AN EMPIRICAL THEORY AS A DEVELOPMENT PROCESS

eJournal: uffmm.org
ISSN 2567-6458, 2.April 22 – 3.April 2022
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

BLOG-CONTEXT

This post is part of the Philosophy of Science theme which is part of the uffmm blog.

PREFACE

In a preceding post I have illustrated how one can apply the concept of an empirical theory — highly inspired by Karl Popper — to an everyday problem given as a county and its demographic problem(s). In this post I like to develop this idea a little more.

AN EMPIRICAL THEORY AS A DEVELOPMENT PROCESS

The figure shows a simplified outline of the idea of an empirical theory being realized during a development process based on the interactions and the communication of citizens as ‘natural experts’.

CITIZENs – natural experts

As starting point we assume citizens understood as our ‘natural experts’ being members of a democratic society with political parties, an freely elected parliament, which can create some helpful laws for the societal life and some authorities serving the need of the citizens.

SYMBOLIC DESCRIPTIONS

To coordinate their actions by a sufficient communication the citizens produce symbolic descriptions to make public how they see the ‘given situation’, which kinds of ‘future states’ (‘goals’) they want to achieve, and a list of ‘actions’ which can ‘change/ transform’ the given situation step wise into the envisioned future state.

LEVELS OF ABSTRACTIONS

Using an everyday language — possibly enriched with some math expressions – one can talk about our world of experience on different levels of abstraction. To get a rather wide scope one starts with most abstract concepts, and then one can break down these abstract concepts more and more with concrete properties/ features until these concrete expressions are ‘touching the real experience’. It can be helpful — in most cases — not to describe everything in one description but one does a partition of ‘the whole’ into several more concrete descriptions to get the main points. Afterwards it should be possible to ‘unify’ these more concrete descriptions into one large picture showing how all these concrete descriptions ‘work together’.

LOGICAL INFERENCE BY SIMULATION

A very useful property of empirical theories is the possibility to derive from given assumptions and assumed rules of inference possible consequences which are ‘true’ if the assumptions an the rules of inference are ‘true’.

The above outlined descriptions are seen in this post as texts which satisfy the requirements of an empirical theory such that the ‘simulator’ is able to derive from these assumptions all possible ‘true’ consequences if these assumptions are assumed to be ‘true’. Especially will the simulator deliver not only one single consequence only but a whole ‘sequence of consequences’ following each other in time.

PURE WWW KNOWLEDGE SPACE

This simple outline describes the application format of the oksimo software which is understood here as a kind of a ‘theory machine’ for everybody.

It is assumed that a symbolic description is given as a pure text file or as a given HTML page somewhere in the world wide web [WWW].

The simulator realized as an oksimo program can load such a file and can run a simulation. The output will be send back as an HTML page.

No special special data base is needed inside of the oksimo application. All oksimo related HTML pages located by a citizen somewhere in the WWW are constituting a ‘global public knowledge space’ accessible by everybody.

DISTRIBUTED OKSIMO INSTANCES

An oksimo server positioned behind the oksimo address ‘oksimo.com’ can produce for a simulation demand a ‘simulator instance’ running one simulation. There can be many simulations running in parallel. A simulation can also be connected in real time to Internet-of-Things [IoT] instances to receive empirical data being used in the simulation. In ‘interactive mode’ an oksimo simulation does furthermore allow the participation of ‘actors’ which function as a ‘dynamic rule instance’: they receive input from the simulated given situation and can respond ‘on their own’. This turns a simulation into an ‘open process’ like we do encounter during ‘everyday real processes’. An ‘actor’ must not necessarily be a ‘human’ actor; it can also be a ‘non-human’ actor. Furthermore it is possible to establish a ‘simulation-meta-level’: because a simulation as a whole represents a ‘full theory’ on can feed this whole theory to an ‘artificial intelligence algorithm’ which dos not run only one simulation but checks the space of ‘all possible simulations’ and thereby identifies those sub-spaces which are — according to the defined goals — ‘zones of special interest’.

OKSIMO APPLICATIONS – Simple Examples – Citizens of a County

eJournal: uffmm.org ISSN 2567-6458

27.March 2022 – 27.March 2022
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

BLOG-CONTEXT

This post is part of the Oksimo Application theme which is part of the uffmm blog.

