Category: change rules

FORECASTING – PREDICTION: What?

eJournal: uffmm.org
ISSN 2567-6458, 19.August 2022 – 25 August 2022, 14:26h
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

This text is part of the subject COMMON SCIENCE as Sustainable Applied Empirical Theory, besides ENGINEERING, in a SOCIETY. It is a preliminary version, which is intended to become part of a book.

FORECASTING – PREDICTION: What?

optimal prediction

In the introduction of the main text it has been underlined that within a sustainable empirical theory it is not only necessary to widen the scope with a maximum of diversity, but at the same time it is also necessary to enable the capability for an optimal prediction about the ‘possible states of a possible future’.

the meaning machinery

In the text after this introduction it has been outlined that between human actors the most powerful tool for the clarification of the given situation — the NOW — is the everyday language with a ‘built in’ potential in every human actor for infinite meanings. This individual internal meaning space as part of the individual cognitive structure is equipped with an ‘abstract – concrete’ meaning structure with the ability to distinguish between ‘true’ and ‘not true’, and furthermore equipped with the ability to ‘play around’ with meanings in a ‘new way’.

COORDINATION

Thus every human actor can generate within his cognitive dimension some states or situations accompanied with potential new processes leading to new states. To share this ‘internal meanings’ with other brains to ‘compare’ properties of the ‘own’ thinking with properties of the thinking of ‘others’ the only chance is to communicate with other human actors mediated by the shared everyday language. If this communication is successful it arises the possibility to ‘coordinate’ the own thinking about states and possible actions with others. A ‘joint undertaking’ is becoming possible.

shared thinking

To simplify the process of communication it is possible, that a human actor does not ‘wait’ until some point in the future to communicate the content of the thinking, but even ‘while the thinking process is going on’ a human actor can ‘translate his thinking’ in language expressions which ‘fit the processed meanings’ as good as possible. Doing this another human actor can observe the language activity, can try to ‘understand’, and can try to ‘respond’ to the observations with his language expressions. Such an ‘interplay’ of expressions in the context of multiple thinking processes can show directly either a ‘congruence’ or a ‘difference’. This can help each participant in the communication to clarify the own thinking. At the same time an exchange of language expressions associated with possible meanings inside the different brains can ‘stimulate’ different kinds of memory and thinking processes and through this the space of shared meanings can be ‘enlarged’.

phenomenal space 1 and 2

Human actors with their ability to construct meaning spaces and the ability to share parts of the meaning space by language communication are embedded with their bodies in a ‘body-external environment’ usual called ‘external world’ or ‘nature’ associated with the property to be ‘real’.

Equipped with a body with multiple different kinds of ‘sensors’ some of the environmental properties can stimulate these sensors which in turn send neuronal signals to the embedded brain. The first stage of the ‘processing of sensor signals’ is usually called ‘perception’. Perception is not a passive 1-to-1 mapping of signals into the brain but it is already a highly sophisticated processing where the ‘raw signals’ of the sensors — which already are doing some processing on their own — are ‘transformed’ into more complex signals which the human actor in its perception does perceive as ‘features’, ‘properties’, ‘figures’, ‘patterns’ etc. which usually are called ‘phenomena’. They all together are called ‘phenomenal space’. In a ‘naive thinking’ this phenomenal space is taken ‘as the external world directly’. During life a human actor can learn — this must not happen! –, that the ‘phenomenal space’ is a ‘derived space’ triggered by an ‘assumed outside world’ which ’causes’ by its properties the sensors to react in a certain way. But the ‘actual nature’ of the outside world is not really known. Let us call the unknown outside world of properties ‘phenomenal space 1’ and the derived phenomenal space inside the body-brain ‘phenomenal space 2’.

TIMELY ORDERING

Due to the availability of the phenomenal space 2 the different human actors can try to ‘explore’ the ‘unknown assumed real world’ based on the available phenomena.

If one takes a wider look to the working of the brain of a human actor one can detect that the processing of the brain of the phenomenal space is using additional mechanisms:

  1. The phenomenal space is organized in ‘time slices’ of a certain fixed duration. The ‘content’ of a time slice during the time window (t,t’) will be ‘overwritten’ during the next time slice (t’,t”) by those phenomena, which are then ‘actual’, which are then constituting the NOW. The phenomena from the time window before (t’,t”) can become ‘stored’ in some other parts of the brain usually called ‘memory’.
  2. The ‘storing’ of phenomena in parts of the brain called ‘memory’ happens in a highly sophisticated way enabling ‘abstract structures’ with an ‘interface’ for ‘concrete properties’ typical for the phenomenal space, and which can become associated with other ‘content’ of the memory.
  3. It is an astonishing ability of the memory to enable an ‘ordering’ of memory contents related to situations as having occurred ‘before’ or ‘after’ some other property. Therefore the ‘content of the memory’ can represent collections of ‘stored NOWs’, which can be ‘ordered’ in a ‘sequence of NOWs’, and thereby the ‘dimension of time’ appears as a ‘framing property’ of ‘remembered phenomena’.
  4. Based on this capability to organize remembered phenomena in ‘sequences of states’ representing a so-called ‘timely order’ the brain can ‘operate’ on such sequences in various ways. It can e.g. ‘compare’ two states in such a sequence whether these are ‘the same’ or whether they are ‘different’. A difference points to a ‘change’ in the phenomenal space. Longer sequences — even including changes — can perhaps show up as ‘repetitions’ compared to ‘earlier’ sequences. Such ‘repeating sequences’ can perhaps represent a ‘pattern’ pointing to some ‘hidden factors’ responsible for the pattern.

formal representations [1]

Basic outline of human actor as part of an external world with an internal phenomenal space 2, including a memory and the capability to elaborate cognitive meta-levels using the dimension of time. There is a limited exchange medium between different brains realized by language communication. Formal models are an instrument to represent recognized timely sequences of sets of properties with typical changes.

