recognized during my training.
  5. Responses I give can vary slightly even if the same question is posed multiple times.
  6. I can’t directly attribute my statements to specific, individual sources.
  7. 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:

  1. a sustainable empirical theory contains an empirical theory as its core.
    1. 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, …).
    2. 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.

WHY THE WORLD NEEDS ANTHROPOLOGISTS – Review Part 1

eJournal: uffmm.org, ISSN 2567-6458, 1.December  2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

ANTHROPOLOGY AND ENGINEERING

The starting point of view in this blog has been and still is the point of engineering, especially the perspective of man-machine interface [MMI], later as Man-Machine Interaction, then  accompanied by   human-computer interaction [HCI] or human-machine interaction [HMI]. While MMI often is discussed in isolation, not as part of engineering, this blog emphasizes a point of view where MMI is understood as an integrated part of systems engineering. The past years have shown, that this integration makes a great difference in the overall layout as well as in the details of the used methods. This integration widened the scope of MMI to the context of engineering in a way which teared down many artificial boundaries in dealing with the subject of MMI. The analysis part of MMI can take into account not only the intended users and a limited set of tasks required for the usage of a system but it can extend the scope to the different kinds of contexts of the intended users as well as the intended service/product as such: cultural patterns, sustainable perspectives, climate relevance, political implications, and more. This triggers the question, whether there are other established scientific disciplines which are sharing this scope with MMI. Traditionally experimental and cognitive psychology has always played an important role as part of the MMI analysis.  Different special disciplines like physiology or neuro-psychology, linguistics, phonetics etc. have played some role too. More recently culture and society have been brought more into the focus of MMI. What about sociology? What about anthropology? The following text discusses a possible role of anthropology in the light of the recent book Why The World Needs Anthropologists?

INTRODUCTION AND CONCLUSION

This review has the addendum ‘Part 1’ pointing to the fact, that this text does not deal with the whole book first, but only with some parts, the introduction and the conclusion.

An Introduction

The introduction of the book is asking, why does the world needs anthropologists?, and the main pattern of the introduction looks back to the old picture of anthropology, and then seeks to identify, what could/is the new paradigm which should be followed.

The roots of anthropology are located in the colonial activities of the British Empire as well as in the federal activities of the USA, which both had a strong bias to serve the political power more than to evolve a really free science. And an enduring gap between the more theoretical anthropology and an applied one is thematised although there existed always  a strong inter-dependency  between both.

To leave the close connection with primarily  governmental interests and to see the relation  between the theory and the different Applications  more positive than negative anthropology is understood  as challenged to rebrand its appearance in the public and in their own practice.

The most vital forces for such a rebranding seem to be rooted in more engagements in societal problems of public interests and thereby challenging the theory to widen their concept and methods.

Besides the classical methods of anthropology (cultural relativism, ethnography, comparison, and contextual understanding)  anthropology has to show that it can make sense beyond pure data, deciphering ambiguity, complexity, and ambivalence, helping with  diversity, investigating the interface between culture, technology, and environment.

What Is Left Out

After the introduction the main chapters of the book  are left out in this text  until later. The chapters in the book are giving examples to the questions, why the world needs anthropology, what have been the motivations for active anthropologists to become one, how they have applied anthropology, and which five tips they would give for practicing and theorizing.

Conclusion

In the conclusion of the book not the five questions are the guiding principle but ‘five axis that matter greatly’, and these five axis are circumscribed as (i) navigate the ethics of change; (ii) own-it in the sense, that an anthropologist should have a self-esteem for his/ her/ x  profession and can co-create it with others; (iii) expand the skill-set; (iv) collaborate, co-create and study-up; (v) recommend as being advisors and consultants.