PREFACE

This post shows a simple simulation example with the beta-version of the new Version 2 of the oksimo programming environment. This example shall illustrate the concept of an ‘Everyday Empirical Theory‘ as described in this blog 11 days before. It is intentionally as ‘simple as possible’. Probably some more examples will be shown.

FROM THEORY TO AN APPLICATION

To apply a theory concept in an everyday world there are many formats possible. In this text it will be shown how such an application would look like if one is applying the oksimo programming environment. Until now there exists only a German Blog (oksimo.org) describing the oksimo paradigm a little bit. But the examples there are written with oksimo version 1, which didn’t allow to use math. In version 2 this is possible, accompanied by some visual graph features.

Everyday Experts – Basic Ideas

This figure shows a simple outline of the basic assumptions of the oksimo programming environment constituting the oksimo paradigm: (i) Every human person is assumed to be a ‘natural expert’ being member of a bigger population which shares the same ‘everyday language’ including basic math. (ii) An actor is embedded in some empirical environment including the own body and other human actors. (iii) Human actors are capable of elaborating as inner states different kinds of ‘mental (cognitive) models’ based on their experience of the environment and their own body. (iv) Human actors are further capable to use symbolic languages to ‘represent’ properties of these mental models encoded in symbolic expressions. Such symbolic encoding presupposes an ‘inner meaning function’ which has to be learned. (v) In the oksimo programming environment one needs for the description of a ‘given state’ two kinds of symbolic expressions: (v.1) Language expressions to describe general properties and relations which are assumed to be ‘given’ (= ‘valid by experience’); (v.2) Language expressions to name concrete quantitative properties (simple math expressions).

This figure shows the idea how to change a given state (situation) by so-called ‘change rules’. A change rule encodes experience from the everyday world under which conditions some properties of a given situation S can be ‘changed’ in a way, that a ‘new situation’ S* comes into being. Generally a given state can change if either language expression is ‘deleted’ from the description or ‘contributed’. Another possibility is realized if one of the given quantitative expressions changes its value. In the above simple situation the only change happens by changing the number of citizens by some growth effect. But, as other examples will demonstrate, everything is possible what is possible in the empirical world.

SOME MORE FEATURES

The basic schema of the oksimo paradigm assumes the following components:

  1. The description of a ‘given situation’ as a ‘start state’.
  2. The description of a ‘vision’ functioning as a ‘goal’ which allows a basic ‘Benchmarking’.
  3. A list of ‘change rules’ which describe the assumed possible changes
  4. An ‘inference engine’ called ‘simulator’: Depending from the number of wanted ‘simulation cycles’ (‘inferences’) the simulator applies the change rules onto a given state S and thereby it is producing a ‘follow up state’ S*, which becomes the new given state. The series of generated states represents the ‘history’ of a simulation. Every follow up state is an ‘inference’ and by definition also a ‘forecast’.

All these features (1) – (4) together constitute a full empirical theory in the sense of the mentioned theory post before.

Let us look to a real simulation.

A REAL SIMULATION

The following example has been run with Oksimo v2.0 (Pre-Release) (353e5). Hopefully we can finish the pre-release to a full release the next few weeks.

A VISION

Name: v2026

Expressions:

The Main-Kinzig County exists.

Math expressions:

YEAR>2025 and YEAR<2027

This simple goal assumes the existence of the Main-Kinzig County for the year 2026.

GIVEN START STATE

Name: StartSimple1

Expressions:

The Main-Kinzig County exists.

The number of citizens is known.

Comparing the number of different years one has computed a growth rate.

Math expressions:

YEAR=2018Number

CITIZENS=418950Amount

GROWTH=0.0023Percentage

The start state makes some simple statements which are assumed to be ‘valid’ in a ‘real given situation’ by the participating natural experts.

CHANGE RULES

In this example there is only one change rules (In principle there can be as many change rules as wanted).

Rule name: Growth1

Probability: 1.0

Conditions:

The Main-Kinzig County exists.

Math conditions:

CITIZENS < 450000

Effects plus:

Effects minus:

Effects math:

CITIZENS=CITIZENS+(CITIZENS*GROWTH)

YEAR=YEAR+1

This change rules is rather simple. It looks only to the fact whether the Main-Kinzig County exists and wether the number of citizens is still below 450000. If this is the case, then the year will be incremented and the number of citizens will be incremented according to an extremely simple formula.