Based on a rather sophisticated internal processing structure every human actor has the capability to compose language descriptions which can ‘represent’ with the aid of sets of language expressions different kinds of local situations. Every expression can represent some ‘meaning’ which is encoded in every human actor in an individual manner. Such a language encoding can partially becoming ‘standardized’ by shared language learning in typical everyday living situations. To that extend as language encodings (the assumed meaning) is shared between different human actors they can use this common meaning space to communicate their experience.

Based on the built-in property of abstract knowledge to have an interface to ‘more concrete’ meanings, which finally can be related to ‘concrete perceptual phenomena’ available in the sensual perceptions, every human actor can ‘check’ whether an actual meaning seems to have an ‘actual correspondence’ to some properties in the ‘real environment’. If this phenomenal setting in the phenomenal space 2 with a correspondence to the sensual perceptions is encoded in a language expression E then usually it is told that the ‘meaning of E’ is true; otherwise not.

Because the perceptual interface to an assumed real world is common to all human actors they can ‘synchronize’ their perceptions by sharing the related encoded language expressions.

If a group of human actors sharing a real situation agrees about a ‘set of language expressions’ in the sens that each expression is assumed to be ‘true’, then one can assume, that every expression ‘represents’ some encoded meanings which are related to the shared empirical situation, and therefore the expressions represent ‘properties of the assumed real world’. Such kinds of ‘meaning constructions’ can be further ‘supported’ by the usage of ‘standardized procedures’ called ‘measurement procedures’.

Under this assumption one can infer, that a ‘change in the realm of real world properties’ has to be encoded in a ‘new language expression’ associated with the ‘new real world properties’ and has to be included in the set of expressions describing an actual situation. At the same time it can happen, that an expression of the actual set of expressions is becoming ‘outdated’ because the properties, this expression has encoded, are gone. Thus, the overall ‘dynamics of a set of expressions representing an actual set of real world properties’ can be realized as follows:

  1. Agree on a first set of expression to be a ‘true’ description of a given set of real world properties.
  2. After an agreed period of time one has to check whether (i) the encoded meaning of an expression is gone or (ii) whether a new real world property has appeared which seems to be ‘important’ but is not yet encoded in a language expression of the set. Depending from this check either (i) one has to delete those expressions which are no longer ‘true’ or (ii) one has to introduce new expressions for the new real world properties.

In a strictly ‘observational approach’ the human actors are only observing the course of events after some — regular or spontaneous –time span, making their observations (‘measurements’) and compare these observations with their last ‘true description’ of the actual situation. Following this pattern of behavior they can deduce from the series of true descriptions <D1, D2, …, Dn> for every pair of descriptions (Di,Di+1) a ‘difference description’ as a ‘rule’ in the following way: (i) IF x is a subset of expressions in Di+1, which are not yet members of the set of expressions in Di, THEN ‘add’ these expressions to the set of expressions in Di. (ii) IF y is a subset of expressions in Di, which are no more members of the set of expressions in Di+1, THEN ‘delete’ these expressions from the set of expressions in Di. (iii) Construct a ‘condition-set’ of expressions as subset of Di, which has to be fulfilled to apply (i) and (ii).

Doing this for every pair of descriptions then one is getting a set of ‘change rules’ X which can be used, to ‘generate’ — starting with the first description D0 — all the follow-up descriptions only by ‘applying a change rule Xi‘ to the last generated description.

This first purely observational approach works, but every change rule Xi in this set of change rules X can be very ‘singular’ pointing to a true singularity in the mathematical sense, because there is not ‘common rule’ to predict this singularity.

It would be desirable to ‘dig into possible hidden factors’ which are responsible for the observed changes but they would allow to ‘reduce the number’ of individual change rules of X. But for such a ‘rule-compression’ there exists from the outset no usable knowledge. Such a reduction will only be possible if a certain amount of research work will be done hopefully to discover the hidden factors.

All the change rules which could be found through such observational processes can in the future be re-used to explore possible outcomes for selected situations.

COMMENTS

[1] For the final format of this section I have got important suggestions from René Thom by reading the introduction of his book “Structural Stability and Morphogenesis: An Outline of a General Theory of Models” (1972, 1989). See my review post HERE : https://www.uffmm.org/2022/08/22/rene-thom-structural-stability-and-morphogenesis-an-outline-of-a-general-theory-of-models-original-french-edition-1972-updated-by-the-author-and-translated-into-english-by-d-h-fowler-1989/

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

E ⊢X 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

ES ⊢Σ,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

ES ⊢T,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.

 

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: 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)

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