Category: sustainability

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} ⊢X  <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, Part 4: Tool based Actor Story Development with Testing and Gaming

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, March 3-4, 2021,
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: March 4, 2021, 07:49h (Minor corrections; relating to the UN SDGs)

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 4: Tool based Actor Story Development with Testing and Gaming

Context

This text is preceded by the following texts:

INFO GRAPH

Overview about different scenarios which will be possible for the development, simulation, testing and gaming of actor stories using the oksimo software tool

Introduction

In the preceding post it has been explained, how one can format an actor story [AS] as a theory in the  format  of  an Evaluated Theory Tε with Algorithmic Intelligence:   Tε,α=<M,∑,ε,α>.

In the following text it will be explained which kinds of different scenarios will be possible to elaborate, to simulate, to test, and to enable gaming with  an actor story theory by using the oksimo software tool.

UNIVERSAL TEAM

The classical distinctions between certain types of managers, special experts and the rest of the world is given up here in favor of a stronger generalization: everybody is a potential expert with regard to a future, which nobody knows. This is emphasized by the fact, that everybody can use its usual mother tongue, a normal language, every language. Nothing more is needed.

BASIC MODELS (S, X)

As minimal elements for all possible applications it is assumed here that the experts define at least a given situation (state) [S] and a set of change rules [X].

The given state S is  either (i)  taken as it is or (ii)  as a state which  should be improved. In both cases the initial state S is called the start state [S0].

The change rules X describe possible changes which transform a given state S into a changed successor state S’.

A pair of S and X as (S,X) is called a basic model M(S,X). One can define as many models as one wants.

A DIRECTION BY A VISION V

A vision [V] can describe a possible state SV  in an assumed future. If such a state SV is given, then this state becomes a goal state SGoal.  In this case  we assume V ≠ 0. If no explicit goal is given, then we assume V = 0.

DEVELOPMENT BY GOALS

If a vision is given (V ≠ 0), then the vision can be used to induce a direction which can/ shall be approached by creating a set X, which enables the generation of a sequence of states with the start state S0 as first state followed by successor state Si until the goal state SGoal has been reached or at least it holds that the goal state is a subset of the reached state: SGoal ⊆ Sn.

It is possible to use many basic models M(S,X) in parallel and for each model Mi one can define a different goal Vi (the typical situation in a pluralistic society).

Thus there can be many basic theories T(M,V) in parallel.

STEADY STATES (V = 0)

If no explicit visions are defined (V = 0) then every direction of change is allowed. A basic steady state theory T(M,V) with V = 0 can   be written as T(M,0). Whether such a case can be of interest is not clear at the moment.

BASIC INTERACTION PATTERNS

The following interaction modes are assumed as typical cases:

  1. N-1: Within an online session an interactive webpage with the oksimo software is active and the whole group can interact with the oksimo software tool.
  2. N-N-1: N-many participants can individually login into the interactive oksimo website and being logged in they can collaborate within the oksimo software with one project.
  3. N-N-N: N-many participants can individually login into the interactive oksimo website and there everybody can run its own process or can collaborate in various ways.

The default case is case (1). The exact dates for the availability of modes (2) – (3) depends from how fast the roadmap can be realized.

BASIC APPLICATIONS
  1. Exploring Simulation-Based Development [ESBD] (V ≠ 0): If the main goal is to find a path from a given state today S (Now) to an envisioned state V in the future then one has  to collect appropriate change rules X to approach the final goal state SGoal better and better. Activating the simulator ∑ during search and construction phase at will can be of great help, especially if the documents (S, X, V) are becoming more and more complex.
  2. Embedded Simulation-Based  Testing [ESBT] (V ≠ 0): If a basic  actor story theory T(M,∑) is given with a given goal (V ≠ 0) then it is of great help if the simulation is done in interactive mode where the simulator is not applying the change rules by itself but by asking different logged in users which rule they want to apply and how. These tests show not only which kinds of errors will occur but they can also show during n-many repetitions to which degree an user  can learn to behave task-conform. If the tests will not show the expected outcomes then this can point  to possible deficiencies of the software as well to specialties of the user.
  3. Embedded Simulation-Based Gaming [ESBTG] (V ≠ 0):  The case of gaming is partially  different to the case of testing.  Although it is assumed here too that at least one vision (goal) is given, it is additionally assumed that  there exists  a competition between different players or different teams. Different to testing exists in gaming according to the goal(s) the role of a winner: that player/ team which has reached a defined  goal state before the other player/ teams,  has won. As a side-effect of gaming one can also evaluate the playing environment and give some feedback to the developers.
ALGORITHMIC INTELLIGENCE
  1. Case ESBD, T(S,X,V,∑,ε,α): Because a normal simulation with the simulator ∑ always does  produce only one path from the start state to the goal state it is desirable to have an algorithm α which would run on demand as many times as wanted and thereby the algorithm α would search for all possible paths and at the same time it would look for those derivations, where the goal state satisfies with  ε certain special requirements. Thus the result from the application of α onto a given model M with the vision V would generate the set SV* of all those final states which satisfy the special requirements.
  2. Case ESBG, T(S,X,V,∑,ε,α):   The case of gaming allows at least three kinds of interesting applications for algorithmic intelligence: (i) Introduce non-biological players with learning capabilities which can act simultaneously with the biological players; (ii) Introduce non-biological players with learning capabilities which have to learn how to support, to assist, to train biological player. This second case addresses the challenging task to develop algorithmic tutors for several kinds of learning tasks. (iii) Another variant of case (ii) is to enable the development of a personal algorithmic assistant who works only with one person on a long-term basis.