For every named quantity in this simulation (YEAR, GROWTH, CITIZENS) the values are collected for every simulation cycle and therefore can be used for evaluations. In this simple case only the quantities of YEAR and CITIZENS have changes:

Simple linear graph for the quantity named YEAR
Simple linear graph for the quantity named CITIZENS

Here the quick log of simulation cycle round 7 – 9:

Round 7

State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2025Number
CITIZENS: 425741.8149741673Amount
GROWTH: 0.0023Percentage

50.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
None

Round 8

State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2026Number
CITIZENS: 426721.0211486079Amount
GROWTH: 0.0023Percentage

100.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
YEAR>2025 and YEAR<2027,

Round 9

State rules:
Vision rules:
Current states: The number of citizens is known.,Comparing the number of different years one has computed a growth rate.,The Main-Kinzig County exists.
Current visions: The Main-Kinzig County exists.
Current values:
YEAR: 2027Number
CITIZENS: 427702.4794972497Amount
GROWTH: 0.0023Percentage

50.00 percent of your vision was achieved by reaching the following states:
The Main-Kinzig County exists.,
And the following math visions:
None

In round 8 one can see, that the simulation announces:

100.00 percent of your vision was achieved by reaching the following states: The Main-Kinzig County exists., And the following math visions: YEAR>2025 and YEAR<2027

From this the natural expert can conclude that his requirements given in the vision are ‘fulfilled’/’satisfied’.

WHAT COMES NEXT?

In a loosely order more examples will follow. Here you find the next one.

THE OKSIMO CASE as SUBJECT FOR PHILOSOPHY OF SCIENCE. Part 5. Oksimo as Theory?

eJournal: uffmm.org
ISSN 2567-6458, 24.March – 24.March 2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

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.

DERIVATION

In formal logic exists the concept of logical derivation ‘⊢’ written as

EX e

saying that one can get the expression e out of the set of expressions E by applying the rules X.

In the oksimo case we have sets of expressions ES to represent either a given starting state S or to represent as EV a given vision V. Furthermore  we have change rules X operating on sets of expressions and we can derive sequences of states of expressions <E1, E2, …, En> by applying the change rules X with the aid of a simulator Σ onto these expressions written as

ESΣ,X <E1, E2, …, En>

Thus given an initial set of expressions ES one can derive a whole sequence of expression sets Ei by applying the change rules.

While all individual expressions of the start set ES are by assumption classified as true it holds for the derived sets of expressions Ei  that these expressions are correct with regard to the used change rules X but whether these sets of expressions are also true with regard to a given  situation Si considered as a possible future state Sfuti has to be proved separately! The reason for this unclear status results from the fact that the change rules X represent changes which the authoring experts consider as possible changes which they want to apply but they cannot guarantee the empirical validity for all upcoming times   only by thinking. This implicit uncertainty can be handled a little bit with the probability factor π of an individual change rule. The different degrees of certainty in the application of a change rule can give an approximation of this uncertainty. Thus as longer the chain of derivations is becoming as lower the assumed probability will develop.

SIMPLE OKSIMO THEORY [TOKSIMO]

Thus if we have some human actors Ahum, an environment ENV, some starting situation S as part of the environment ENV, a first set of expressions ES representing only true expressions with regard to the starting situation S, a set of elaborated change rules X, and a simulator Σ then one can  define a simple  oksimo-like theory Toksimo as follows:

TOKSIMO(x) iff x = <ENV, S, Ahum, ES, X, Σ, ⊢Σ,X, speakL(), makedecidable()>

The human actors can describe a given situation S as part of an environment ENV as a set of expressions ES which can be proved with makedecidable() as true. By defining a set of change rules X and a simulator Σ one can define  a formal derivation relation Σ,X which allows the derivation of a sequence of sets of expressions <E1, E2, …, En> written as

EST,Σ,X <E1, E2, …, En>

While the truth of the first set of expressions ES has been proved in the beginning, the truth of the derived sets of expressions has to be shown explicitly for each set Ei separately. Given is only the formal correctness of the derived expressions according to the change rules X and the working of the simulator.

VALIDADED SIMPLE OKSIMO THEORY [TOKSIMO.V]

One can extend the simple oksimo theory TOKSIMO to a biased  oksimo theory TOKSIMO.V if one includes in the theory a set of vision expressions EV. Vision expressions can describe a possible situation in the future Sfut which is declared as a goal to be reached. With a given vision document EV the simulator can check for every new derived set of expressions Ei to which degree the individual expressions e of the set of vision expressions EV are already reached.

