OKSIMO MEETS POPPER. The Generalized Oksimo Theory Paradigm

eJournal: uffmm.org
ISSN 2567-6458, 5.April – 5.April  2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last changes: Small corrections, April 8, 2021

CONTEXT

This text is part of a philosophy of science  analysis of the case of the oksimo software (oksimo.com). A specification of the oksimo software from an engineering point of view can be found in four consecutive  posts dedicated to the HMI-Analysis for  this software.

THE GENERALIZED OKSIMO THEORY PARADIGM
The Generalized Oksimo Paradigm
Figure: Overview of the Generalized Oksimo Paradigm

In the preceding sections it has been shown that the oksimo paradigm is principally fitting in the theory paradigm as it has been  discussed by Popper. This is possible because some of the concepts used by Popper have been re-interpreted by re-analyzing the functioning of the symbolic dimension. All the requirements of Popper could be shown to work but now even in a more extended way.

SUSTAINABLE FUTURE

To describe the oksimo paradigm it is not necessary to mention as a wider context the general perspective of sustainability as described by the United Nations [UN][1]. But if one understands the oksiomo paradigm deeper and one knows that from the 17 sustainable development goals [SDGs] the fourth goal [SDG4] is understood by the UN as the central key for the development of all the other SDGs [2], then one can understand this as an invitation to think about that kind of knowledge which could be the ‘kernel technology’ for sustainability. A ‘technology’ is not simply ‘knowledge’, it is a process which enables the participants — here assumed as human actors with built-in meaning functions — to share their experience of the world and as well their hopes, their wishes, their dreams to become true in a reachable future. To be ‘sustainable’ these visions have to be realized in a fashion which keeps the whole of biological life alive on earth as well in the whole universe. Biological life is the highest known value with which the universe is gifted.

Knowledge as a kernel technology for a sustainable future of the whole biological life has to be a process where all human biological life-forms headed by the human actors have to contribute with their experience and capabilities to find those possible future states (visions, goals, …) which can really enable a sustainable future.

THE SYMBOLIC DIMENSION

To enable different isolated brains in different bodies to ‘cooperate’ and thereby to ‘coordinate’ their experience, and their behavior, the only and most effective way to do this is known as ‘symbolic communication’: using expressions of some ordinary language whose ‘meaning’ has been learned by every member of the population beginning with being born on this planet.  Human actors (classified as the life-form ‘homo sapiens’) have the most known elaborated language capability by being able to associate all kinds of experience with expressions of an ordinary language. These ‘mappings’ between expressions and the general experience is taking place ‘inside the brain’ and these mappings are highly ‘adaptive’; they can change over time and they are mostly ‘synchronized’ with the mappings taking place in other brains. Such a mapping is here called a ‘meaning function’ [μ].

DIFFERENT KINDS OF EXPRESSIONS

The different sientific disciplines today have developed many different views and models how to describe the symbolic dimension, their ‘parts’, their functioning. Here we assume only three different kinds of expressions which can be analayzed further with nearly infinite many details.

True Concrete Expressions [S_A]

The ‘everyday case’ occurs if human actors share a real actual situation and they use their symbolic expressions to ‘talk about’ the shared situation, telling each other what is given according to their understanding using their built-in meaning function μ. With regard to the shared knowledge and language these human actors can decide, wether an expression E used in the description is matching the observed situation or not. If the expression is matching than such an expression is classified as being a ‘true expression’. Otherwise it is either undefined or eventually ‘false’ if it ‘contradicts’ directly. Thus the set of all expressions assumed to be true in a actual given situation S is named  here S_A. Let us look to an example: Peter says, “it is raining”, and Jenny says “it is not raining”. If all would agree, that   it is raining, then Peters expression is classified as ‘true’ and Jennys expression as ‘false’. If  different views would exist in the group, then it is not clear what is true or false or undefined in this group! This problem belongs to the pragmatic dimension of communication, where human actors have to find a way to clarify their views of the world. The right view of the situation  depends from the different individual views located in the individual brains and these views can be wrong. There exists no automatic procedure to get a ‘true’ vision of the real world.

General Assumptions [S_U]

It is typical for human actors that they are collecting knowledge about the world including general assumptions like “Birds can fly”, “Ice is melting in the sun”, “In certain cases the covid19-virus can bring people to death”, etc. These expressions are usually understood as ‘general’ rules  because they do not describe a concrete single case but are speaking of many possible cases. Such a general rule can be used within some logical deduction as demonstrated by the  classical greek logic:  ‘IF it is true that  “Birds can fly” AND we have a certain fact  “R2D2 is a bird” THEN we can deduce the fact  “R2D2 can fly”‘.  The expression “R2D2 can fly”  claims to be  true. Whether this is ‘really’ the case has to be shown in a real situation, either actually or at some point in the future. The set of all assumed general assumptions is named here S_U.

