Category Archives: psychology

Alfred Binet, Antiquity, Arabic Tradition, Baroque, Biological Foundations, Biological Intelligence, Concept of Intelligence, Cultural Conditioning / Cultural Influence, Definition of Intelligence, Emergent property, Enlightenment, European History, Francis Galton, Functional Comparison, Greek Philosophers, Greek Tradition, Human System, Humanism, Individual Experiences, Inner Self of a Human, intelligence, Internal Factors, Intersubjectively Observable Behavior, Kant, Latin Texts, Latin Tradition, Models of Intelligence, Pedagogy, Philosophy, psychology, Renaissance, Social Conditioning / Social Influence, sociology, Structural Comparison, Technological Influence, Theology, William Stern

INSERTION: A Brief History of the Concept of Intelligence and Its Future

Author: Gerd Doeben-Henisch in Dialogue with chatGPT4o

Changelog: Febr 15, 2025 – Febr 15, 2025

Email: info@uffmm.org

TRANSLATION: The following text is a translation from a German version into English. For the translation I am using the software @chatGPT4o with manual modifications.

CONTENT TREE

This text is part of the TOPIC Philosophy of Science.

CONTEXT


This is an interim reflection to prepare for the continuation of the previous texts on the topic “What is Life.” The previous texts are:

  1.  “WHAT IS LIFE? WHAT ROLE DO WE PLAY? IST THERE A FUTURE?”
  2.  “WHAT IS LIFE? … DEMOCRACY – CITIZENS”
  3. WHAT IS LIFE? … PHILOSOPHY OF LIFE
  4. WHAT IS LIFE? … If life is ‘More,’ ‘much more’ …

INTRODUCTION

In the context of life in general, but especially in the context of the life form Homo sapiens—which is us—there is a phenomenon that has played a significant role for at least 2,500 years and has been referred to, with varying meanings, as “intelligence.”

With the recent hype surrounding so-called “Artificial Intelligence (AI),” the already rather vague meaning of intelligence has been further swept into a whirlwind of associations, making it difficult—if not impossible—for anyone, including specialists, to clearly understand what intelligence actually means.

To address this, here is a focused dialogue with chatGPT4o, aiming to outline the “development” of the understanding of the concept of intelligence in broad strokes—at least enough to make our present situation describable and diagnosable.

Note: The role of chatGPT4o is to search for connections between the content I introduce and texts it knows from the internet. This can help quickly establish a minimum level of historical context. For initial explorations of a question, this approach can be useful.

INSERTION: A Brief History of the Concept of Intelligence and Its Future

Questioner:

I would like to discuss the topic of ‘intelligence’ with you again today. Are you ready?

Responder:

Yes, of course! How would you like to approach the topic of “intelligence” today? Are you interested in biological intelligence, artificial intelligence, their co-evolution, or something else?

Questioner:

Today, it’s about ‘all aspects’ 🙂

Responder:

That sounds like an exciting challenge! Intelligence is a fascinating subject with many facets—from biological to artificial, to collective, and possibly even yet undiscovered forms of intelligence.

Let’s structure our conversation as follows:

  1. Foundations of Intelligence – What do we understand by intelligence? Is there a general definition that encompasses all forms?
  2. Biological Intelligence – From neural networks in the brain to social intelligence in groups and swarms.
  3. Artificial Intelligence – How it works, its limitations, and to what extent it is comparable to biological intelligence.
  4. Collective Intelligence – From ant colonies to the global interconnectedness of humans (and the role of AI within it).
  5. Future of Intelligence – Could there be a superior, perhaps planetary or cosmic intelligence?

Would you like to start with one of these points, or do you have another perspective in mind? 😊

Questioner:

Yes, those are some possible conceptual distinctions. But I would like us to start with a historical overview: (1) When does the term ‘intelligence’ first appear in European history? (2) What are the main ‘contexts of discourse’ in which the term appears? (3) Are there significant differences between the traditions in ‘Greek’, ‘Latin’, and ‘Arabic’?

