OKSIMO.R – EVERYDAY SCENES – Daily routine (temporal structure(s))

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

Parts of this text have been translated with www.DeepL.com/Translator (free version), afterwards minimally edited.

CONTEXT

This post is part of the book project ‘oksimo.R Editor and Simulator for Theories’ and represents a continuation of Part 2 of the everyday scene ‘Going out to eat’ as well as the explanation box ‘World, Space, Time’.

CONTENT

This text is about using an ‘explicit time structure’ in addition to the ‘implicit time structure’ when describing a process in everyday life. An ‘implicit’ description of time is given when one arranges different events in the narrative one after the other without explicitly stating time (Peter is standing in front of the door. He opens the door and goes in.) An ‘explicit’ time statement uses such expressions as agreed ‘time markers’ (It is early in the morning. Peter wakes up. After 15 minutes he goes to the bathroom. At 12:00h he has to be at the store….).

A Daily Routine

Time of Natur – Machine Time

In the previous example, a process is described (going out to eat), which can be understood as ‘part of a day’: A ‘day’ is usually understood as the ‘time’ between getting up in the ‘morning’ and ‘going to bed’ in the ‘evening’, where ‘evening’ is fuzzy; for many, the time to ‘go to bed’ extends to ‘midnight’ or even later. While the activities ‘getting up’ and ‘going to bed’ as such have a reasonably concrete meaning, it is a bit more difficult with ‘morning’ and ‘evening’. Originally ‘morning’ was the time when the ‘sun rises’ and ‘evening’ when the ‘sun sets’ (‘time caused by nature’). With the progress of ‘urbanization’ and the ‘mechanization’ of the living world, a stronger and stronger uncoupling of the daily routine from ‘periodic natural events’ (sun, moon, …) takes place and simultaneously a stronger and stronger coupling to ‘artificial environments’, with ‘time machines’ (clocks) as ingredients. [1] The ‘periodic signals’ of these time machines (‘machine time’) then serve more and more as a substitute for natural periodic processes. My ‘morning’ is then perhaps no longer the ‘sunrise’ but the ‘ringing of my alarm clock’ at e.g. 7:00h. The ‘lunch time’ is then no longer the highest sun position but e.g. 12:30h to 13:30h as the ‘official lunch break’ of the respective institution. etc.

A Timely-Structured Day

If you want to work with explicit time specifications in an oksimo.R text, then these must occur as a ‘property of a situation’. A simple example:

Gerd is sitting in his office. It is 12:30h. Gerd is hungry.

One could then continue with e.g.:

Gerd decides to go to the Greek restaurant around the corner. Gerd goes to the Greek. It is 12:40h when he leaves his office.

In this way, one can let a clock run the whole day until the time when Gerd goes to sleep.

It is 23:35h. Gerd falls asleep. At 7:00h the alarm clock rings.

Let’s assume the simple case that the daily routine is largely regulated by ‘fixed points’. Then one could describe with few rules any number of daily routines one after the other.

A first demo example

Let’s assume the following simple daily routine [2]:

  • Morning, waking up
  • Leaving the apartment
  • Morning, office
  • Noon, snack
  • Afternoon, office
  • End of work
  • Evening errands
  • Late evening free time
  • Sleeping at night

ACTUAL description

A baseline situation could start at any point in time, e.g. at the end of work:

ACTUAL DESCRIPTION (end of work)

Name: end-of-work1
It is the end of work.
Gerd leaves the office.

GOAL description(s)

The actor can have many goals at the same time, e.g.:

GOAL DESCRIPTION(s) [3].

GOAL 1 (Shopping)

Name: g-shopping1

It is the end of work.
Gerd has made his purchases.

GOAL 2

It is late in the evening. Gerd has been playing music.

GOAL 3

It is after 23:00h. Gerd has gone to sleep.

Rules of change

Now you have to think about which change rules – based on the ACTUAL description – can be used to achieve the various goals.


… for GOAL 1

To achieve GOAL 1, one could perhaps adopt the following change rule(s):

CR Purchasing

IF

It is the end of work. Gerd leaves the office.

THEN

Added: Gerd goes to the store around the corner.

Away: Gerd leaves the office.

A change rule in the oksimo.R format:

Rule: cr-shop1
Conditions:
It is the end of work.
Gerd leaves the office.
Positive Effects:
Gerd goes to the store around the corner.

Negative Effects:
Gerd leaves the office.

Rule name: vr-ladencr-shop2
Conditions:
It is the end of work.
Gerd goes to the store around the corner.
Positive Effects:
Gerd is in the store
Gerd picks up everything he needs.
Gerd goes to the cash register and pays.
Gerd has made his purchases.

Negative Effects:
Gerd goes to the store around the corner.

Unify the change rules so far in a rules document:

rd-shopping1

cr-shop1
cr-shop2

A first partial Simulation

name of stored simulation: shopping1-sim1

Your vision:
Gerd has made his purchases.,It is the end of work.

Initial states: 
It is the end of work.,Gerd leaves the office.

Round 1

Current states: Gerd goes to the store around the corner.,It is the end of work.
Current visions: Gerd has made his purchases.,It is the end of work.
Current values:

50.00 percent of your vision was achieved by reaching the following states:
It is the end of work.,

Round 2

Current states: Gerd has made his purchases.,Gerd picks up everything he needs.,Gerd goes to the cash register and pays.,Gerd is in the store,It is the end of work.
Current visions: Gerd has made his purchases.,It is the end of work.
Current values:

100.00 percent of your vision was achieved by reaching the following states:
Gerd has made his purchases.,It is the end of work.,

Differentiation of the concept ‘ACTUAL description’.