Possible Future States [S_V]

By experience and some ‘creative’ thinking human actors can imagine concrete situations, which are not yet actually given but which are assumed to be ‘possible’; the possibility can be interpreted as some ‘future’ situation. If a real situation would be reached which includes the envisioned state then one could say that the vision has become  ‘true’. Otherwise the envisioned state is ‘undefined’: perhaps it can become true or not.  In human culture there exist many visions since hundreds or even thousands of years where still people are ‘believing’ that they will become ‘true’ some day. The set of all expressions related to a vision is named here S_V.

REALIZING FUTURE [X, X]

If the set of expressions S_V  related to a ‘vision’ (accompanied by many emotions, desires, details of all kinds) is not empty,  then it is possible to look for those ‘actions’ which with highest ‘probability’ π can ‘change’ a given situation S_A in a way that the new situation S’  is becoming more and more similar to the envisioned situation S_V. Thus a given goal (=vision) can inspire a ‘construction process’ which is typical for all kinds of engineering and creative thinking. The general format of an expression to describe a change is within the oksimo paradigm assumed as follows:

  1. With regard to a given situation S
  2. Check whether a certain set of expressions COND is a subset of the expressions of S
  3. If this is the case then with probability π:
  4. Remove all expressions of the set Eminus from S,
  5. Add all expressions of the set Eplus to S
  6. and update (compute) all parameters of the set Model

In a short format:

S’π = S – Eminus + Eplus & MODEL(S)

All change rules together represent the set X. In the general theory paradigm the change rules X represent the inference rules, which together with a general ‘inference concept’ X constitute the ‘logic’ of the theory. This enables the following general logical relation:

{S_U, S_A} <S_A, S1, S2, …, Sn>

with the continuous evaluation: |S_V ⊆ Si| > θ. During the whole construction it is possible to evaluate each individual state whether the expressions of the vision state S_V are part of the actual state Si and to which degree.

Such a logical deduction concept is called a ‘simulation’ by using a ‘simulator’ to repeat the individual deductions.

POSSIBLE EXTENSIONS

The above outlined oksimo theory paradigm can easily be extended by some more features:

  1. AUTONOMOUS ACTORS: The change rules X so far are ‘static’ rules. But we know from everyday life that there are many dynamic sources around which can cause some change, especially biological and non-biological actors. Every such actors can be understood as an input-output system with an adaptive ‘behavior function’ φ.  Such a behavior can not be modeled by ‘static’ rules alone. Therefore one can either define theoretical models of such ‘autonomous’ actors with  their behavior and enlarge the set of change rules X with ‘autonomous change rules’ Xa as Xa ⊆ X. The other variant is to include in real time ‘living autonomous’ actors as ‘players’ having the role of an ‘autonomous’ rule and being enabled to act according to their ‘will’.
  2. MACHINE INTELLIGENCE: To run a simulation will always give only ‘one path’ P in the space of possible states. Usually there would be many more paths which can lead to a goal state S_V and the accompanying parameters from Model can be different: more or less energy consumption, more or less financial losses, more or less time needed, etc. To improve the knowledge about the ‘good candidates’ in the possible state space one can introduce  general machine intelligence algorithms to evaluate the state space and make proposals.
  3. REAL-TIME PARAMETERS: The parameters of Model can be connected online with real measurements in near real-time. This would allow to use the collected knowledge to ‘monitor’ real processes in the world and based on the collected knowledge recommend actions to react to some states.
COMMENTS

[1] The 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries – developed and developing – in a global partnership. They recognize that ending poverty and other deprivations must go hand-in-hand with strategies that improve health and education, reduce inequality, and spur economic growth – all while tackling climate change and working to preserve our oceans and forests. See PDF: https://sdgs.un.org/sites/default/files/publication/21252030%20Agenda%20for%20Sustainable%20Development%20web.pdf

[2] UN, SDG4, PDF, Argumentation why the SDG4 ist fundamental for all other SDGs: https://sdgs.un.org/sites/default/files/publications/2275sdbeginswitheducation.pdf

 

 

HMI Analysis for the CM:MI paradigm. Part 2. Problem and Vision

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, February 27-March 16, 2021,
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: March 16, 2021 (minor corrections)

HISTORY

As described in the uffmm eJournal  the wider context of this software project is an integrated  engineering theory called Distributed Actor-Actor Interaction [DAAI] further extended to the Collective Man-Machine Intelligence [CM:MI] paradigm.  This document is part of the Case Studies section.

HMI ANALYSIS, Part 2: Problem & Vision

Context

This text is preceded by the following texts:

Introduction

Before one starts the HMI analysis  some stakeholder  — in our case are the users stakeholder as well as  users in one role —  have to present some given situation — classifiable as a ‘problem’ — to depart from and a vision as the envisioned goal to be realized.

Here we give a short description of the problem for the CM:MI paradigm and the vision, what should be gained.