The stronger commitment with actual societal problems leads anthropology at the crossroads of many processes which require new views, new methods. To gain new knowledge and to do a new practice is  not always accompanied by  known ethical schemata. Doing this induces  ethical questions which have not been known before in this way.  While a new practice is challenging the old knowledge and induces a pressure for change, new versions of knowing can  trigger new forms of practice as well. Theory and application are a dynamic pair where each part learns from the other.

The long-lasting preference of academic anthropology, thinking predominantly  in the mind-setting of   white-western-man, is  more and more resolved  by extending anthropology from academia to application, from man into the diversity of genders, from western culture into all the other cultures, from single persons to assemblies of diverse gatherings living an ongoing discourse with a growing publicity.

This widening of anthropological subjects and methods calls naturally for more interdisciplinarity, transdisciplinarity, and of a constructive attitude  which looks ahead to  possible futures of processes.

Close to this are expressions like collaboration and co-creation with others. In the theory dimension this is reflected by multiperspectivity and a holistic view. In societal development processes — like urban planning — there are different driving forces acting working top-down or acting working bottom-up.

Recommending solutions based on anthropological thinking ending in a yes or no, can be of help and can be necessary because real world processes can not only wait of final answers (which are often not realistic), they need again and again decisions to proceed now.

REFLECTIONS FOLLOWING THE INTRODUCTION AND THE CONCLUSION

The just referred texts making a fresh impression of a discipline in a dynamic movement.

General Knowledge Architecture

For the point of view of MMI (Man-Machine Interface, later HMI Human-Machine Interaction, in my theory extended to DAAI Distributed Actor-Actor Interaction) embedded in systems engineering with an openness for the whole context of society and culture arises the question whether such a dynamic anthropology can be of help.

To clarify this question let us have a short look to the general architecture of knowledge.

Within the everyday world philosophy can be understood as the most general point of view of knowing  and thinking.  Traditionally logic and mathematics can be understood as part of philosophy although today this has been changed. But there are no real reasons for this departure: logic and mathematics are not empirical sciences and they are not engineering.

Empirical science can be understood as specialized extension of philosophical thinking with identifiable characteristics which allow to  differentiate to some extend different  disciplines.  Traditionally all the different disciplines of empirical science have a more theoretical part and a more applied part. But systematically they depend from each other. A theory is only an empirical one, if there exists a clear relationship to the everyday world, and certain aspects of the everyday world are only theoretical entities (data) if there exists a relationship to an explicit theory which gives a formal explanation.

Asking for a  systematic place for engineering it is often said, that it belongs to the applied dimension of empirical science.  But engineering has realized processes, buildings, machines long before there was a scientific framework for to do this, and engineering uses in its engineering processes lots of knowledge which is not part of science. On the other side, yes, engineering is using scientific knowledge as far as it is usable and it is also giving back many questions to science which are not yet solved sufficiently. Therefore it is sound to locate engineering besides science, but   being  part of philosophy dealing with the practical dimensions of life.

What About Anthropology?

While philosophy (with logic and mathematics) is ‘on top’ of empirical science and engineering, it is an interesting question where to place anthropology?

While empirical science as well as engineering are inheriting all what philosophy provides remains the question whether  anthropology is more an empirical science or more engineering or some kind of a hybrid system with roots in empirical science as well as in engineering?

Looking back into history it could arise the impression that anthropology is more a kind of an empirical science with strong roots in academia, but doing  fieldwork to feed the theories.

Looking to the new book it could support the image that anthropology should be more like engineering: identifying  open problems in society and trying to transform these problems — like engineers — into satisfying solutions, at least on the level of counseling.

Because in our societies the universities have traditionally a higher esteem then the engineers — although the engineers  are all  trained by highly demanding university courses — it could be a bias in the thinking of  anthropologist not to think of their discipline   as engineering.

If one looks to the real world than everything which  makes human societies livable is realized by engineers. Yes, without science many of the today solutions wouldn’t be possible, but no single scientific theory has ever enabled directly some practical stuff.  And without the engineers there would not exist any of the modern machines used for measurements and experiments for science. Thus both are intimately  interrelated: science inspires engineering and engineering inspires and enables science, but both are genuinely different and science and engineering play their own fundamental role.