FROM THEORY TO ENGINEERING

But one has to keep in mind that the purely formal achievement of a given vision document EV does not imply that the corresponding situation Sfut    is a real situation.  The corresponding situation Sfut  is first of all only an idea in the mind of the experts.  To transfer this idea into the real environment as a real situation is a process on its own known as engineering.

 

THE OKSIMO CASE as SUBJECT FOR PHILOSOPHY OF SCIENCE. Part 4. Describing Change

eJournal: uffmm.org
ISSN 2567-6458, 24.March – 24.March 2021
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

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.

CHANGE

AS described in part 1 of the philosophy of science analysis of the oksimo behavior space it is here assumed — following  the ideas of  von Uexküll — that every biological species SP embedded in a real environment ENV transforms this environment  in its species specific internal representation  ENVSP which is no 1-to-1 mapping. Furthermore we know from modern Biology and brain research that the human brain cuts its sensory perceptions P into time-slices P1, P2, … which have durations between about 50 – 700 milliseconds and which are organized as multi-modal structures for further processing. The results of this processing are different kinds of abstracted structures which represent — not in a 1-to-1 fashion — different aspects of a given situation S which   in the moment of being processed and then being stored is not any longer actual, ‘not now’, but ‘gone‘, ‘past‘.

Thus if we as human actors are speaking about change then we are primarily speaking about the difference which our brain can compute comparing the actual situation S being kept in an actual time-slice P0 and those abstracted structures A(P) coming out of preceding time slices interacting in many various ways with other available abstracted structures:  Diff(A(P0), A(P)) = Δint. Usually we assume automatically that the perceived internal change Δint corresponds to a change in the actual situation S leading to a follow-up situation S’ which differs with regard to the species specific perception represented in Δint as Δext = Diff(S, S’). As psychological tests can  reveal  this automatic (unconscious) assumption that a perceived change Δint corresponds to a real external change Δext must not be the case. There is a real difference between Δint, Δext and on account of this difference there exists the possibility that we can detect an error  comparing our ideas with the real world environment. Otherwise — in the absence of an error —  a congruence can be interpreted as a confirmation of our ideas.

EXPRESSIONS CAN FOLLOW REAL PROPERTIES

As described in the preceding posts about a decidable start state S and a vision V  it is possible to map a perceived actual situation S in a set of expressions ES={e1, e2, …, en }. This general assumption is valid for all real states S, which results in the fact that a series of real states S1, S2, …, Sn is conceivable where every such real state Si can be associated with a set of expressions Ei which contain individual expressions ei which represent according to the presupposed meaning function φ certain aspects/ properties Pi of the corresponding real situation Si.  Thus, if two consecutive real states Si, Si+1 are include perceived  differences  indicated by some properties then it is possible to express these differences by corresponding expressions ei as part of the whole set of expressions Ei and Ei+1. If e.g. in the successor of Si one property px expressed by ex  is missing which is present in Si then the corresponding set Ei+1 should not include the expression ex. Or if the successor state Si+1 contains a property py expressed by the expression ey which is not yet given in Si then this fact too indicates a difference. Thus the differing pair (Si, Si+1)  could correspond to the pair (Ei, Ei+1) with ex as part of Ei but not any more in Ei+1 and the expression ey not part of Ei but then in Ei+1.

The general schema could be described as:

Si+1 = Si -{px} + {py} (the real dimension)

Ei+1 = Ei – {ex} + {ey} (the symbolic dimension)

Between the real dimension and the symbolic dimension is the body with the brain offering all the neural processing which is necessary to enable such complex mappings. This can bne expressed by the following pragmatic recipe:

symbolicarticulation: S x body[brain] —> E

symbolicarticulation(S,body[brain]) = E

Having a body with a brain embedded in an actual (real) situation S the body (with the brain) can produce symbolic expressions corresponding to certain properties of the situation S.