Problem: Mankind on the Planet Earth

In this project  the mankind  on the planet earth is  understood as the primary problem. ‘Mankind’ is seen here  as the  life form called homo sapiens. Based on the findings of biological evolution one can state that the homo sapiens has — besides many other wonderful capabilities — at least two extraordinary capabilities:

Outside to Inside

The whole body with the brain is  able to convert continuously body-external  events into internal, neural events. And  the brain inside the body receives many events inside the body as external events too. Thus in the brain we can observe a mixup of body-external (outside 1) and body-internal events (outside 2), realized as set of billions of neural processes, highly interrelated.  Most of these neural processes are unconscious, a small part is conscious. Nevertheless  these unconscious and conscious events are  neurally interrelated. This overall conversion from outside 1 and outside 2 into neural processes  can be seen as a mapping. As we know today from biology, psychology and brain sciences this mapping is not a 1-1 mapping. The brain does all the time a kind of filtering — mostly unconscious — sorting out only those events which are judged by the brain to be important. Furthermore the brain is time-slicing all its sensory inputs, storing these time-slices (called ‘memories’), whereby these time-slices again are no 1-1 copies. The storing of time-sclices is a complex (unconscious) process with many kinds of operations like structuring, associating, abstracting, evaluating, and more. From this one can deduce that the content of an individual brain and the surrounding reality of the own body as well as the world outside the own body can be highly different. All kinds of perceived and stored neural events which can be or can become conscious are  here called conscious cognitive substrates or cognitive objects.

Inside to Outside (to Inside)

Generally it is known that the homo sapiens can produce with its body events which have some impact on the world outside the body.  One kind of such events is the production of all kinds of movements, including gestures, running, grasping with hands, painting, writing as well as sounds by his voice. What is of special interest here are forms of communications between different humans, and even more specially those communications enabled by the spoken sounds of a language as well as the written signs of a language. Spoken sounds as well as written signs are here called expressions associated with a known language. Expressions as such have no meaning (A non-speaker of a language L can hear or see expressions of the language L but he/she/x  never will understand anything). But as everyday experience shows nearly every child  starts very soon to learn which kinds of expressions belong to a language and with what kinds of shared experiences they can be associated. This learning is related to many complex neural processes which map expressions internally onto — conscious and unconscious — cognitive objects (including expressions!). This mapping builds up an internal  meaning function from expressions into cognitive objects and vice versa. Because expressions have a dual face (being internal neural structures as well as being body-outside events by conversions from the inside to body-outside) it is possible that a homo sapiens  can transmit its internal encoding of cognitive objects into expressions from his  inside to the outside and thereby another homo sapiens can perceive the produced outside expression and  can map this outside expression into an intern expression. As far as the meaning function of of the receiving homo sapiens  is sufficiently similar to the meaning function of  the sending homo sapiens there exists some probability that the receiving homo sapiens can activate from its memory cognitive objects which have some similarity with those of  the sending  homo sapiens.

Although we know today of different kinds of animals having some form of language, there is no species known which is with regard to language comparable to  the homo sapiens. This explains to a large extend why the homo sapiens population was able to cooperate in a way, which not only can include many persons but also can stretch through long periods of time and  can include highly complex cognitive objects and associated behavior.

Negative Complexity

In 2006 I introduced the term negative complexity in my writings to describe the fact that in the world surrounding an individual person there is an amount of language-encoded meaning available which is beyond the capacity of an  individual brain to be processed. Thus whatever kind of experience or knowledge is accumulated in libraries and data bases, if the negative complexity is higher and higher than this knowledge can no longer help individual persons, whole groups, whole populations in a constructive usage of all this. What happens is that the intended well structured ‘sound’ of knowledge is turned into a noisy environment which crashes all kinds of intended structures into nothing or badly deformed somethings.

Entangled Humans

From Quantum Mechanics we know the idea of entangled states. But we must not dig into quantum mechanics to find other phenomena which manifest entangled states. Look around in your everyday world. There exist many occasions where a human person is acting in a situation, but the bodily separateness is a fake. While sitting before a laptop in a room the person is communicating within an online session with other persons. And depending from the  social role and the  membership in some social institution and being part of some project this person will talk, perceive, feel, decide etc. with regard to the known rules of these social environments which are  represented as cognitive objects in its brain. Thus by knowledge, by cognition, the individual person is in its situation completely entangled with other persons which know from these roles and rules  and following thereby  in their behavior these rules too. Sitting with the body in a certain physical location somewhere on the planet does not matter in this moment. The primary reality is this cognitive space in the brains of the participating persons.