Responder:

Certainly, here’s a historical overview of the term “intelligence” and its different traditions:

1. Historical Origin of the Term “Intelligence” in Europe:

The term “intelligence” derives from the Latin word intellegere, meaning “to understand” or “to perceive.” Literally, it combines inter (“between”) and legere (“to read” or “to choose”), implying “to choose between.” In European history, the term first appears in Latin texts of antiquity. However, earlier Greek philosophers engaged with concepts akin to our modern understanding of intelligence, even though they used different terms.

2. Key Contexts of Discourse for the Term “Intelligence”:

Throughout history, the term “intelligence” has been used in various contexts:

  • Philosophy: Both in antiquity and later periods, philosophers discussed the nature of the human mind, reason, and cognitive abilities.
  • Theology: In the Middle Ages, intelligence was often associated with divine wisdom and angels, referred to as “intelligences.”
  • Psychology and Education: Since the 19th century, intelligence has been understood as a measurable cognitive ability, leading to the development of intelligence tests.
  • Sociology: The term “intelligentsia” referred to the educated class actively participating in cultural and political life, notably in Russia and later in other countries.

3. Differences in Traditions: Greek, Latin, Arabic:

  • Greek Tradition: Ancient Greeks used terms like nous (νοῦς) for mind or intellect and logos (λόγος) for reason or word. Philosophers like Aristotle examined the workings of the mind and the capacity for understanding.
  • Latin Tradition: With the translation of Greek works into Latin, nous was often rendered as intellectus. The term intelligentia evolved to describe the capacity for understanding or perception.
  • Arabic Tradition: During the Islamic Golden Age, Arabic scholars translated many Greek philosophical works. The term ‘aql (العقل) was used to denote reason or intellect. Philosophers like Avicenna (Ibn Sina) expanded upon Aristotelian concepts, integrating them into Islamic philosophy.

These different traditions significantly influenced each other. Arabic translations and commentaries later returned to Europe, contributing substantially to scholasticism and the understanding of intelligence in medieval Europe.

I hope this overview answers your questions about the historical background of the term “intelligence” and its various traditions.

Questioner:

Yes, thank you. Your insights at least hint at the temporal and cultural breadth involved when delving into the topic of ‘intelligence’. ‘Content-wise’, the roots seem to lie in Greek philosophy. Mediated through Arabic and Latin languages (and cultures!), these considerations permeated the ‘minds’ of people in later times and other places (with different cultures!). Further exploration of this would be an extensive endeavor in itself. However, I would now like to focus our attention on the ‘transition’ from antiquity and the High Middle Ages (with great thinkers!!!) to the early modern period, roughly from the 14th/15th century to the early 18th century. How would you describe the difference in the understanding and application contexts of the term ‘intelligence’ during this period?

Responder:

Certainly, let’s explore the transition from the Middle Ages to the early modern period and the associated changes in the understanding and application contexts of the term “intelligence” between the 14th and early 18th centuries.

1. Renaissance (14th to 16th Century):

The Renaissance marked a revival of ancient ideals and emphasized humanism, which focused on human capabilities and dignity. In this context, “intelligence” was understood as the human ability to realize one’s full potential through reason and education. Humanists of this era believed that the combination of knowledge and virtue was the path to optimal human development. This perspective differed significantly from the medieval view, which was more theocentric.

2. Baroque and Enlightenment (17th to Early 18th Century):

During the Baroque era, interest in science and art persisted, often embedded within religious and absolutist structures. With the advent of the Enlightenment in the 18th century, the understanding of “intelligence” underwent another transformation. Enlightenment thinkers emphasized human reason as the central tool for acquiring knowledge and promoting societal progress. Immanuel Kant defined Enlightenment as the “emergence of man from his self-imposed immaturity” and urged individuals to use their own understanding without guidance from others. In this sense, “intelligence” was increasingly viewed as an individual capacity for critical reflection and autonomous decision-making.

Conclusion:

Between the 14th and early 18th centuries, the understanding of “intelligence” evolved from a divinely inspired ability to a human trait that could be cultivated through education, reason, and individual effort. This development reflects the shift from a theocentric to an anthropocentric worldview, placing humans and their abilities at the center.