It can be seen from the ACTUAL description of round 2 that in this ACTUAL description actually ‘several state descriptions’ were summarized. The individual statements {Gerd goes to the cash register and pays, Gerd collects everything he needs, It is the end of work, Gerd is in the store} are such that each describes an ACTUAL situation that can stand alone and which in everyday life ‘presuppose’ a certain sequence:

  1. It is the end of work.
  2. Gerd is in the store.
  3. Gerd picks up everything he needs.
  4. Gerd goes to the cash register and pays.

This raises the fundamental question of whether such a ‘summary’ of individual ACTUAL descriptions still constitutes an ACTUAL description that meets the following requirements: (i) A set of properties that are unchanged within a time interval. (ii) All actors involved in the situation can confirm the statements. If one introduces the distinction between ‘Elementary ACTUAL Descriptions’ and ‘Compound ACTUAL Descriptions’, then one could agree:

  1. Def: An ‘elementary ACTUAL description’ is an ACTUAL description.
  2. Def: A ‘composite ACTUAL description’ represents a ‘collection’ of elementary ACTUAL descriptions’.
  3. Truth criterion: The parties involved in a common situation must decide whether they accept the elementary/composite IS descriptions.

… !! Not yet finished !! …

COMMENTS

[1] To work as part of a larger society all the individual time machines have to be ‘coordinated’ such that every single time machine’ shows every moment the same ‘time marker’.

[2] On the one hand, this daily routine is extremely simple, but at the same time, in its simplicity, it describes a daily routine that looks completely different for many other people. It would certainly be interesting to see a daily routine as a ‘building block’ of an everyday life process, by which for the acting actor is determined to a large extent what he/she/x ‘experiences’, what he/she/x ‘does’, which social and societal interactions he/she/x experiences, and so on.

[3] Normally we formulate goal descriptions as wishes, in a form in which we express what we positively want without that it having already occurred: “I want to go shopping later”, “I want to play music after shopping”, “I will go to bed after 23:00h at the latest”. In the context of an oksimo.R text you have to formulate wishes in a way that describes the ‘result of the wish’, e.g. instead of “I want to go shopping later” you have to write: “I went shopping” or instead of “I want to make music after shopping” you have to write “I made music after shopping”, etc. The ‘logic’ behind this is that an oksimo.R text is a ‘theory’ that refers to an ACTUAL situation (e.g.: “It’s the end of work. Gerd is leaving the office,”), which then applies possible ‘change rules’ to an ACTUAL situation, and by applying change rules to an ACTUAL situation, a ‘new ACTUAL situation’ then arises. And then it can happen that after a certain sequence of ACTUAL situations an ACTUAL situation occurs in which the original desire to shop has taken place, i.e. in the ACTUAL situation there can then be the property “Gerd has shopped”. If there is then a TARGET description that says “Gerd has shopped”, then the system can immediately determine that this goal has been achieved. If, however, the TARGET description would have the format “Gerd wants to shop”, then this goal could never be achieved, because it is not clear when it would then be fulfilled.

oksimo.R: EXPLANATION BOX: World, Space, Time

eJournal: uffmm.org
ISSN 2567-6458, 25.November 2022 – 25.November 2022, 10:31h
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Parts of this text have been translated with www.DeepL.com/Translator (free version), afterwards minimally edited.

CONTEXT

This post is part of the book project ‘oksimo.R Editor and Simulator for Theories’ and represents an explanation box to the topic ‘World, Space, Time’.

CONTENT

In the example (part 1+2) already a little bit of the peculiarities of an ‘oksimo.R text’ becomes visible. These refer to aspects of space and time in our linguistic communication with texts. First comments on this here. More will follow later in the concluding overall theoretical presentation.

World, Space, Time

In our everyday life we presuppose – normally – the existence of a body world, to which also our own body belongs. We know of this — for the brain external — body world only something by the sense organs of our body and insofar as our brain ‘processes’ these signals of the sense organs – in the context of many other signals from the own body -, to different internal event structures. Perceptions of so-called ‘objects’ like cups, chairs, tables, cars, also animals and other people, are therefore ‘processed products’; we can never directly perceive the ‘triggering things of the external body world’ itself. Our brain creates a ‘virtual world’ in our head, but this is for us the ‘primary real world’. As a child one learns laboriously to distinguish between ‘mere imaginations (in our head)’ and such imaginations which also ‘correspond’ with immediate sensual perception and additionally link up with many kinds of ‘concrete (= sensual)’ properties. If a child is looking for his toy teddy bear in the ‘red box’ and it is not there, then this is one of the many experiences on the subject that the ‘imagination in the head’ should not automatically be equated with a ‘real factual situation’.

If our brain in closest cooperation with our body continuously generates a ‘virtual world’ of the ‘assumed external real world’, then it is already an interesting question, which of the many properties of the real world (which we know only on the basis of ‘experiences’ and ‘scientific reconstructions’), can be found in the virtual models of the brain? The question becomes even more exciting if we look at ‘linguistic communication between humans’: it is one thing that our brain ‘fills’ us with virtual constructs (ideas), it is quite another question which of these ideas can be communicated between brains (humans) by means of language.

The ‘space-time problem’ has been discussed by many philosophers and scientists. One of the most prominent representatives, who strongly influenced the discussion in European thinking at the beginning, is surely Immanuel Kant, who tried to work out with his book “Critique of Pure Reason” in 1781 (1787 2nd edition) that the ideas of ‘space’ and ‘time’ are laid out in our human thinking in such a way that we always ‘imagine’ and ‘think’ objective things as ‘part of a space’; he assumed the same for the idea of time. More precise analyses of this point of view of his are difficult for many reasons. For the following considerations one can be ‘sensitized’ by Kant’s position to the effect that in our ‘normal perception and thinking’ as well as then especially in our linguistic communication we have to reckon with properties which have to do with ideas of space and time.

oksimo.R Text as a ‘Set’

If we want to pursue the question whether and how ‘notions of space and time’ make themselves felt within normal linguistic communication, it is perhaps advisable to start with the format of oksimo.R texts, since these give the writer and reader ‘less freedom’ than a ‘normal’ English text.[1] The format of oksimo.R texts can be described relatively easily.