If you continue looking around in your everyday world you will probably detect that the everyday world is full of different kinds of  cognitively induced entangled states of persons. These internalized structures are functioning like protocols, like scripts, like rules in a game, telling everybody what is expected from him/her/x, and to that extend, that people adhere to such internalized protocols, the daily life has some structure, has some stability, enables planning of behavior where cooperation between different persons  is necessary. In a cognitively enabled entangled state the individual person becomes a member of something greater, becoming a super person. Entangled persons can do things which usually are not possible as long you are working as a pure individual person.[1]

Entangled Humans and Negative Complexity

Although entangled human persons can principally enable more complex events, structures,  processes, engineering, cultural work than single persons, human entanglement is still limited by the brain capacities as well as by the limits of normal communication. Increasing the amount of meaning relevant artifacts or increasing the velocity of communication events makes things even more worse. There are objective limits for human processing, which can run into negative complexity.

Future is not Waiting

The term ‘future‘ is cognitively empty: there exists nowhere an object which can  be called ‘future’. What we have is some local actual presence (the Now), which the body is turning into internal representations of some kind (becoming the Past), but something like a future does not exist, nowhere. Our knowledge about the future is radically zero.

Nevertheless, because our bodies are part of a physical world (planet, solar system, …) and our entangled scientific work has identified some regularities of this physical world which can be bused for some predictions what could happen with some probability as assumed states where our clocks are showing a different time stamp. But because there are many processes running in parallel, composed of billions of parameters which can be tuned in many directions, a really good forecast is not simple and depends from so many presuppositions.

Since the appearance of homo sapiens some hundred thousands years ago in Africa the homo sapiens became a game changer which makes all computations nearly impossible. Not in the beginning of the appearance of the homo sapiens, but in the course of time homo sapiens enlarged its number, improved its skills in more and more areas, and meanwhile we know, that homo sapiens indeed has started to crash more and more  the conditions of its own life. And principally thinking points out, that homo sapiens could even crash more than only planet earth. Every exemplar of a homo sapiens has a built-in freedom which allows every time to decide to behave in a different way (although in everyday life we are mostly following some protocols). And this built-in freedom is guided by actual knowledge, by emotions, and by available resources. The same child can become a great musician, a great mathematician, a philosopher, a great political leader, an engineer, … but giving the child no resources, depriving it from important social contexts,  giving it the wrong knowledge, it can not manifest its freedom in full richness. As human population we need the best out of all children.

Because  the processing of the planet, the solar system etc.  is going on, we are in need of good forecasts of possible futures, beyond our classical concepts of sharing knowledge. This is where our vision enters.

VISION: DEVELOPING TOGETHER POSSIBLE FUTURES

To find possible and reliable shapes of possible futures we have to exploit all experiences, all knowledge, all ideas, all kinds of creativity by using maximal diversity. Because present knowledge can be false — as history tells us –, we should not rule out all those ideas, which seem to be too crazy at a first glance. Real innovations are always different to what we are used to at that time. Thus the following text is a first rough outline of the vision:

  1. Find a format
  2. which allows any kinds of people
  3. for any kind of given problem
  4. with at least one vision of a possible improvement
  5. together
  6. to search and to find a path leading from the given problem (Now) to the envisioned improved state (future).
  7. For all needed communication any kind of  everyday language should be enough.
  8. As needed this everyday language should be extendable with special expressions.
  9. These considerations about possible paths into the wanted envisioned future state should continuously be supported  by appropriate automatic simulations of such a path.
  10. These simulations should include automatic evaluations based on the given envisioned state.
  11. As far as possible adaptive algorithms should be available to support the search, finding and identification of the best cases (referenced by the visions)  within human planning.

REFERENCES or COMMENTS

[1] One of the most common entangled state in daily life is the usage of normal language! A normal language L works only because the rules of usage of this language L are shared by all speaker-hearer of this language, and these rules are explicit cognitive structures (not necessarily conscious, mostly unconscious!).

Continuation

Yes, it will happen 🙂 Here.

 

 

 

 

 

 

CASE STUDIES

eJournal: uffmm.org
ISSN 2567-6458, 4.May  – 16.March   2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

In this section several case studies will  be presented. It will be shown, how the DAAI paradigm can be applied to many different contexts . Since the original version of the DAAI-Theory in Jan 18, 2020 the concept has been further developed centering around the concept of a Collective Man-Machine Intelligence [CM:MI] to address now any kinds of experts for any kind of simulation-based development, testing and gaming. Additionally the concept  now can be associated with any kind of embedded algorithmic intelligence [EAI]  (different to the mainstream concept ‘artificial intelligence’). The new concept can be used with every normal language; no need for any special programming language! Go back to the overall framework.

COLLECTION OF PAPERS

There exists only a loosely  order  between the  different papers due to the character of this elaboration process: generally this is an experimental philosophical process. HMI Analysis applied for the CM:MI paradigm.