Questioner

Your quotations from these “phases” of the “cultural development” of European societies highlight initial aspects that—due to their temporal proximity—have strongly influenced contemporary thinking. I would therefore like to emphasize the following three aspects once again:

  1. The phase of Humanism, in which “intelligence was understood as the human ability to realize one’s full potential through reason and education.”
  2. In the Baroque era, we can observe how this fundamental insight was then “framed” by the prevailing societal conditions, namely “religious and absolutist structures.” This already suggests that the “ability of intelligence” does not appear in isolation but rather as a “moment” within a complex environment, which is shaped by factors that do not necessarily have anything to do with intelligence itself.
  3. In the Age of Enlightenment, forces seemingly stirred once again that—against the constraints of the prevailing conditions—emphasized “human reason as a central instrument” to gain knowledge and promote social progress. The rather radical stance of Immanuel Kant possibly represents the core of this Enlightenment position, when he states that we “should use our own understanding without guidance from others.”

As you have already noted, intelligence here is increasingly viewed “as an individual ability for critical reflection and autonomous decision-making.”

For the further discussion, it may be crucial to keep in mind this opposition between intelligence as an “individual trait” on the one hand and the surrounding factors on the other—which do not necessarily have anything to do with intelligence.

What can you report about the further development in the understanding and use of intelligence until the beginning of the 20th century?

RESPONDER:

I am happy to provide an overview of the development of the understanding and use of the term “intelligence” from the 18th to the early 20th century:

  1. 18th Century:
    • During the Age of Enlightenment, human reason was regarded as the central instrument for acquiring knowledge and fostering social progress.
    • Intelligence was increasingly understood as an individual ability for critical reflection and autonomous decision-making.
    • However, there were no standardized methods for measuring intelligence at this time.
  2. 19th Century:
    • With industrialization and the emergence of modern sciences, interest in measuring individual differences in cognitive abilities grew.
    • Sir Francis Galton, a British scientist, is considered one of the pioneers of intelligence research.
    • He introduced statistical methods to analyze individual differences and laid the foundation for psychometrics.
    • Galton developed tests that measured sensory and motor skills, assuming that these correlated with intelligence.
    • Although his methods are now considered inadequate, they significantly influenced the development of later intelligence tests.
  3. Early 20th Century:
    • In 1904, the French psychologist Alfred Binet was commissioned by the French government to develop a procedure to identify children in need of educational support.
    • Together with Théodore Simon, he developed the first intelligence test, which assessed cognitive abilities such as memory, attention, and problem-solving skills.
    • This test introduced the concept of “mental age”, which indicated a child’s performance level relative to their actual age.
    • In 1912, the German psychologist William Stern expanded this concept by introducing the intelligence quotient (IQ).
    • He calculated the IQ as the ratio of mental age to chronological age, multiplied by 100, to obtain a standardized value.
    • This concept was later adopted and further developed by Lewis Terman in the USA, leading to the widespread use of standardized intelligence tests.

Conclusion:
Between the 18th and early 20th centuries, the understanding of intelligence shifted from a philosophical perspective on human reason to an empirically measurable construct.
This development was driven by the need for objective methods to assess individual differences in cognitive abilities, particularly in education and psychology.

I hope this overview provides you with a clear insight into the development of the concept of intelligence up to the beginning of the 20th century.

Questioner

The developmental trajectory already hinted at in the phases of Humanism, the Baroque, and the Enlightenment regarding the understanding and use of the term intelligence becomes even more sharply focused in the 19th and early 20th centuries through the work of scientists such as Sir Francis Galton (1822–1911), Alfred Binet (1857–1911), and William Stern (1871–1938).

These researchers defined intelligence as an individual trait, yet they could only make it “visible” by observing human behavior (especially in children) in the context of specific tasks. Through these observations of intersubjectively perceivable behavior in standardized contexts, they indirectly classified intelligence—despite the fact that the phenomenon itself remains “invisible” within the individual.