Thus if I am reading the new book as engineer (attention: I am also a philosopher and I am trained in the Humanities too!) then I think there are more arguments to understand anthropology  as engineering than as a pure empirical science. In the light of my distributed actor-actor interaction paradigm, which is a ‘spinoff’ of engineering and societal thinking it seems very ‘naturally’ to think of anthropology as a kind of social engineering.

Let us discuss both perspectives a bit more, thereby not excluding the hybrid version.

1) Anthropology as Engineering

The basic idea of engineering is to enable a change process which is completely transparent in all respects: Why, Who, Where, When, How etc. The process starts with explicit preferences turning some known reality into a problem on account of some visions which have been imagined and which have become ranked higher than the given known reality. And then the engineers try to organized an appropriate change process which will lead from the given situation to a new situation until some date in the future where the then given situation — the envisioned goal state — has become real and the situation from the beginning, which has been ranked down, has disappeared, or is at least weakened in a way that one can say, yes, it has changed.

Usually engineers are known to enable change processes which enable the production of everyday things (tools, products, machines, houses, plants, ships, airplanes, …), but to the extend that the engineering is touching the everyday life deeper and deeper (e.g. the global digital revolution absorbing more and more from the real life processes by transforming them into digital realities forcing human persons to act digitally and not any more with their bodies in the everyday world) the sharp boundary between engineering products and the societal life of human persons is vanishing. In such a context engineering is becoming social engineering even if the majority of traditional engineers this doesn’t see yet in this way. As the traditional discipline MMI Man-Machine Interface and then  expanded to HMI Human-Machine Interaction and further morphed into DAAI Distributed Actor-Actor Interaction this  already manifests, that the realm of human persons, yes  the whole of society is already included in engineering.  The border between machines and human actors is already at least fuzzy and the mixing of technical devices and human actors (as well as all other biological actors) has already gained a degree which does not allow any longer a separation.

These ideas would argue for the option to see anthropology as social engineering: thematizing all the important visions which seem to be helpful or important for a good future of modern mankind, and to help to organize change processes, which will support approaching this better future. That these visions can fail, can be wrong is part of the ever lasting battle of the homo sapiens to gain the right knowledge.

2) Anthropology as  an Empirical Science

… to be continued …

3) Anthropology as a Hybrid Couple of Science and Engineering

… to be continued …

 

 

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

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

CONTEXT

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

Abstract

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

PDF DOCUMENT

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

PHILOSOPHY LAB

eJournal: uffmm.org

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

Changes: July 20.2019 (Rewriting the introduction)

CONTEXT

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

SCOPE OF SECTION

This section deals with  the following topics:

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

 

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

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

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

PDF

ABSTRACT

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

INTRODUCTION

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

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

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

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

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

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

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

Actor Story

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

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

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

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

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

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

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

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

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

Actor Model

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

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

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

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

Def: Input-Output System (IOSYS)

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

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

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

Testing

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

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

Further Readings:

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

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

REFERENCES

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

EXAMPLE

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

uffmm – RESTART AS SCIENTIFIC WORKPLACE

RESTART OF UFFMM AS SCIENTIFIC WORKPLACE.
For the Integrated Engineering of the Future (SW4IEF)
Campaining the Actor-Actor Systems Engineering (AASE) paradigm

eJournal: uffmm.org, ISSN 2567-6458
Email: info@uffmm.org

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.

AAI (KNOWN AS HMI, HCI …)

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.

Ontologies of the AASE paradigm
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.

THEORY IN A BOOK FORMAT

(Still not final)

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.

Last Update 22.June 2018

Philosophy of the Actor

This sections describes basic assumptions about the cognitive structure of the human AAI expert.

From HCI to AAI. Some Bits of History

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.