The peculiarity of oksimo.R texts can be described relatively simply:

  1. An oksimo.R text is a ‘set’ (‘collection’) of ‘linguistic expressions’ of a ‘normal language’ (e.g. German, English, Russian, Spanish, …).
  2. As a ‘part of the set text’ each linguistic expression is an ‘element’ of the set text.
  3. The ‘order’ of these elements in the text does not follow a certain structure. This means that the ‘sequence’ of elements in the written form has no meaning of its own. As in a usual set of the mathematical concept of a set, the elements can be ‘regrouped’ among themselves without ‘changing’ an oksimo.R text.
  4. The elements of a ‘set oksimo.R Text’ have as such no specific meaning. A ‘meaning’ comes to the elements of an oksimo.R text only if the writer-readers of oksimo.R texts know the language of these elements (e.g. English) and assign ‘agreed meanings’ to the elements by virtue of their language competence. However, this meaning exists exclusively ‘in the minds’ of the writer-readers, not explicitly in the text itself.

With these first observations about the peculiarity of oksimo.R texts, one can make a first comparison to texts of a normal language (here: English).

Normal Text, not a mere Set

If we look at the text of a normal language (here: English), then we link the written expressions ‘automatically’ (spontaneously, …) with different ‘(linguistically induced) meanings’ while reading. These ‘linked meanings’ are on the one hand strongly dependent on the ‘individual learning history’ with specific ‘individual preconditions’, but on the other hand also on the ‘cultural patterns of the social environment’, within which a person acquires/ builds up/ develops his language competence.

While the linguistic expressions as such do not induce any particular ‘order’, the ‘switched on’ linguistic meaning structures can, however, articulate different ‘relations’ through the factual structures contained in them with their learned properties, which mutually refer to each other. Thus, for example, when speaking of a ‘cup on a table’, this implies a ‘spatial structure’ with a ‘stands-on’ or ‘is-under’ relation. Moreover, the writer-reader of a text ‘knows’ that normally a cup is not on a table, but only when someone has explicitly put the cup there. A sequence of expressions like ‘Gerd puts the cup on the table. When Peter comes in he sees that there is a cup on the table’ then appears to a reader as ‘normal/ usual/ accustomed’. But if the text would say ‘When Peter comes in, he sees that there is a cup on the table. Gerd puts the cup on the table’, then a normal reader would stop and ask himself what the text wants to say: The cup is on the table and only then it is put on the table?

This simple example demonstrates besides an ‘implicit spatial structure’ also an ‘implicit temporal structure: In everyday experience, embedded in an external body world, it is normal that properties – and thus a whole situation – can change. However, these changes do not happen (! ) in the sensory perception (the present as such is ‘absolute’), but are only revealed in the ‘downstream processing’ by the brain, which is able to ‘store’ partial aspects of a current sensory perception in such a way (a highly complex neuronal process), that it can ‘remember’ these ‘stored structures’ again (also a highly complex neuronal process) and additionally ‘compares’ them with ‘other memory contents’ in such a way (also a highly complex neuronal process) that our brain can thereby reconstruct a ‘sequence’ as well as ‘identify’ possible ‘changes between single elements of the sequence’. Because of this highly complex mechanism the brain can break up/ overcome the ‘absoluteness of the present’ by ‘remembering and comparing’.

A ‘normal text’ has many more special properties. Here, first of all, it is only important to see that it is the dimension of ‘linguistic meaning’ localized ‘in’ a human writer-reader, through which a set of linguistic expressions can induce a complex ‘network of properties’ that have their ‘own linguistically induced logic’.

Linguistic Communication

Again, the whole spectrum of possible properties of ‘linguistic communication’ shall not be described here – a ‘sea’ of articles and books would could to be cited here – but only a few aspects shall be addressed which suggest themselves from the previous considerations on texts.

If the previous ‘working hypothesis on linguistic meaning’ is correct, then written linguistic expressions have the function to enable ‘between two brains’ a a ‘medium’ suitable to make so-called ‘signs’ out of the ‘linguistic expressions’. [2] A sign is a sensually perceptible material that can be related by ‘sign users’ to an ‘agreed space of sign meanings’. These agreed sign meanings are localized as such ‘in the head’ of the respective sign users, but they have the peculiarity that the different sign users have ‘learned’ by common ‘training’ which ‘sensuously perceptible realities of the external body world’ are to be linked with certain sign (material). If such a ‘coordination’ of sign(material) and sign-meaning succeeds (all children of this world practice this form of training in spontaneous language learning), a single sign-user can ‘hint’ at certain ‘elements of his meaning-space’ by practicing certain sign-connections to another trained sign-user. Through a ‘back and forth’ of statements, questions, possibly also interpretative gestures in a real situation, a certain ‘understanding’ can then – usually – be established. The more complex the circumstances are, the further away one is from a concrete situation, the more difficult it becomes to ‘convey’ what is meant sufficiently clearly.

What does all this mean in concrete terms?

For this we look at further examples realized with oksimo.R texts.

COMMENTS

[1] Even in the area of ‘normal’ English texts, there is a great variety of texts that make very special demands on ‘filling in’.

[2] There are numerous disciplines in academia that deal more or less ‘generally’ with properties of ‘normal’ languages and communication with normal languages. The discipline that actually does this most ‘generally’, ‘semiotics’, still leads a rather ‘shadowy existence’ worldwide next to the ‘established’ other disciplines. Here, too, there exists a myriad of articles and books on the subject.

Recommended further Reading

It is recommended to continue reading from here with the section about a daily routine HERE.

OKSIMO.R – EVERYDAY SCENES – GO OUT FOR EAT – Part 2

eJournal: uffmm.org
ISSN 2567-6458, 18.November 2022 – 25.November 2022, 10:58h
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Parts of this text have been translated with www.DeepL.com/Translator (free version), afterwards minimally edited.