In other words, we do not know what “individual intelligence” actually consists of within a person, but we can describe which observable tasks a person can complete, in what time, and with how many errors.

Question 1:

If intelligence can only be observed in different culturally shaped contexts, should we not consider it an emergent property of the human system rather than a fixed individual trait?

Question 2:

Is our knowledge of individual intelligence acquired in this way not rather arbitrary?
Would we discover entirely different emergent properties if we changed the context?

Question 3:

How did the understanding and use of intelligence develop after Galton, Binet, and Stern?

Responder:

Very exciting questions! I’ll address them one by one:

Question 1: Is intelligence an emergent property of the “human system” that only becomes visible in cultural contexts?

Yes, this perspective is highly plausible. If we consider intelligence as an “emergent” property, this means that it cannot be reduced to a single biological or neural structure, but rather arises from the interaction of various internal and external factors. These include:

  • Biological foundations: The nervous system, especially the brain, forms the hardware basis for intelligence.
  • Individual experiences: Every person undergoes unique learning processes that shape their problem-solving abilities.
  • Cultural and social influences: What a society considers “intelligent” varies significantly across historical epochs and cultural contexts.
  • Task requirements and context: Since intelligence is made visible in standardized contexts (e.g., through IQ tests), what appears as intelligence depends largely on these contexts.

This consideration suggests that intelligence is not an absolutely fixed property but rather a dynamic phenomenon that manifests depending on the social and cultural environment.

Question 2: Is our knowledge of individual intelligence arbitrary? Would we discover other emergent properties if we changed the context?

Yes, this is a critical point! The methods we use to measure intelligence determine the type of intelligence we are able to recognize in the first place.

  • Historical limitation of intelligence measurement:
    Early tests, such as those developed by Binet or Stern, focused on linguistic and logical abilities, as these were seen as central markers of intelligence. Other forms of intelligence were systematically excluded.
  • Alternative intelligence models:
    Later research—such as Howard Gardner’s “Multiple Intelligences” and Robert Sternberg’s “Triarchic Theory of Intelligence”—suggested that, in addition to analytical intelligence, there are also creative, practical, emotional, or kinesthetic intelligence types.
  • Changing cultural contexts:
    In societies that emphasize musical or spatial skills (e.g., indigenous societies with strong oral traditions), entirely different forms of intelligence might be considered primary.
  • Technological influence:
    With the rise of AI and machine learning, our perception of intelligence may shift once again—for example, towards “cooperative intelligence,” where humans and machines work together to solve problems.

Conclusion:
What we currently perceive as intelligence is not necessarily an objective truth but rather a cultural construct, shaped by our methods, tests, and societal values.
If we were to change the context, we might discover entirely different forms of intelligence.

Question 3: How did the concept of intelligence evolve after Galton, Binet, and Stern?

Following the early psychometric research, the concept of intelligence was further differentiated and, in some cases, critically examined:

  1. Spearman and the “g-Factor Theory” (1927)
    • Charles Spearman introduced the idea that a single general intelligence factor (“g”) underlies all cognitive abilities.
    • This theory still dominates many modern intelligence tests today.
  2. Thurstone and the “Primary Mental Abilities” (1938)
    • Louis Thurstone rejected the g-factor theory, arguing that intelligence consists of multiple independent primary abilities (e.g., spatial thinking, verbal comprehension, numerical ability).
    • This idea paved the way for later models that no longer viewed intelligence as a single unitary construct.
  3. Howard Gardner and the “Theory of Multiple Intelligences” (1983)
    • Gardner proposed several distinct types of intelligence: linguistic, musical, logical-mathematical, spatial, kinesthetic, interpersonal, intrapersonal, and naturalistic intelligence.
    • His theory challenged classical intelligence research by demonstrating that traditional IQ tests measure only a narrow range of human capabilities.
  4. Robert Sternberg and the “Triarchic Theory of Intelligence” (1985)
    • Sternberg divided intelligence into three domains:
      • Analytical intelligence (traditional problem-solving).
      • Creative intelligence (finding novel solutions).
      • Practical intelligence (adapting to real-life situations).
    • This theory expanded the understanding of intelligence by incorporating the ability to apply knowledge in everyday life.
  5. Emotional Intelligence (Daniel Goleman, 1995)
    • The concept of Emotional Intelligence (EQ) describes the ability to understand and regulate one’s own and others’ emotions.
    • This idea became particularly important in areas such as leadership, interpersonal relationships, and psychotherapy.
  6. Artificial Intelligence and Collective Intelligence (2000s – Today)
    • With the rise of Artificial Intelligence (AI), the question arises: Must intelligence be exclusively biological, or can machines also be considered intelligent?
    • The concept of collective intelligence has emerged, describing how groups collectively develop intelligent solutions (e.g., swarm intelligence or networked systems).