DESCRIBING CHANGE

Assuming that symbolic articulation is possible and that there is some regular mapping between an actual situation S and a set of expressions E it is conceivable to describe the generation of two successive actual states S, S’  as follows:

Apply a Change Rule ξ of X
  • We have a given actual situation S.
  • We have a group of human actors Ahum which are using a language L.
  • The group generates a decidable description of S as a set of expressions ELS following the rules of language L.
  • Thus we have symbolicarticulation(S, Ahum) = ELS
  • The group of human actors defines a finite set of change rules X which describe which expressions Eminus should be removed from ES and which expressions Eplus should be added to ES to get the successor state  ES‘ represented in a symbolic space:
  • ES‘ = ES – Eminus + Eplus . An individual change rule ξ of X has the format:
  • IF COND THEN with probability π REMOVE Eminus and ADD Eplus.
  • COND is a set of expressions which shall be a subset of the given set ES saying: COND ⊆ ES. If this condition is satisfied (fulfilled) then the rule can be applied following probability  π.
  • Thus applying a change rule ξ to a given state S means to operate on the corresponding set of expressions ES of  S as follows:
  • applychange: S x ES x {ξ}    —> ES
  • There can be more than only one change rule ξ as a finite set X = {ξ1, ξ2, …, ξn}. They have all to be applied in a random order. Thus we get:
  • applychange: S x ES x X   —> ES‘ or applychange(S,ES,X) = ES
Simulation

If one has a given actual state S with a finite set of change rules X we know now how to apply this finite set of change rules X to a given state description  ES. But if we would enlarge the set of change rules X in a way that this set X* not only contains rules for the given actual state description ES but also for a finite number of other possible state descriptions ES* then one could repeat the application of the change rules X* several times by using the last outcome desribing ES‘ to make ES‘ to the new actual state description ES. Proceeding in this way we can generate a whole sequence of state decriptions: <ES.0, ES.1, …, ES.n> where for each pair (ES.i, ES.i+1) it holds that  applychange(Si,ES.i,X) = ES.i+1

Such a repetitive application of the applychange() rule we call here a simulation: S x ES x X   —> <ES.0, ES.1, …, ES.n> with the condition  for each pair (ES.i, ES.i+1) that it holds that  applychange(Si,ES.i,X) = ES.i+1also written as: simulation(S , ES, X) = <ES.0, ES.1, …, ES.n>.

A device which can operate a simulation is called a simulator ∑. A simulator is either a human actor or a computer with an appropriate algorithm.

 

HMI Analysis for the CM:MI paradigm. Part 3. Actor Story and Theories

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, March 2, 2021,
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Last change: March 2, 2021 13:59h (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 3: Actor Story and  Theories

Context

This text is preceded by the following texts:

Introduction

Having a vision is that moment  where something really new in the whole universe is getting an initial status in some real brain which can enable other neural events which  can possibly be translated in bodily events which finally can change the body-external outside world. If this possibility is turned into reality than the outside world has been changed.

When human persons (groups of homo sapiens specimens) as experts — here acting as stakeholder and intended users as one but in different roles! — have stated a problem and a vision document, then they have to translate these inevitably more fuzzy than clear ideas into the concrete terms of an everyday world, into something which can really work.

To enable a real cooperation  the experts have to generate a symbolic description of their vision (called specification) — using an everyday language, possibly enhanced by special expressions —  in a way that  it can became clear to the whole group, which kind of real events, actions and processes are intended.

In the general case an engineering specification describes concrete forms of entanglements of human persons which enable  these human persons to cooperate   in a real situation. Thereby the translation of  the vision inside the brain  into the everyday body-external reality happens. This is the language of life in the universe.

WRITING A STORY

To elaborate a usable specification can metaphorically be understood  as the writing of a new story: which kinds of actors will do something in certain situations, what kinds of other objects, instruments etc. will be used, what kinds of intrinsic motivations and experiences are pushing individual actors, what are possible outcomes of situations with certain actors, which kind of cooperation is  helpful, and the like. Such a story is  called here  Actor Story [AS].

COULD BE REAL

An Actor Story must be written in a way, that all participating experts can understand the language of the specification in a way that   the content, the meaning of the specification is either decidable real or that it eventually can become real.  At least the starting point of the story should be classifiable as   being decidable actual real. What it means to be decidable actual real has to be defined and agreed between the participating experts before they start writing the Actor Story.

ACTOR STORY [AS]

An Actor Story assumes that the described reality is classifiable as a set of situations (states) and  a situation as part of the Actor Story — abbreviated: situationAS — is understood  as a set of expressions of some everyday language. Every expression being part of an situationAS can be decided as being real (= being true) in the understood real situation.

If the understood real situation is changing (by some event), then the describing situationAS has to be changed too; either some expressions have to be removed or have to be added.