CONTEXT

This post is part of the book project ‘oksimo.R Editor and Simulator for Theories’ and represents a continuation of Part 1.

CONTENT

In Part 1, the beginning of a simple example was presented, where an actor (here: ‘Gerd’) is sitting in his office, feels hungry, and imagines that he does not want to be hungry. In part 1, he decides to leave his office and go out to eat. Embedded in the mini-theory of this example, several concepts are explained: text types (ACTUAL description, TARGET description, CHANGE description), rule application, oksimo.R software contextualization, theory testing, inference testing, goal fulfillment testing, starting a simulation, and logical inference.

In part 2, the mini-theory will be completed. The story ends with the actor Gerd not feeling hungry anymore (at least not for the moment :-)).

Continuation of the story

An oksimo.R theory can be understood simply as a ‘story’, a kind of ‘script’, although this story has all the properties of a full empirical theory (more on theory below).

The story so far is simply told:

Starting point (Scene 1):
Gerd is sitting in his office.Gerd is hungry.
Target:
Gerd is not hungry.

Scene 2:
Gerd leaves his office.Gerd is hungry.
Target achievement so far: 0%.

The transition from Scene 1 to Scene 2 was only possible because a change rule was adopted which states that Scene 1 can be changed if the condition ‘Gerd is hungry’ holds. Since this is the case, the property ‘Gerd is sitting in his office’ was removed and the new property ‘Gerd is leaving his office’ was added.

For another continuation, a rule is missing at the moment. However, the only change rule so far can be reapplied over and over again, so that scene 2 is repeated any number of times (like a record player hitting a broken groove in the record, so that the record player repeats that track endlessly until we turn it off.)

This ‘repeatability’ can become a problem if you’re not careful. Here’s an example of unwanted repetition (which we ultimately don’t want!).

Unwanted repetition(s)

Since there is a Greek bistro ‘around the corner to the left’ where Gerd could eat a snack, we write down the following new change rule:

CHANGE Description 2:

IF:

Gerd is hungry.

THEN:

Add as a property to the ACTUAL situation: Gerd decides to go to the Greek around the corner.

Remove as a property from the ACTUAL situation: – Nothing -.

APPLICATION of the change description:

Since the condition ‘Gerd is hungry.’ is met, the rule could be applied and we would get the following result with this rule:

THEN:

NEW ACTUAL situation (with rule 2):

Gerd decides to go to the Greek around the corner. Gerd is hungry.

However, there is still rule 1, which does not disappear (as an option, however, conceivable). This rule has the same condition as rule 2 and can therefore also be applied. It would produce the following result:

NEW ACTUAL situation (with rule 1):

Gerd leaves his office. Gerd is hungry.

A ‘union’ of the continuation according to rule 1 and the continuation according to rule 2 leads to the following result:

Gerd decides to go to the Greek around the corner. Gerd is hungry. Gerd leaves his office.

With the oksimo.R software (level 2) this would look like this:

Entering a Change Rule

Rule:Food1-Location1
Conditions:
Gerd is hungry.
Positive Effects:
Gerd decides to go to the Greek around the corner.

Negative Effects: — Nothing —

Starting a New Simulation

With code number one you can start a new simulation. We need the following ‘ingredients’:

Selected visions:
Food1-v1
Selected states:
Food1
Selected rules:
Food1-Location1
Food1-Decision1

Protocol of the simulation (simple version)

Your vision:
Gerd is not hungry.

Initial states: 
Gerd is hungry.,Gerd is sitting in his office.

Round 1

Current states: Gerd is hungry.,Gerd leaves his office.,Gerd decides to go to the Greek around the corner.
Current visions: Gerd is not hungry.

0.00 percent of your vision was achieved by reaching the following states:
None

Round 2

Current states: Gerd is hungry.,Gerd leaves his office.,Gerd decides to go to the Greek around the corner.
Current visions: Gerd is not hungry.

0.00 percent of your vision was achieved by reaching the following states:
None

Already after two simulation cycles one recognizes that everything repeats itself. And with knowledge of the change rules one knows that both are ‘activated’ again and again as long as their condition is fulfilled. In the concrete example this is the case. This points to a general structure of rule-driven changes with situational reference.

On the meta-logic of situational change rules

At this point it should be remembered again that an ACTUAL description is nothing more than a ‘set of linguistic expressions’ of the respective language chosen. Here the English language is used. In the original source of the oksimo.org blog the German language is used. Any other language is also possible.

However, from the point of view of the respective actor working with such IS-descriptions, every linguistic expression used in the space of his ‘linguistic understanding’ has additionally a ‘special meaning’, which can partially be ‘correlate’ with ‘properties of the external body world’ in a ‘specific way’. So, if someone reads the expression ‘Gerd’, he will mostly associate with it the idea that it is the ‘name of an individual’. And when one reads the linguistic expression ‘… sitting in his office’, one will usually think of a ‘room in a building’. Both notions ‘name of an individual’ as well as ‘room’ in a building’ have – normally – the property that one can ‘relate’ to them concrete ‘objects of the external body world’ via ‘individual perception’. This can happen in many ways, e.g. in which someone else says to me “Look (and he points to a person), this is Gerd”, or I come into the room 204 in building 1 of the Frankfurt University of Applied Sciences and someone says to me “Look, this is Gerd’s office”. In both cases, a concrete perception can then connect with an ‘imagined conception’ in such a way that the inherently ‘abstract’ conception of an individual person in a room connects (associates) with a bundle of sensually perceived properties.