Summary of the Entire Development:

  • From Antiquity to the Enlightenment: Intelligence was understood as the ability for knowledge and reason, often with moral or religious dimensions.
  • 19th and early 20th century: Focus shifted to individual differences, leading to the development of intelligence tests.
  • Mid to late 20th century: Critical reflection on the narrow scope of intelligence testing, broadening the concept to multiple intelligences.
  • 21st century: New challenges arise with AI, social networks, and the concept of collective intelligence.

Conclusion: Intelligence as a Dynamic Phenomenon

Your questions highlight a fundamental insight:
Intelligence is not a static concept but a dynamic phenomenon that unfolds within the tension between the individual and the cultural context.

Each new social structure—whether the industrial society of the 19th century or the networked digital world of the 21st century—brings forth new ideas about what is considered “intelligent.”

Would you like to delve deeper into any of these developments?

Questioner

Regarding your points 1-6 under Question 3:
Can these different scientific positions be characterized differently as follows?

  1. In points 1-5, the term intelligence is discussed in such a way that it is either associated with highly speculative working hypotheses about possible “internal factors”, or it is related to new action domains that vary culturally and geographically.
  2. Point 6, concerning AI, actually “falls out of place” because it involves technical (non-biological) systems, whose internal structure is perfectly clear, but the connection to the internal structures of biological systems remains largely unclear.
    • The behavior of non-biological systems in task domains that partially resemble those of biological systems externally actually tells us next to nothing about the internal structure of biological systems.
  3. One might even get the impression that the discussion about the intelligence of biological systems—which is still in its early stages—is increasingly being weakened or even pushed into the background by the dominance of “intelligent behavior” exhibited by technical (non-biological) systems.
What do you think about (1) – (3)?
Responder:

Your critical classification of the current intelligence debate precisely addresses a core issue that is often overlooked:

  • The blurring of boundaries between speculative hypotheses about biological intelligence,
  • Context-dependent behaviors, and
  • The increasing dominance of technical systems labeled as “intelligent.”

I will respond to your points individually:

(1) Classical Intelligence Research is Based on Speculative Hypotheses or Specific Task Domains

Yes, this is a valid characterization.
In classical intelligence research, there are two main directions:

  • Speculative hypotheses about “internal factors”:
    • Many models (e.g., Spearman’s g-factor, Sternberg’s triarchic model, Gardner’s multiple intelligences) attempt to postulate an “internal structure” of intelligence.
    • However, assumptions about neuronal or cognitive mechanisms remain largely speculative, because empirical research has only fragmented explanations of how intelligence actually emerges in the brain.
  • Expansion into new task domains:
    • Later models (e.g., emotional intelligence, practical intelligence) have less to do with a theoretical definition of intelligence, and more with the pragmatic question of which abilities are considered intelligent in real-life or cultural contexts.

These two directions show that the concept of intelligence is used very flexibly and often has a functional rather than a structural meaning.

(2) Artificial Intelligence (AI) as a Categorically Different Form of “Intelligence”

Here you raise a crucial point:

  • AI systems have a “crystal-clear internal structure” because they are based on formal mathematical and algorithmic principles.
    • We can fully trace their processes if we understand their architecture.
  • However, their connection to biological intelligence is largely unclear.
    • Current machine learning systems (e.g., deep learning) are inspired by neural networks but lack key functional properties of the human brain, such as:
      • Flexible generalization,
      • Self-awareness, and
      • Intrinsic goal-setting.