 

JANUARY 2021 – OCTOBER 2021

  1. HMI Analysis for the CM:MI paradigm. Part 1 (Febr. 25, 2021)(Last change: March 16, 2021)
  2. HMI Analysis for the CM:MI paradigm. Part 2. Problem and Vision (Febr. 27, 2021)
  3. HMI Analysis for the CM:MI paradigm. Part 3. Actor Story and Theories (March 2, 2021)
  4. HMI Analysis for the CM:MI paradigm. Part 4. Tool Based Development with Testing and Gaming (March 3-4, 2021, 16:15h)

APRIL 2020 – JANUARY 2021

  1. From Men to Philosophy, to Empirical Sciences, to Real Systems. A Conceptual Network. (Last Change Nov 8, 2020)
  2. FROM DAAI to GCA. Turning Engineering into Generative Cultural Anthropology. This paper gives an outline how one can map the DAAI paradigm directly into the GCA paradigm (April-19,2020): case1-daai-gca-v1
  3. CASE STUDY 1. FROM DAAI to ACA. Transforming HMI into ACA (Applied Cultural Anthropology) (July 28, 2020)
  4. A first GCA open research project [GCA-OR No.1].  This paper outlines a first open research project using the GCA. This will be the framework for the first implementations (May-5, 2020): GCAOR-v0-1
  5. Engineering and Society. A Case Study for the DAAI Paradigm – Introduction. This paper illustrates important aspects of a cultural process looking to the acting actors  where  certain groups of people (experts of different kinds) can realize the generation, the exploration, and the testing of dynamical models as part of a surrounding society. Engineering is clearly  not  separated from society (April-9, 2020): case1-population-start-part0-v1
  6. Bootstrapping some Citizens. This  paper clarifies the set of general assumptions which can and which should be presupposed for every kind of a real world dynamical model (April-4, 2020): case1-population-start-v1-1
  7. Hybrid Simulation Game Environment [HSGE]. This paper outlines the simulation environment by combing a usual web-conference tool with an interactive web-page by our own  (23.May 2020): HSGE-v2 (May-5, 2020): HSGE-v0-1
  8. The Observer-World Framework. This paper describes the foundations of any kind of observer-based modeling or theory construction.(July 16, 2020)
  9. CASE STUDY – SIMULATION GAMES – PHASE 1 – Iterative Development of a Dynamic World Model (June 19.-30., 2020)
  10. KOMEGA REQUIREMENTS No.1. Basic Application Scenario (last change: August 11, 2020)
  11. KOMEGA REQUIREMENTS No.2. Actor Story Overview (last change: August 12, 2020)
  12. KOMEGA REQUIREMENTS No.3, Version 1. Basic Application Scenario – Editing S (last change: August 12, 2020)
  13. The Simulator as a Learning Artificial Actor [LAA]. Version 1 (last change: August 23, 2020)
  14. KOMEGA REQUIREMENTS No.4, Version 1 (last change: August 26, 2020)
  15. KOMEGA REQUIREMENTS No.4, Version 2. Basic Application Scenario (last change: August 28, 2020)
  16. Extended Concept for Meaning Based Inferences. Version 1 (last change: 30.April 2020)
  17. Extended Concept for Meaning Based Inferences – Part 2. Version 1 (last change: 1.September 2020)
  18. Extended Concept for Meaning Based Inferences – Part 2. Version 2 (last change: 2.September 2020)
  19. Actor Epistemology and Semiotics. Version 1 (last change: 3.September 2020)
  20. KOMEGA REQUIREMENTS No.4, Version 3. Basic Application Scenario (last change: 4.September 2020)
  21. KOMEGA REQUIREMENTS No.4, Version 4. Basic Application Scenario (last change: 10.September 2020)
  22. KOMEGA REQUIREMENTS No.4, Version 5. Basic Application Scenario (last change: 13.September 2020)
  23. KOMEGA REQUIREMENTS: From the minimal to the basic Version. An Overview (last change: Oct 18, 2020)
  24. KOMEGA REQUIREMENTS: Basic Version with optional on-demand Computations (last change: Nov 15,2020)
  25. KOMEGA REQUIREMENTS:Interactive Simulations (last change: Nov 12,2020)
  26. KOMEGA REQUIREMENTS: Multi-Group Management (last change: December 13, 2020)
  27. KOMEGA-REQUIREMENTS: Start with a Political Program. (last change: November 28, 2020)
  28. OKSIMO SW: Minimal Basic Requirements (last change: January 8, 2021)

 

 

ENGINEERING AND SOCIETY: The Role of Preferences

eJournal: uffmm.org,
ISSN 2567-6458, 4.May 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

FINAL HYPOTHESIS

This suggests that a symbiosis between creative humans and computing algorithms is an attractive pairing. For this we have to re-invent our official  learning processes in schools and universities to train the next generation of humans in a more inspired and creative usage of algorithms in a game-like learning processes.