With this background knowledge one can then understand why an IS-description as a set of linguistic expressions has ‘two faces’: (i) At first sight there are only a set of linguistic expressions without any recognizable further property, and (ii) , starting from the linguistic expressions, mediated by the linguistic meaning knowledge of a speaker-hearer of the respective language, a set of meanings appears, which in the case of an IS-description must by agreement all have at least one concrete reference to the external body world. Roughly, one can therefore say at this point that every linguistic expression of a normal language can be linked (associated) with a ‘property’ of the external body world. In this second sense, an ACTUAL description then represents not only a ‘set of linguistic expressions’ but at the same time also (language comprehension in the actor presupposed) a ‘set of body-world properties’. The removal of a linguistic expression then means at the same time the removal of a property, and the addition of a linguistic expression the addition of a property.

Due to this generally assumed ‘linguistic dimension of meaning’ in each involved actor, ACTUAL descriptions thus potentially represent a connection between the virtual images in the brain of an actor to possible sensually perceptible correlates of an external body world linked to it, for which a ‘self-driven dynamic’ is assumed. By this is meant that the world of our sensual perception (linked with our memory!), apparently constantly ‘partially changes’ and simultaneous ‘partial stays constant’. The ‘extension’ of the ‘quantity of the properties of the external body world’ seems to be almost ‘infinite’ and at the same time also the possible extent of the changes.

Against this background (largely always hypothetical), any ACTUAL description always appears as a ‘very small selection’ of this body world property set and a concrete ACTUAL description forms a kind of ‘snapshot’ of a continuously dynamic event which can only be ‘traced’ in a highly simplified way via the explicitly formulated rules of change. In particular, there is a problem of how to keep an ACTUAL description ‘up to date’ when the external body world is continuously changing due to its ‘inherent dynamics’ without any oksimo.R theory-builder actor having formulated a single rule of change. In other words, an ACTUAL description ‘becomes obsolete’ by itself if the ‘coupling’ of the ACTUAL description to the external body world is not ensured with ‘appropriate’ change rules. In order to be able to do this, one needs a ‘translator’ who continuously ‘maps’ the changes of the external body world into the linguistic meaning space of the actors and these then generate corresponding linguistic expression sets.

Further possible requirements for a process

After these meta-logical considerations about the function of ACTUAL descriptions in the interplay with an assumed external body world with its own inherent dynamics, some further aspects shall be brought up here, which are/can be significant for the creation of a ‘plan’.

So far the small oksimo.R theory – the current story – has the following format:

Initial state (Scene 0): 
Gerd is hungry.Gerd is sitting in his office.

The vision:
Gerd is not hungry.

Scene 1:
Gerd is hungry.Gerd leaves his office.Gerd decides to go to the Greek around the corner.

Success: 0.00 percent 

Scene 2:
Gerd is hungry.Gerd leaves his office. Gerd decides to go to the Greek around the corner.

Success: 0.00 percent

The goal is still that the actor Gerd reaches his goal, the ‘Greek around the corner’, so that he can eat, for example, so that his feeling of hunger disappears.

For this, on the one hand, there must be rules that move the actor ‘through space’ to the ‘Greek around the corner’, on the other hand, the rules must be such that they cannot activate properties that should no longer occur in the process at all.

A rule like ‘Food-Location1′, which ensures that Gerd leaves his office, should not be applied again at a ‘later time’, similarly the rule ‘Food1-Decision1’, which describes the decision that Gerd wants to go to the ‘Greek around the corner’.

Since the activation of a change rule depends on the respective ‘condition’, this means that the condition for a rule should be such that the ‘triggering property’ is as ‘process-specific’ as possible. For the property ‘Gerd is hungry’, which is valid throughout the whole story until the actual eating, this is rather not true. Since all rules with this ‘non-specific trigger’ would be activated again and again, until at some point the eating produces the new property ‘Gerd is not hungry’.

This raises the question of how an ACTUAL description should be formatted such that, in addition to ‘long-living’ properties, there are also ‘short-living’ properties that can actually serve selectively as ‘triggers for rule activation’.

Time information is often not enough

In everyday life we are used to link events to a certain time, thereby assuming the existence of clocks that are synchronized worldwide; or the whole thing extended by a calendar with days, weeks, months and years. Such a tool can easily be introduced into an oksimo.R theory. But this solves the problem only partially. For many events one knows in advance neither ‘whether’ they occur at all, nor ‘when’ this will happen. In that case, the only possibility is to link a ‘subsequent event’ directly to a certain ‘preceding’ event: For example, it only makes sense to open the umbrella when it actually rains. There is usually no exact date when this event will occur.

Design perspectives: Goal and precision

What use are these considerations in the specific example where a ‘sequence’ is sought that leads to Gerd experiencing that his feeling of hunger disappears?

Two general considerations may be helpful here:

  1. Thinking from the end (goal)
  2. What ‘accuracy’ is required/desired?

If one knows a goal (which is not self-evident; often one first has to find out what a meaningful goal could be), then one can try to think ‘backwards’ from the goal by being guided by the question, ‘Which action A do I have to do to achieve result B?’. In the case of the desired goal state ‘Gerd is not hungry’, the usual experience would be to eat something ‘appropriate’, which leads to the ‘disappearance of the feeling of hunger’ (most of the time). Then you have to know what that ‘food’ might be, where to get it, and what you would have to do to get there (let’s ignore the case of someone just bringing something from home to eat). From such ‘backward-thinking’ a hypothetical sequence of actions can emerge, which can become the basis for a ‘plan’, which the actor will work out ‘in his head’ and then implement piecemeal by corresponding ‘real actions’.