Every kind of change in the real situation S* has to be represented in the actor story with the situationAS S symbolically in the format of a change rule:

X: If condition  C is satisfied in S then with probability π  add to S Eplus and remove from  S Eminus.

or as a formula:

S’π = S + Eplus – Eminus

This reads as follows: If there is an situationAS S and there is a change rule X, then you can apply this change rule X with probability π onto S if the condition of X is satisfied in S. In that case you have to add Eplus to S and you have to remove Eminus from S. The result of these operations is the new (successor) state S’.

The expression C is satisfied in S means, that all elements of C are elements of S too, written as C ⊆ S. The expression add Eplus to S means, that the set Eplus is unified with the set S, written as Eplus ∪ S (or here: Eplus + S). The expression remove Eminus from S means, that the set Eminus is subtracted from the set S, written as S – Eminus.

The concept of apply change rule X to a given state S resulting in S’ is logically a kind of a derivation. Given S,X you will derive by applicating X the new  S’. One can write this as S,X ⊢X S’. The ‘meaning’ of the sign ⊢  is explained above.

Because every successor state S’ can become again a given state S onto which change rules X can be applied — written shortly as X(S)=S’, X(S’)=S”, … — the repeated application of change rules X can generate a whole sequence of states, written as SQ(S,X) = <S’, S”, … Sgoal>.

To realize such a derivation in the real world outside of the thinking of the experts one needs a machine, a computer — formally an automaton — which can read S and X documents and can then can compute the derivation leading to S’. An automaton which is doing such a job is often called a simulator [SIM], abbreviated here as ∑. We could then write with more information:

S,X ⊢ S’

This will read: Given a set S of many states S and a set X of change rules we can derive by an actor story simulator ∑ a successor state S’.

A Model M=<S,X>

In this context of a set S and a set of change rules X we can speak of a model M which is defined by these two sets.

A Theory T=<M,>

Combining a model M with an actor story simulator enables a theory T which allows a set of derivations based on the model, written as SQ(S,X,⊢) = <S’, S”, … Sgoal>. Every derived final state Sgoal in such a derivation is called a theorem of T.

An Empirical Theory Temp

An empirical theory Temp is possible if there exists a theory T with a group of experts which are using this theory and where these experts can interpret the expressions used in theory T by their built-in meaning functions in a way that they always can decide whether the expressions are related to a real situation or not.

Evaluation [ε]

If one generates an Actor Story Theory [TAS] then it can be of practical importance to get some measure how good this theory is. Because measurement is always an operation of comparison between the subject x to be measured and some agreed standard s one has to clarify which kind of a standard for to be good is available. In the general case the only possible source of standards are the experts themselves. In the context of an Actor Story the experts have agreed to some vision [V] which they think to be a better state than a  given state S classified as a problem [P]. These assumptions allow a possible evaluation of a given state S in the ‘light’ of an agreed vision V as follows:

ε: V x S —> |V ⊆ S|[%]
ε(V,S) = |V ⊆ S|[%]

This reads as follows: the evaluation ε is a mapping from the sets V and S into the number of elements from the set V included in the set S converted in the percentage of the number of elements included. Thus if no  element of V is included in the set S then 0% of the vision is realized, if all elements are included then 100%, etc. As more ‘fine grained’ the set V is as more ‘fine grained’  the evaluation can be.

An Evaluated Theory Tε=<M,,ε>

If one combines the concept of a  theory T with the concept of evaluation ε then one can use the evaluation in combination with the derivation in the way that every  state in a derivation SQ(S,X,⊢) = <S’, S”, … Sgoal> will additionally be evaluated, thus one gets sequences of pairs as follows:

SQ(S,X,⊢∑,ε) = <(S’,ε(V,S’)), (S”,ε(V,S”)), …, (Sgoal, ε(V,Sgoal))>

In the ideal case Sgoal is evaluated to 100% ‘good’. In real cases 100% is only an ideal value which usually will only  be approximated until some threshold.

An Evaluated Theory Tε with Algorithmic Intelligence Tε,α=<M,,ε,α>

Because every theory defines a so-called problem space which is here enhanced by some evaluation function one can add an additional operation α (realized by an algorithm) which can repeat the simulator based derivations enhanced with the evaluations to identify those sets of theorems which are qualified as the best theorems according to some criteria given. This operation α is here called algorithmic intelligence of an actor story AS]. The existence of such an algorithmic intelligence of an actor story [αAS] allows the introduction of another derivation concept:

S,X ⊢∑,ε,α S* ⊆  S’

This reads as follows: Given a set S and a set X an evaluated theory with algorithmic intelligence Tε,α can derive a subset S* of all possible theorems S’ where S* matches certain given criteria within V.