CONTEXT

The overall context is given by the description of the Actor-Actor Interaction (AAI) paradigm as a whole.  In this text the special relationship between engineering and the surrounding society is in the focus. And within this very broad and rich relationship the main interest lies in the ethical dimension here understood as those preferences of a society which are more supported than others. It is assumed that such preferences manifesting themselves  in real actions within a space of many other options are pointing to hidden values which guide the decisions of the members of a society. Thus values are hypothetical constructs based on observable actions within a cognitively assumed space of possible alternatives. These cognitively represented possibilities are usually only given in a mixture of explicitly stated symbolic statements and different unconscious factors which are influencing the decisions which are causing the observable actions.

These assumptions represent  until today not a common opinion and are not condensed in some theoretical text. Nevertheless I am using these assumptions here because they help to shed some light on the rather complex process of finding a real solution to a stated problem which is rooted in the cognitive space of the participants of the engineering process. To work with these assumptions in concrete development processes can support a further clarification of all these concepts.

ENGINEERING AND SOCIETY

DUAL: REAL AND COGNITIVE

The relationship between an engineering process and the preferences of a society
The relationship between an engineering process and the preferences of a society

As assumed in the AAI paradigm the engineering process is that process which connects the  event of  stating a problem combined with a first vision of a solution with a final concrete working solution.

The main characteristic of such an engineering process is the dual character of a continuous interaction between the cognitive space of all participants of the process with real world objects, actions, and processes. The real world as such is a lose collection of real things, to some extend connected by regularities inherent in natural things, but the visions of possible states, possible different connections, possible new processes is bound to the cognitive space of biological actors, especially to humans as exemplars of the homo sapiens species.

Thus it is a major factor of training, learning, and education in general to see how the real world can be mapped into some cognitive structures, how the cognitive structures can be transformed by cognitive operations into new structures and how these new cognitive structures can be re-mapped into the real world of bodies.

Within the cognitive dimension exists nearly infinite sets of possible alternatives, which all indicate possible states of a world, whose feasibility is more or less convincing. Limited by time and resources it is usually not possible to explore all these cognitively tapped spaces whether and how they work, what are possible side effects etc.

PREFERENCES

Somehow by nature, somehow by past experience biological system — like the home sapiens — have developed   cultural procedures to induce preferences how one selects possible options, which one should be selected, under which circumstances and with even more constraints. In some situations these preferences can be helpful, in others they can  hide possibilities which afterwards can be  re-detected as being very valuable.

Thus every engineering process which starts  a transformation process from some cognitively given point of view to a new cognitively point of view with a following up translation into some real thing is sharing its cognitive space with possible preferences of  the cognitive space of the surrounding society.

It is an open case whether the engineers as the experts have an experimental, creative attitude to explore without dogmatic constraints the   possible cognitive spaces to find new solutions which can improve life or not. If one assumes that there exist no absolute preferences on account of the substantially limit knowledge of mankind at every point of time and inferring from this fact the necessity to extend an actual knowledge further to enable the mastering of an open unknown future then the engineers will try to explore seriously all possibilities without constraints to extend the power of engineering deeper into the heart of the known as well as unknown universe.

EXPLORING COGNITIVE POSSIBILITIES

At the start one has only a rough description of the problem and a rough vision of a wanted solution which gives some direction for the search of an optimal solution. This direction represents also a kind of a preference what is wanted as the outcome of the process.

On account of the inherent duality of human thinking and communication embracing the cognitive space as well as the realm of real things which both are connected by complex mappings realized by the brain which operates  nearly completely unconscious a long process of concrete real and cognitive actions is necessary to materialize cognitive realities within a  communication process. Main modes of materialization are the usage of symbolic languages, paintings (diagrams), physical models, algorithms for computation and simulations, and especially gaming (in several different modes).

As everybody can know  these communication processes are not simple, can be a source of  confusions, and the coordination of different brains with different cognitive spaces as well as different layouts of unconscious factors  is a difficult and very demanding endeavor.

The communication mode gaming is of a special interest here  because it is one of the oldest and most natural modes to learn but in the official education processes in schools and  universities (and in companies) it was until recently not part of the official curricula. But it is the only mode where one can exercise the dimensions of preferences explicitly in combination with an exploring process and — if one wants — with the explicit social dimension of having more than one brain involved.

In the last about 50 – 100 years the term project has gained more and more acceptance and indeed the organization of projects resembles a game but it is usually handled as a hierarchical, constraints-driven process where creativity and concurrent developing (= gaming) is not a main topic. Even if companies allow concurrent development teams these teams are cognitively separated and the implicit cognitive structures are black boxes which can not be evaluated as such.

In the presupposed AAI paradigm here the open creative space has a high priority to increase the chance for innovation. Innovation is the most valuable property in face of an unknown future!