The question of ‘accuracy of representation’ (of a story, of a theory) is not easy to answer. If engineers have to program a robot that is supposed to be able to perform certain operations, then this will normally require an almost merciless accuracy (apart from the case that there are already many ready-made modules that can take care of ‘small stuff’ (such as so-called ‘machine learning’ after successful training)). If it is the author of a crime novel or the author of a screenplay, then besides ‘factual aspects’ very much also the ‘effect on the readers / viewers’ must be considered. In the case of achieving a concrete goal in a concrete world, the potential success of the implementation of a description depends entirely on whether the concrete requirements of the world – here the everyday world – are completely satisfied. Of course, the reader/listener/user of a description also plays a major role: If we can assume that we are dealing with ‘experts’ who ‘know’ the process to be performed well, we can perhaps work with hints only; if we are dealing more with ‘newcomers’, then we must provide very detailed information. Sometimes a purely text-based description is not sufficient; more is then needed: pictures, videos or even your own training.

With a target and with ‘everyday’ accuracy

In the concrete case, there exists a target and ‘everyday experience’ is to be taken as a yardstick for accuracy; the latter, of course, leaves much ‘room for interpretation’.

Starting from the goal ‘thought backwards’ the following chain of actions seems plausible as a ‘hypothetical plan’:

  1. Gerd is not hungry’ because:
  2. ‘Gerd is eating his stew’ because:
  3. ‘Gerd gets his order’ because:
  4. ‘Gerd is ordering a stew’ because:
  5. ‘Gerd is standing in front of the counter’ because:
  6. ‘Gerd enters the bistro’ because:
  7. ‘Gerd goes to the Greek around the corner’ because:
  8. ‘Gerd decides to go to the Greek around the corner’ because:
  9. ‘Gerd is hungry’, ‘Gerd is in his office’, because:
  10. … there is a ‘cut’ here: arbitrary decision where to start the story/theory …

In fact, at any moment, there is not only one choice, and many things can happen during the ‘execution’ of this ‘plan’, which can result in a change of the plan. And, of course, there are many more possible aspects that could (or should) be relevant for the execution of this plan.

Constant and variable properties

As observed earlier, there are properties that are ‘rather constant’ and those that are ‘short-lived’. For example, in the context of the ‘plan’ above, the property ‘Gerd is hungry’ is constant from the beginning until the event ‘Gerd is not hungry’. Another property like ‘Gerd leaves his office’ is rather short-lived.

If we take the above hypothetical plan as a reference point, the following distribution of ‘rather constant’ and ‘rather short-lived’ properties suggests itself (left column ‘rather constant’, right column ‘rather short-lived’):

Gerd is hungry.Gerd is in his office
Gerd is hungry.Gerd decides …
Gerd is hungry.Gerd walks …
Gerd is hungry.Gerd enters …
Gerd is hungry.Gerd stands in front of ..
Gerd is hungry.Gerd orders …
Gerd is hungry.Gerd gets …
Gerd is hungry.Gerd eats …
Gerd is not hungry.

A simple strategy to avoid inappropriate repetitions would be the one in which the condition of a change rule refers to a ‘rather short-lived’ property that ‘automatically’ disappears with the implementation of a change rule.

Example (short form):

  1. If: ‘Gerd is hungry’ and ‘Gerd is in his office’, Then: ‘Gerd decides to…’.
  2. If ‘Gerd is hungry’ and ‘Gerd decides…’, Then add: ‘Gerd goes…’, Delete: ‘Gerd in office…’
  3. If ‘Gerd is hungry’ and ‘Gerd goes…’, then add: ‘Gerd enters…’, delete: ‘Gerd goes…’
  4. If ‘Gerd is hungry’ and ‘Gerd enters…’, then add: ‘Gerd stands in front of…’, delete: ‘Gerd enters…’.
  5. If ‘Gerd is hungry’ and ‘Gerd stands in front of…’, then add: ‘Gerd orders …’, delete: ‘Gerd stands in front of …’
  6. If ‘Gerd is hungry’ and ‘Gerd orders …’, then add: ‘Gerd gets …’, delete: ‘Gerd orders …’
  7. If ‘Gerd is hungry’ and ‘Gerd gets …’, then add: ‘Gerd eats…’, delete: ‘Gerd gets’.
  8. If ‘Gerd is hungry’ and ‘Gerd eats’, then add: ‘Gerd is not hungry’, delete: ‘Gerd eats…’.

This small example already shows very clearly the ‘double nature’ of our everyday reality: one is what we do ourselves, and the other is the ‘effects’ of our doing in the external body world. When someone intends to ‘walk’ and then actually walks, then one moves the body, which ‘automatically’ changes the position of the body in the external body world. Normally, one does not describe these ‘effects’ explicitly, because every person knows that this is so, based on everyday world experience. But if one wants to create a ‘description’ of the external body world with its properties, which is such that an ACTUAL description contains everything that is important for the description of a process, then one must also make some of the ‘implicit properties’ ‘explicit’ by including them in the description. Most important is the attention to ‘more ephemeral’ (temporary) properties, whose presence or absence is crucial for many actions.

Simulation extension

The extended simulation adopts the action outline from ‘backward thinking’ (see above). New change rules are formulated for this purpose.

The previous ACTUAL description is retained:

Eat1

Gerd is sitting in his office.
Gerd is hungry.

The current TARGET description is retained:

Eat1-v1

Gerd is not hungry.

The following change rules are reformulated:

Eat1-Decision1

Rule name: Eat1-Decision1
Conditions:
Gerd is hungry.
Gerd is sitting in his office.
Effects plus:
Gerd goes to the Greek.
Gerd decides to go to the Greek restaurant around the corner.
Effects minus:
Gerd is sitting in his office.

Eat1-Enter1

Rule: Eat1-Enter1
Conditions:
Gerd goes to the Greek.

Positive Effects:
Gerd is in the bistro.
Gerd enters the bistro.

Negative Effects:
Gerd goes to the Greek.

Gerd decides to go to the Greek restaurant around the corner.

Eat1-Stand-Before1

Rule: Eat1-Stand-Before1
Conditions:
Gerd enters the bistro.
Positive Effects:
Gerd stands in front of the counter.

Negative Effects:
Gerd enters the bistro.