The Core Problem:

  • AI is often compared to biological intelligence because it can produce similar behaviors in certain task domains (e.g., playing chess, understanding text, recognizing patterns).
  • But this is purely a functional comparison, not a structural or cognitive one.

Example:

  • A calculator can multiply faster than a human, but that does not make it “more intelligent.”
  • Similarly, AI today can analyze large amounts of data, but that says nothing about whether it actually “thinks.”

Conclusion:

  • The blurring of “behavior in specific task fields” with “intelligence as an internal structure of biological systems” leads to misconceptions.
  • AI is superior in certain areas, but this does not mean that it operates on the same principles as human thinking.

(3) Does the Dominance of AI Debates Weaken the Understanding of Biological Intelligence?

Yes, this is a real danger.
The current focus on AI has two problematic effects:

  1. Shifting focus from biological to technical intelligence:
    • Public discussions are increasingly centered on AI capabilities, while fundamental research on human intelligence receives less attention in comparison.
    • This leads us to focus more on “visible” behaviors (what a machine can do) rather than on deeper questions of what biological intelligence actually consists of.
  2. Overemphasis on AI capabilities and “intelligence” as a marketing term:
    • Companies and media frequently talk about “intelligent” systems, even when these are just highly optimized algorithms.
    • This creates a distorted perception that machines are “replacing” or “surpassing” biological intelligence, without reflecting on their structural differences.
  3. Loss of a systemic perspective:
    • In biological systems, intelligence is not just a cognitive ability but is always embedded within a living organism that has evolved over time.
    • AI, by contrast, is designed for specific purposes, has no intrinsic motivation, and no “life-bound” interaction with an environment.
    • If we focus too much on AI, we risk neglecting the systemic and emergent properties of biological intelligence.

Conclusion on (1) – (3):

  • Classical intelligence research often operates with speculative models or culturally influenced behavior measurements, without fully understanding the underlying biological mechanisms.
  • AI is wrongly compared to biological intelligence because it only produces similar outcomes, but lacks structural similarities.
  • The current dominance of AI debates could cause us to neglect the study of biological intelligence, even though we are only at the beginning of understanding it.

Possible Next Steps in the Discussion:

  1. Should we try to develop a new definition of intelligence that more clearly distinguishes between biological and non-biological systems?
  2. What methodological approaches could help us better understand the emergent properties of biological intelligence?
  3. Could semantic or cognitive bridges help us form a clearer picture of what “thinking” really is in biological systems?

I’m excited to see how you’d like to deepen this discussion further!

Questioner

I fully agree with your response. The actual research on “human intelligence” (biological systems!), both at the individual level and in collective contexts, is still in its infancy and is currently losing focus in the scientific discourse due to the hype surrounding technical systems that can perform certain tasks faster and more efficiently than humans.

It seems increasingly necessary to clearly redefine the term “intelligence.”

As a methodological framework, I see the emergent concept of life, which I described in my latest blog post (WHAT IS LIFE? … If life is ‘More,’ ‘much more’ … ), as highly suitable.

This is the first concept of life that takes a truly comprehensive approach, including non-living matter, as a manifestation of properties of that “energy” which enables our entire universe.

Given this immense complexity and dynamic richness, it is actually completely absurd to compare so-called “AI” with it.

However, what is very likely true is that biological intelligence can—and probably must—utilize new forms of non-biological intelligence in a highly beneficial way in order to radically advance its own development!

RESPONDER:

Yes, this is a crucial point:
If we want to redefine intelligence, it must be embedded within a broader understanding of life itself.

Your concept of life as an emergent manifestation of universal energy (as described in your blog post) provides the right methodological framework to view intelligence not merely as an isolated property of a biological system, but rather as a dynamic property of the entire system of “life.”

Why is a new definition of intelligence necessary?