While the open space for a real creativity has to be executed in all the mentioned modes of communication the final gaming mode is of special importance.  To enable a gaming process one has explicitly to define explicit win-lose states. This  objectifies values/ preferences hidden   in the cognitive space before. Such an  objectification makes things transparent, enables more rationality and allows the explicit testing of these defined win-lose states as feasible or not. Only tested hypothesis represent tested empirical knowledge. And because in a gaming mode whole groups or even all members of a social network can participate in a  learning process of the functioning and possible outcome of a presented solution everybody can be included.  This implies a common sharing of experience and knowledge which simplifies the communication and therefore the coordination of the different brains with their unconsciousness a lot.

TESTING AND EVALUATION

Testing a proposed solution is another expression for measuring the solution. Measuring is understood here as a basic comparison between the target to be measured (here the proposed solution) and the before agreed norm which shall be used as point of reference for the comparison.

But what can be a before agreed norm?

Some aspects can be mentioned here:

  1. First of all there is the proposed solution as such, which is here a proposal for a possible assistive actor in an assumed environment for some intended executive actors which has to fulfill some job (task).
  2. Part of this proposed solution are given constraints and non-functional requirements.
  3. Part of this proposed solution are some preferences as win-lose states which have to be reached.
  4. Another difficult to define factor are the executive actors if they are biological systems. Biological systems with their basic built in ability to act free, to be learning systems, and this associated with a not-definable large unconscious realm.

Given the explicit preferences constrained by many assumptions one can test only, whether the invited test persons understood as possible instances of the  intended executive actors are able to fulfill the defined task(s) in some predefined amount of time within an allowed threshold of making errors with an expected percentage of solved sub-tasks together with a sufficient subjective satisfaction with the whole process.

But because biological executive actors are learning systems they  will behave in different repeated  tests differently, they can furthermore change their motivations and   their interests, they can change their emotional commitment, and because of their   built-in basic freedom to act there can be no 100% probability that they will act at time t as they have acted all the time before.

Thus for all kinds of jobs where the process is more or less fixed, where nothing new  will happen, the participation of biological executive actors in such a process is questionable. It seems (hypothesis), that biological executing actors are better placed  in jobs where there is some minimal rate of curiosity, of innovation, and of creativity combined with learning.

If this hypothesis is empirically sound (as it seems), then all jobs where human persons are involved should have more the character of games then something else.

It is an interesting side note that the actual research in robotics under the label of developmental robotics is struck by the problem how one can make robots continuously learning following interesting preferences. Given a preference an algorithm can work — under certain circumstances — often better than a human person to find an optimal solution, but lacking such a preference the algorithm is lost. And actually there exists not the faintest idea how algorithms should acquire that kind of preferences which are interesting and important for an unknown future.

On the contrary, humans are known to be creative, innovative, detecting new preferences etc. but they have only limited capacities to explore these creative findings until some telling endpoint.

This suggests that a symbiosis between creative humans and computing algorithms is an attractive pairing. For this we have to re-invent our official  learning processes in schools and universities to train the next generation of humans in a more inspired and creative usage of algorithms in a game-like learning processes.

 

 

 

 

LIBRARIES AS ACTORS. WHAT ABOUT THE CITIZENS?

eJournal: uffmm.org, ISSN 2567-6458, 19.Januar 2019
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

In this blog a new approach to the old topic of ‘Human-Machine Interaction (HMI)’ is developed turning the old Human-Machine dyad into the many-to-many relation of ‘Actor-Actor Interaction (AAI)’. And, moreover, in this new AAI approach the classical ‘top-down’ approach of engineering is expanded with a truly ‘bottom-up’ approach locating the center of development in the distributed knowledge of a population of users assisted by the AAI experts.

PROBLEM

From this perspective it is interesting to see how on an international level the citizens of a community/ city are not at the center of research, but again the city and its substructures – here public libraries – are called ‘actors’ while the citizens as such are only an anonymous matter of driving these structures to serve the international ‘buzz word’ of a ‘smart city’ empowered by the ‘Internet of Things (IoT)’.

This perspective is published in a paper from Shannon Mersand et al. (2019) which reviews all the main papers available focusing on the role of public libraries in cities. It seems – I could not check by myself the search space — that the paper gives a good overview of this topic in 48 cited papers.

The main idea underlined by the authors is that public libraries are already so-called ‘anchor institutions’ in a community which either already include or could be extended as “spaces for innovation, collaboration and hands on learning that are open to adults and younger children as well”. (p.3312) Or, another formulation “that libraries are consciously working to become a third space; a place for learning in multiple domains and that provides resources in the form of both materials and active learning opportunities”. (p.3312)

The paper is rich on details but for the context of the AAI paradigm I am interested only on the general perspective how the roles of the actors are described which are identified as responsible for the process of problem solving.