Eat1-Order1

Rule: Eat1-Order1
Conditions:
Gerd stands in front of the counter.
Positive Effects:
Gerd orders a stew.

Negative Effects:
Gerd stands in front of the counter.

Eat1-Come1

Rule name: Eat1-Come1
Conditions:
Gerd orders a stew.
Effects plus:
Gerd gets his stew.
Effects minus:
Gerd orders a stew.

Eat1-Food1

Rule: Eat1-Food1
Conditions:
Gerd gets his stew.
Positive Effects:
Gerd eats his stew.

Negative Effects:
Gerd gets his stew.

Eat1-Not-Hungry1

Rule:Eat1-Not-Hungry1
Conditions:
Gerd eats his stew.
Positive Effects:
Gerd is not hungry.

Negative Effects:
Gerd is hungry.
Gerd eats his stew.

Collecting single Rules in one Rules Document

If you wanted to start a new simulation now, you would normally have to enter each rule individually. When experimenting, this can quickly become very annoying. Instead, you can combine all rules that ‘thematically’ ‘belong together’ in a ‘rule document’. Then you only need to enter the name of the rule document in the future.

In the present case, a rule document with the name ‘Eat1-RQuantity1′ is created. This document then includes the following rules:

  1. Eat1-Decision1
  2. Eat1-Enter1
  3. Eat1-Stand-Before1
  4. Eat1-Order1
  5. Eat1-Come1
  6. Eat1-Food1
  7. Eat1-Not-Hungry1

To start a new simulation, you then only need to enter the following:

Selected visions:
Eat1-v1
Selected states:
Eat1
Selected rules:
doc Eat1-RQuantity1

Enter maximum number of simulation rounds

>10

SIMULATION PROTOCOL (with rule applications)

Simulation saved as: Eat1-sim6

Your vision:
Gerd is not hungry.

Initial states: 
Gerd is hungry.,Gerd is sitting in his office.
Initial math states

Round 1

Current states: Gerd is hungry.,Gerd decides to go to the Greek restaurant around the corner.,Gerd goes to the Greek.
Current visions: Gerd is not hungry.
Current values:

0.00 percent of your vision was achieved by reaching the following states:
None

Round 2

Current states: Gerd is hungry.,Gerd is in the bistro.,Gerd enters the bistro.
Current visions: Gerd is not hungry.
Current values:

0.00 percent of your vision was achieved by reaching the following states:
None

Round 3

Current states: Gerd is hungry.,Gerd stands in front of the counter.,Gerd is in the bistro.
Current visions: Gerd is not hungry.
Current values:

0.00 percent of your vision was achieved by reaching the following states:
None

Round 4

Current states: Gerd is hungry.,Gerd orders a stew.,Gerd is in the bistro.
Current visions: Gerd is not hungry.
Current values:

0.00 percent of your vision was achieved by reaching the following states:
None

Round 5

Current states: Gerd is hungry.,Gerd gets his stew.,Gerd is in the bistro.
Current visions: Gerd is not hungry.
Current values:

0.00 percent of your vision was achieved by reaching the following states:
None

Round 6

Current states: Gerd is hungry.,Gerd is in the bistro.,Gerd eats his stew.
Current visions: Gerd is not hungry.
Current values:

0.00 percent of your vision was achieved by reaching the following states:
None

Round 7

Current states: Gerd is not hungry.,Gerd is in the bistro.
Current visions: Gerd is not hungry.
Current values:

100.00 percent of your vision was achieved by reaching the following states:
Gerd is not hungry.,

Further Reading:

We recommend to continue with the explanation box about ‘World, Space, Time’ and then, after having read this, go to ‘Daily Routine (temporal structures)’.

OKSIMO.R – EVERYDAY SCENES – GO OUT FOR EAT

eJournal: uffmm.org
ISSN 2567-6458, 6.November 2022 – 17.November 2022
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

Parts of this text have been translated with www.DeepL.com/Translator (free version), afterwards only minimally edited.

CONTEXT

This post is part of the book project ‘oksimo.R Editor and Simulator for Theories’.

CONTENT

A normal everyday scene is used to illustrate some properties of modeling (theory building) in the oksimo.R paradigm. This case is about a person who works in a university, has an office there (together with others), and ‘feels hungry’ around noon. This becomes the occasion for this person to decide to go out to eat. In this case ‘to the Greek around the corner’. The short story ends with this person no longer feeling hungry.

OKSIMO.R TEXT TYPES

Modeling (theory building) in the oksimo.R paradigm takes place by a group of people working together to formulate a text in a common language. In the concrete case, this is the German language; however, it can also be any other language.

Three types of texts are distinguished:

  • ACTUAL descriptions (initial situations)
  • TARGET descriptions (requirements)
  • CHANGE descriptions (rules for change)

These distinctions presuppose that a human actor can distinguish between such ideas in his head, which ‘correspond’ to experiences outside his brain (in ‘his own body’, in the ‘body world outside his body’), and such ideas in his head, which he thinks/remembers/dreams/fantasizes ‘alone’, ‘for himself’ … .

ACTUAL situation

Here, ACTUAL descriptions refer to such ideas that relate to the body world beyond the own body and that can be ‘shared’ by other human actors. For example, if someone stands outside and says “It is raining”, and all bystanders would confirm this, then this would be a case of an ACTUAL-description that can be ‘confirmed’ by all. Most of the time people then also say that this description is ‘true’. If in this situation, where it is raining, someone would say “It is not raining” then everyone – usually – would say that this ‘statement’ is ‘false’. If someone says instead “It will rain soon”, then all bystanders who understand English will be able to form an idea in their brain that it is raining, but there is then no concrete equivalent to this idea in the real interpersonal physical world. This statement would then be neither ‘true’ nor ‘false’. Its relation to the ‘common body world’ would be ‘indeterminate’: it may perhaps become true, but need not.