WHERE WE ARE NOW

As it should have become clear now the work of HMI analysis is the elaboration of a story which can be done in the format of different kinds of theories all of which can be simulated and evaluated. Even better, the only language you have to know is your everyday language, your mother tongue (mathematics is understood here as a sub-language of the everyday language, which in some special cases can be of some help). For this theory every human person — in all ages! — can be a valuable  colleague to help you in understanding better possible futures. Because all parts of an actor story theory are plain texts, everybody ran read and understand everything. And if different groups of experts have investigated different  aspects of a common field you can merge all texts by only ‘pressing a button’ and you will immediately see how all these texts either work together or show discrepancies. The last effect is a great opportunity  to improve learning and understanding! Together we represent some of the power of life in the universe.

CONTINUATION

See here.

 

 

 

 

 

 

 

 

OKSIMO SW – Minimal Basic Requirements

Integrating Engineering and the Human Factor (info@uffmm.org)
eJournal uffmm.org ISSN 2567-6458, January 8, 2021
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

As described in the uffmm eJournal  the wider context of this software project is an integrated  engineering theory called Distributed Actor-Actor Interaction [DAAI]. This includes Human Machine Intelligence [HMIntelligence]  as part of Human Machine Interaction [HMI]. In  the section Case Studies of the uffmm eJournal there is also a section about Python co-learning – mainly dealing with python programming – and a section about a web-server with Dragon. This document is part of the Case Studies section.

CONTENT

In the long way of making the theory  as well as the software [SW] more concrete we have reached January 5, 2021 a first published version on [www.]oksimo.com.  This version contains a sub-part of the whole concept which I call here the Minimal Basic Version [MBV] of the osimo SW. This minimal basic will be tested until the end of february 2021. Then we will add stepwise all the other intended features.

THE MINIMAL BASIC VERSION

oksimo SW Minimal Basic Version Jan 3, 2021
oksimo SW Minimal Basic Version Jan 3, 2021

If one compares this figure with the figure of the Multi-Group Management from Dec 5, 2020 one can easily detect simplifications for the first modul now called Vision [V] as well as for the last modul called Evaluation [EVAL].

While the basic modules States [S], Change Rules [X] and Simulator [SIM] stayed the same the mentioned first and last module have slightly changed in the sense that they have become simplified.

During the first tests with the oksimo reloaded SW it became clear that for a simulation unified with evaluation  it is sufficient to have at least one vision V to be compared with an actual state S whether parts of the vision V are also part of the state S. This induced the requirement that a vision V has to be understood as a collection of statements where earch statement describes some aspect of a vision as a whole.

Example 1: Thus a global vision of a city to have a ‘Kindergarten’ could be extended with facts like ‘It is free for all children’, ‘I is constructed in an ecological acceptable manner’, …

Example 2: A global vision to have a system interface [SI] for the oksimo reloaded SW could include statements (facts) like: ‘The basic mode is text input in an everyday language’, ‘In an advanced mode you can use speech-recognition tools to enter a text into the system’, ‘The basic mode of the simulation output is text-based’, ‘In an advanced mode you can use text-to-speech SW to allow audio-output of the simulation’, ….

Vision V – Statement S: The citizen which will work with the oksimo reloaded SW has now only to distinguish between the vision V which points into some — as such — unknown future and the given situation S describing some part of the everyday world. The vision with all its possible different partial views (statements, facts) can then be used to a evaluate a given state S whether the vision is already part of it or not. If during a simulation a state S* has been reached and the global vision ‘The city has a Kindergarten’ is part of S*  but not the partial aspects ‘It is free for all children’, ‘I is constructed in an ecological acceptable manner’,  then only one third of the vision has been fulfilled: eval(V,S*)= 33,3 … %. As one can see the amount of vision facts determines the fineness of the evaluation.

Requirements Point of View: In Software Engineering [SWE] and — more general — in Human-Machine Interaction [HMI] as part of System Engineering [SE] the analysis phase is characterized by a list of functional and non-functional requirements [FR, NFR]. Both concepts are in the oksimo SW parts of the vision modul. Everything you think of  to be important for your vision you can write down as some aspect of the vision.  And if you want to structure your vision into several parts you can edit different vision documents which for a simulation can be united to one document again.