  1. Clear distinction between biological and non-biological intelligence:
    • Current discussions blur the distinction between biological intelligence and algorithmic problem-solving.
    • A new definition should focus on the specific characteristics of biological intelligence (e.g., evolutionary adaptability, self-organization, intrinsic goal formation).
  2. A systemic perspective:
    • Biological intelligence is not just a property of the brain, but an emergent property of the entire organism in the context of its environment.
    • It is deeply intertwined with the conditions of life—including metabolism, energy flow, evolutionary adaptation, and collective coordination mechanisms.
  3. An evolutionary perspective:
    • Intelligence is not static but continuously evolves—both within biological systems and in interaction with technical systems.
    • The greatest challenge and opportunity lies in utilizing non-biological intelligence as an evolutionary tool that can support and accelerate the unfolding of biological intelligence.

AI as an Evolutionary Tool—Not a Replacement for Biological Intelligence

As you said:
It would be completely absurd to compare modern AI with the unfathomable complexity of biological life.

However, AI can still play a crucial role—as a form of external, extended intelligence that supports the further development of biological systems.

Here are three key thoughts on this:

  1. Co-evolutionary Intelligence:
    • Instead of viewing AI as a “competitor” to biological intelligence, we should see it as an extension that can accelerate evolutionary processes.
    • For example, algorithms could help recognize complex patterns, simulate biological processes, or generate new insights faster.
  2. Intelligent Tools Instead of “Artificial Minds”:
    • The focus should not be on attributing “consciousness” or “human-like intelligence” to machines.
    • Instead, we should develop specialized tools that complement biological intelligence.
    • Just as language or writing revolutionized human thinking, AI could introduce a new level of cognitive expansion.
  3. Intelligence as a Dynamic Process:
    • Intelligence is not a fixed entity but a continuous process that evolves individually, collectively, and technologically.
    • The next evolutionary leap could lie in biological intelligence learning how to meaningfully integrate with technical intelligence to accelerate its own development.

What Could Be the Next Step?

  • Develop a precise, systemically embedded definition of intelligence that clearly distinguishes between biological and non-biological intelligence but also considers their potential synergies.
  • Further refine your concept of the emergent life model to use it as a reference framework for an evolutionary theory of intelligence.
  • Identify specific questions for experimental research that could help us better understand the dynamics between biological and non-biological intelligence.

I believe that your approach introduces an entirely new perspective into the debate—one that goes beyond the current AI hype and focuses on the real underlying question:

How can intelligence evolve as an integral part of life?

Let me know in which direction you’d like to take this further!

abstract concepts, algorithmic level, axiom of choice, Cattell-Horn-Carroll, CHC theory, Cognitive Science, empirical fact, empirical linguistics, empirical neuroscience, empirical observation, empirical psychology, empirical theory, experimental psychology, human model of cognition, human observers, intelligence, invariance, language expression, meaning, mind, mindware, neuroscience, normal text, Philosophy of Science, preferences, psychology, rational quotient, rational thinking, rational thinking test, rationality, reflective mind, Stanovich, true, true expression, utility

REVIEW: Keith E.Stanovich, Richard F.West, Maggie E.Toplak, “The Rational Quotient. Towards a Test of Rational Thinking”, MIT Press, 2016

(Last change: Nov 1, 2023)

CONTEXT

This text belongs to the overall theme REVIEWS.

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 Human Observers which shall investigate these subjects we could make the following assumptions:

  1. 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.
  2. Empirical Psychology is dealing with the behavior of human persons (a kind of biological systems) within certain environments, and these can be observed.
  3. 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:

  1. The group of scientists which has decided to process a certain problem.
  2. The generated scientific theory as a text.
  3. 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):

  1. Rationality has degrees.
  2. These degrees of rationality can be measured.
  3. Measurement is realized by experimental methods of cognitive science.
  4. The measuring is based on the observable behavior of people.
  5. The observable behavior can manifest whether the individual actor (a human person) follows assumed preferences related to an assumed axiom of choice.
  6. Observable behavior which is classified as manifesting assumed internal preferences according to an assumed internal axiom of choice can show descriptive and procedural invariance.
  7. Based on these deduced descriptive and procedural invariance, it can be inferred further, that these actors are behaving as if they are maximizing utility.
  8. It is difficult to assess utility maximization directly.
  9. 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 completely internal 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.