The in-official problem of cities is how to organize the city to respond to the needs of its citizens. There are some ‘official institutions’ which ‘officially’ have to fulfill this job. In democratic societies these institutions are ‘elected’. Ideally these official institutions are the experts which try to solve the problem for the citizens, which are the main stakeholder! To help in this job of organizing the ‘best fitting city-layout’ there exists usually at any point of time a bunch of infrastructures. The modern ‘Internet of Things (IoT)’ is only one of many possible infrastructures.

To proceed in doing the job of organizing the ‘best fitting city-layout’ there are generally two main strategies: ‘top-down’ as usual in most cities or ‘bottom-‘ in nearly no cities.

In the top-down approach the experts organize the processes of the cities more or less on their own. They do not really include the expertise of their citizens, not their knowledge, not their desires and visions. The infrastructures are provided from a birds perspective and an abstract systems thinking.

The case of the public libraries is matching this top-down paradigm. At the end of their paper the authors classify public libraries not only as some ‘infrastructure’ but “… recognize the potential of public libraries … and to consider them as a key actor in the governance of the smart community”. (p.3312) The term ‘actor’ is very strong. This turns an institution into an actor with some autonomy of deciding what to do. The users of the library, the citizens, the primary stakeholder of the city, are not seen as actors, they are – here – the material to ‘feed’ – to use a picture — the actor library which in turn has to serve the governance of the ‘smart community’.

DISCUSSION

Yes, this comment can be understood as a bit ‘harsh’ because one can read the text of the authors a bit different in the sense that the citizens are not only some matter to ‘feed’ the actor library but to see the public library as an ‘environment’ for the citizens which find in the libraries many possibilities to learn and empower themselves. In this different reading the citizens are clearly seen as actors too.

This different reading is possible, but within an overall ‘top-down’ approach the citizens as actors are not really included as actors but only as passive receivers of infrastructure offers; in a top-down approach the main focus are the infrastructures, and from all the infrastructures the ‘smart’ structures are most prominent, the internet of things.

If one remembers two previous papers of Mila Gascó (2016) and Mila Gascó-Hernandez (2018) then this is a bit astonishing because in these earlier papers she has analyzed that the ‘failure’ of the smart technology strategy in Barcelona was due to the fact that the city government (the experts in our framework) did not include sufficiently enough the citizens as actors!

From the point of view of the AAI paradigm this ‘hiding of the citizens as main actors’ is only due to the inadequate methodology of a top-down approach where a truly bottom-up approach is needed.

In the Oct-2, 2018 version of the AAI theory the bottom-up approach is not yet included. It has been worked out in the context of the new research project about ‘City Planning and eGaming‘  which in turn has been inspired by Mila Gascó-Hernandez!

REFERENCES

  • S.Mersand, M. Gasco-Hernandez, H. Udoh, and J.R. Gil-Garcia. “Public libraries as anchor institutions in smart communities: Current practices and future development”, Proceedings of the 52nd Hawaii International Conference on System Sciences, pages 3305 – 3314, 2019. URL https: //hdl.handle.net/10125/59766 .

  • Mila Gascó, “What makes a city smart? lessons from Barcelona”. 2016 49th Hawaii International Conference on System Sciences (HICSS), pages 2983–2989, Jan 2016. D O I : 10.1109/HICSS.2016.373.

  • Mila Gascó-Hernandez, “Building a smart city: Lessons from Barcelona.”, Commun. ACM, 61(4):50–57, March 2018. ISSN 0001-0782. D O I : 10.1145/3117800. URL http://doi.acm.org/10.1145/3117800 .

THE BETTER WORLD PROJECT IDEA

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

Last changes: 9.Oct.2018 (Engineering part)

Author: Gerd Doeben-Henisch

Enhanced version of the 'Better World Project' Idea by making explicit the engineering part touching all other aspects
Enhanced version of the ‘Better World Project’ Idea by making explicit the engineering part touching all other aspects

 

The online-book project published on the uffmm.org website has to be seen within a bigger idea which can be named ‘The better world project’.

As outlined in the figure above you can see that the AAIwSE theory is the nucleus of a project which intends to enable a global learning space which connects individual persons as well as schools, universities, cities as well as companies, and even more if wanted.

There are other ideas around using the concept ‘better world’, butt these other concepts are targeting other subjects. In this view here the engineering perspective is laying the ground to build new more effective systems to enhance all aspects of life.

As you already can detect in the AAAIwSE theory published so far there exists a new and enlarged vision of the acting persons, the engineers as the great artists of the real world. Taking this view seriously there will be a need for a new kind of spirituality too which is enabling the acting persons to do all this with a vital interest in the future of life in the universe.

Actually the following websites are directly involved in the ‘Better World Project Idea’: this site ‘uffmm.org’ (in English)  and (in German)  ‘cognitiveagent.org‘ and ‘Kommunalpolitik & eGaming‘. The last link points to an official project of the Frankfurt University of Applied Sciences (FRA-UAS) which will apply the AAI-Methods to all communities in Germany (about 11.000).

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.