The kinds of algorithmic Intelligence in (2)(i)-(iii) are different to the  mentioned algorithmic intelligence α in (1).

TYPES OF ACTORS

As the default standard case of an actor it is assumed that there are biological actors, usually human persons, which will not be analyzed with their inner structure [IS]. While the behavior of every system — and  therefore any biological system too — can be described with a behavior function φ: I x IS —> IS x O (if one has all the necessary knowledge), in the default case of biological systems  no behavior function φ is specified, φ = 0. During interactive simulations biological systems act by themselves.

If non-biological actors are used — e.g. automata with a certain machine program (an algorithm) — then one can use these only if one has a fully specified behavior function φ. From this follows that a  change rule which is associated with a non-biological actor has in its Eplus and in its Eminus part not a concrete expression but a variable, which will be computed during the simulation by the non-biological actor depending from its input and its behavior function φ: φ(input)IS=(Eplus, Eminus)IS.

FINAL COMMENT

Everybody who has read the parts (1) – (4) has now a general knowledge about the motivation to develop the oksimo software tool to support human kind to have a better communication and thinking of possible futures and a first understanding (hopefully :-)) how this tool can work. Reading the UN sustainable development goals [SDGs] [1] you will learn, that the SDG4 (Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all) is fundamental to all other SDGs. The oksimo software tool is one tool to be of help to reach these goals.

REFERENCES

[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

 

 

 

 

 

 

 

 

AAI THEORY V2 – USABILITY AND USEFULNESS

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

REMARK (5.May 2019)

This text  has to be reviewed again on account of the new aspect of gaming as  discussed in the post Engineering and Society.

CONTEXT

An overview of the enhanced AAI theory  version 2 you can find here.  In this post we talk about the sixth chapter dealing with usability and usefulness.

USABILITY AND USEFULNESS

In the AAI paradigm the concept of usability is seen as a sub-topic of the more broader concept of usefulness. Furthermore Usefulness  as well as usability are understood as measurements comparing some target with some presupposed norm.

Example: If someone wants to buy a product A whose prize fits well with the available budget and this product A shows only  an average usability then the product is probably ‘more useful’ for the buyer than another product B which does not fit with the budget although it  has a better usability. A conflict can  arise if the weaker value of the usability of product A causes during the usage of product A ‘bad effects’ onto the user of product A which in turn produce additional negative costs which enhance the original ‘nice price’ to a degree where the product A becomes finally  ‘more costly’ than product B.

Therefore  the concept usefulness will be  defined independently from the concept usability and depends completely  from the person or company who is searching for the solution of a problem. The concept of usability depends directly on the real structure of an  actor, a biological one or a non-biological one. Thus independent of the definition of the actual usefulness the given structure of an actor implies certain capabilities with regard to input, output as well as to  internal   processing. Therefore if an X seems to be highly useful for someone and to get X  needs a certain actor story to become realized with certain actors then it can matter whether this process includes a ‘good usability’ for the participating actors or not.

In the AAI paradigm both concepts usefulness as well as usability will be analyzed to provide a  chance to check the contributions of both concepts  in some predefined duration of usage. This allows the analysis of the sustainability of the wanted usefulness restricted to  usability as a parameter. There can be even more parameters   included in the evaluation of the actor story  to enhance the scope of   sustainability. Depending from the definition of the concept of resilience one can interpret the concept of sustainability used in this AAI paradigm as compatible with the resilience concept too.

MEASUREMENT

To speak about ‘usefulness’, ‘usability’, ‘sustainability’ (or ‘resilience’) requires some kind of a scale of values with an   ordering relation R allowing to state about  some values x,y   whether R(x,y) or R(y,x) or EQUAL(x,y). The values used in the scale have to be generated by some defined process P which is understood as a measurement process M which basically compares some target X with some predefined norm N and gives as a result a pair (v,N) telling a number v associated with the applied norm N. Written: M : X x N —> V x N.

A measurement procedure M must be transparent and repeatable in the sense that the repeated application of the measurement procedure M will generate the same results than before. Associated with the measurement procedure there can exist many additional parameters like ‘location’, ‘time’, ‘temperature’, ‘humidity’,  ‘used technologies’, etc.

Because there exist targets X which are not static it can be a problem when and how often one has to measure these targets to get some reliable value. And this problem becomes even worse if the target includes adaptive systems which are changing constantly like in the case of  biological systems.

All biological systems have some degree of learnability. Thus if a human actor is acting as part of an actor story  the human actor will learn every time he is working through the process. Thus making errors during his first run of the process does not imply that he will repeat these errors the next time. Usually one can observe a learning curve associated with n-many runs which show — mostly — a decrease in errors, a decrease in processing time, and — in general — a change of all parameters, which can be measured. Thus a certain actor story can receive a good usability value after a defined number of usages.  But there are other possible subjective parameters like satisfaction, being excited, being interested and the like which can change in the opposite direction, because to become well adapted to  the process can be boring which in turn can lead to less concentrations with many different negative consequences.