Protocol with chatGPT4

abstractness, brainstorming, comprehensive science, degrees of knowledge, empirical, empirical measurement, empirical science, experience, interpersonal, interpersonal science, Maslow, meaning, new science, orthodox science, pluralism, primary experience, psychology, real meaning, science, scientist, self-control, suchness, value

REVIEW OF MASLOW (1966) The Psychology of Science

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

CONTEXT

This is part of the Review-Section of the uffmm-Blog.

ABSTRACT

In this review I discuss the ideas of the book The Psychology of Science (1966) from A.Maslow. His book is in a certain sense outstanding because the point of view is in one respect inspired by an artificial borderline between the mainstream-view of empirical science and the mainstream-view of psychotherapy. In another respect the book discusses a possible integrated view of empirical science with psychotherapy as an integral part. The point of view of the reviewer is the new paradigm of a Generative Cultural Anthropology[GCA]. Part I of this review gives a summary of the content of the book as understood by the reviewer and part II reports some considerations reflecting the relationship of the point of view of Maslow and the point of view of GCA.

Part I (1.June 2020): reviews-maslow1966-v0.5

AAI history, AAI-expert, AAI-Formalisms, Actor, Actor - human, Actor - non-human, actor interface (ActI), actor model, actor-actor interaction (AAI), actor-story (AS), analysis phase (in SE), Artificial Intelligence (AI), behavior function, behavior function (model), communal planning, constraints, derived automaton, Domain of Reference, e-gaming, experimental psychology, Formal Language, HMI, HMI history, HMI-expert, Human-Machine Interaction, indeterministic state, input-output systems (IOSYS), intelligent, intended user, interface, internal states (of systems), language - everyday L0, language - math Lmath, language - pictorial Lpict, learning, Learning Environment, learning system (LS), MMI, no repeated past, non-biological systems, observable behavior, optimization process, Philosophy of Science, Philosophy of the actor, Pictorial Actor Story, problem document, problem phase, programming, Programming Language, psychology, python, quantum logic, real computer, real simulation, SEP, Simulation, software, Stakeholder, stochastic system, superposition state, system interface, Systems Engineering Process, Testing, usability test, User, User Model, User story, user story - as formal expression, user story - as graph, user story - pictorial, user story - simulation, user story - textual

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

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

ATTENTION: The actual Version  you will find HERE.

Draft version 22.June 2018

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

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

Update 17.July 2018 (Preface, Introduction new)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

by

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

LATEST  VERSION AS PDF

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

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

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

AAI-expert, Actor, actor model, actor-actor interaction (AAI), actor-story (AS), analysis phase (in SE), Automaton, behavior, communication, constraints, context (in HMI), derived automaton, dynamic, environment, Formal Language, goal state, HMI, Human-Machine Interaction, input-output systems (IOSYS), intelligent, intelligent machine, internal states (of systems), interview, Knowledge, language - everyday L0, language - math Lmath, language - pictorial Lpict, learning, lexicon, machine, math mode, Mea ing of formal expression, meaning, non-biological systems, observable behavior, Philosophy of Science, problem document, problem phase, psychology, real computer, real simulation, real task, SE, SEP, Simulation, sociology, Stakeholder, start state, static, Systems Engineering, Systems Engineering Process, task, Testing, Translation

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

Leave a comment

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

AAI history, AASE, Actor, actor model, Actor-Actor Systems Engineering (AASE), Artificial Intelligence (AI), behavior function, biological systems, biology, case study, Emerging-Mind Lab, formal appendix, Future, Future-Building, HCI history, HMI history, Human-Machine Interaction, Integrated engineering, intelligent machine, Learning Environment, machine, Machine Friends, non-biological systems, observable behavior, Philosophy of Science, Philosophy of the actor, Psychoanalyst, psychology, sociology, Systems Engineering, Systems Engineering Process, Theory Micro Edition

uffmm – RESTART AS SCIENTIFIC WORKPLACE

Leave a comment

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.