Change Rules [X]: In the minimal basic version only three components of a change rule X will be considered: The condition [COND] part which checks whether an actual state S satisfies (fulfills)  the condition; the Eplus part which contains facts which shall be added to the actual state S for the next turn; the Eminus part which contains facts which shall be removed from the actual state S für the next turn. Other components like Probability [PROB] or Model [MODEL] will be added in the future.

KOMEGA REQUIREMENTS: Interactive Simulations

Integrating Engineering and the Human Factor (info@uffmm.org) eJournal uffmm.org ISSN 2567-6458, Nov 12, 2020
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

CONTENT

Introducing the interactive mode of simulation besides the existing
passive mode.

PDF DOCUMENT

requirements-interactive-simulations12nov2020

KOMEGA REQUIREMENTS: From the minimal to the basic version

ISSN 2567-6458, 18.October  2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

CONTENT

Here we present the ideas how to extend the minimal version to a first basic version. At least two more advanced levels will follow.

VIDEO (EN)

(Last change: Oct 17, 2020)

VIDEO(DE)

(last change: Oct 18, 2020)

KOMEGA REQUIREMENTS No.4, Version 1

ISSN 2567-6458, 26.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

PDF DOCUMENT

requirements-no4-v1-26Aug2020

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

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

CONTEXT

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

Abstract

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

PDF DOCUMENT

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

KOMEGA REQUIREMENTS No.3, Version 1. Basic Application Scenario – Editing S

ISSN 2567-6458, 26.July – 12.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

PDF DOCUMENT

requirements-no3-v1-12Aug2020 (Last update: August 12, 2020)

KOMEGA REQUIREMENTS No.2. Actor Story Overview

ISSN 2567-6458, 26.July – 12.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

PDF DOCUMENT

requirements-no2-v1-11Aug2020 (Last change: August 12, 2020)

REVIEWING TARSKI’s SEMANTIC and MODEL CONCEPT. 85 Years Later …

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

85 Years Later

The two papers of Tarski, which I do discuss here, have been published in 1936. Occasionally I have already read these paper many years ago but at that time I could not really work with these papers. Formally they seemed to be ’correct’, but in the light of my ’intuition’ the message appeared to me somehow ’weird’, not really in conformance with my experience of how knowledge and language are working in the real world. But at that time I was not able to explain my intuition to myself sufficiently. Nevertheless, I kept these papers – and some more texts of Tarski – in my bookshelves for an unknown future when my understanding would eventually change…
This happened the last days.

review-tarski-semantics-models-v1-printed

BACK TO REVIEWING SECTION

Here

 

CASE STUDY 1. FROM DAAI to ACA. Transforming HMI into ACA (Applied Cultural Anthropology)

eJournal: uffmm.org
ISSN 2567-6458, 28.July 2020
Email: info@uffmm.org

Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Abstract

The collection of papers in the Case Studies Section deals with the
possible applications of the general concept of a GCA Generative Cul-
tural Anthropology to all kinds of cultural processes. The GCA paradigm
has been derived from the formalized DAAI Distributed Actor-Actor In-
teraction theory, which in turn is a development based on the common
HMI Human Machine Interaction paradigm reformulated within the Sys-
tems Engineering paradigm. The GCA is a very general and strong theory
paradigm, but, saying this, it is for most people difficult to understand,
because it is highly interdisciplinary, and it needs some formal technical
skills, which are not too common. During the work in the last three
months it became clear, that the original HMI and DAAI approach can
also be understood as the case of something which one could call ACA
Applied Cultural Anthropology as part of an GCA. The concept of ACA
is more or less directly understandable for most people.

case1-daai-aca-v1

KOMEGA REQUIREMENTS No.1. Basic Application Scenario

KOMEGA REQUIREMENTS No.1. Basic Application Scenario

ISSN 2567-6458, 26.July – 11.August 2020
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

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

PDF TEXT:

requirements-no1-v3-11Aug2020 (published: Aug-11, 2020; this version replaces the version from 7.August 2020)

requirements-no1-v2-2-7Aug2020 (published: Aug-7, 2020; this version replaces the version from 6.August 2020)

requirements-no1-v2-6Aug2020 (published: Aug-6, 2020; this version replaces the version from 25.July 2020)

requirements-no1-25july2020-v1-pub (published: July-26, 2020)