GOAL description

TARGET descriptions (also in the form of requirements) refer to such ‘imaginations in the minds of actors’ to which there are accepted linguistic expressions, but which at the moment of writing or saying do not yet have a correspondence in the shared physical world. The ideas belonging to a merely imagined description of a goal have a greater or lesser probability of possibly occurring ‘sometime in the future’. Either there are ‘experiences’ from the past, which suggest an occurrence or there is only the ‘wish’ that these conceptions become real.

CHANGE Descriptions

CHANGE descriptions refer to such ‘events’ or ‘measures’ of which one knows (or strongly assumes) that their occurrence or their implementation ‘changes’ a given situation (ACTUAL) in at least one property in such a way that after a ‘certain time’ (‘time interval’) the ‘old’ situation represents a ‘new’ situation due to the ‘change’, which then becomes the ‘new ACTUAL situation’ as ‘successor situation’. Further events or measures can also change this new actual situation again.

Required Text Sets

While one needs at least one ACTUAL situation and at least one CHANGE description for an oksimo.R modeling (theory building), a TARGET description is optional. If no TARGET description is given, then there is a – more or less – directed or open sequence of ACTUAL states, which can arise by – also repeated – ‘applications’ of the CHANGE descriptions to a given ACTUAL situation. If at least one TARGET description is available, then this can be used to ‘evaluate’ a current ACTUAL situation according to how many elements of the TARGET situation are already present in the ACTUAL situation. This can be between 0% and 100%.

Applying change descriptions to an ACTUAL situation.

For applying a change description to a given ACTUAL situation, one must understand that in the oksimo.R paradigm a TEXT is nothing but a set of LANGUAGE EXPRESSIONS whose ‘meaning’ is known only by the speakers. Each linguistic expression is considered as an ‘element’ of this ‘expression set’ called text , and it is assumed that each linguistic expression describes some ‘property’ of the real ACTUAL situation. An imputed ACTUAL situation has exactly as many properties as the TEXT of the ACTUAL situation contains linguistic expressions. The amount of the imputed properties of a situation represent only a true subset of the real situation. If a certain expression is removed from the text, the associated property disappears; if a new linguistic expression is added, then a new property is created in the imputed ACTUAL situation.

A CHANGE description (also ‘change rule’ or simply ‘rule’) must therefore minimally do the following:

  1. Specify which expressions are to be added (generate new properties)
  2. Specify which of the previous expressions should be removed (eliminate properties).

In order to keep the application of the rule ‘under control’, one should make the application of a change rule to a current ACTUAL situation dependent on CONDITIONS in such a way that one prefixes the change specifications for ‘adding’ or ‘removing’ with a set of expressions, which must be given in the ACTUAL-description; otherwise the change rule can not become ‘active’.

Simple example

ACTUAL situation:

Gerd is sitting in his office. Gerd is hungry.

TARGET situation:

Gerd is not hungry.

CHANGE Description:

IF:
Gerd is hungry.
THEN:
Add as a property to the ACTUAL situation: Gerd leaves his office.
Remove as property from the ACTUAL situation: Gerd is sitting in his office.

APPLICATION of the change description:

The CONDITION is fulfilled.

THEN:

NEW ACTUAL situation:

Gerd leaves his office. Gerd is hungry.

EVALUATION:

The property from the GOAL: ‘Gerd is not hungry’ is not yet fulfilled, so: Success so far: 0%.

REPEATED APPLICATION

Each change rule can in principle be applied as often as possible, but only as long as the CONDITION is fulfilled.

In the example above, the CONDITION ‘Gerd is hungry’ would continue to be fulfilled, but a repeated application of the rule will not change the situation any further. Thus, it is foreseeable that the TARGET condition can never be reached in this model (in this theory).

Example with oksimo.R software

Contextualization of the software

The oksimo.R software is part of the ‘oksimo.R paradigm’. The oksimo.R paradigm includes three components: (i) As an ‘application format’ a set of arbitrary citizens who see themselves as ‘natural experts’ who ‘work together scientifically’. This format is called ‘citizen science 2.0’ in the context of the oksimo.R paradigm. (ii) The ‘oksimo.R software’ that can be used by citizens to formulate (‘edit’) their scientific descriptions of the experiential world in such a way that they ‘automatically’ meet the requirements of an ’empirical theory’, so as to be able to draw ‘inferences’ at any time, practiced as ‘simulations’. (iii) A clear concept of an ’empirical theory’ compatible with all known forms of ’empirical sciences’ (in fact, the general form of the oksimo.R theory concept can also represent all forms of non-empirical theories).

The oksimo.R software is currently being developed and deployed on a server in the Internet, accessible via the address oksimo.com.

Since the theoretical concept of the oksimo.R software covers almost everything we know so far as a software application in the Internet (including the various forms of ‘Artificial Intelligence (AI)’ and ‘Internet of Things (IoT)’), the transformation of the theoretical concept into applicable software is generally an ‘infinite process’. As of this writing (Nov 16, 2022), Level 2 is directly available and work is underway with Level 3 (and there will be much more levels in the future …)

An oksimo.R theory REALIZED WITH the software (Still level 2)

The old menu – still in command line mode – shows up as follows after logging in:

Welcome to Oksimo v2.1 02 May 2022 (ed14)

MAIN MENU
1 is NEW VISION
2 is MANAGE VISIONS
3 is VISION COLLECTIONS
4 is NEW STATE
5 is MANAGE STATES
6 is STATE COLLECTIONS
7 is NEW RULE
8 is MANAGE RULES
9 is RULE DOCUMENT
10 is NEW SIMULATION
11 is MANAGE SIMULATIONS
12 is LOAD SIMULATION
13 is COMBINE SIMULATIONS
14 is SHARE
15 is EXIT SIMULATOR
Enter a Number [1-15] for Menu Option

See: oksimo.com (16.Nov. 2022)

In the old command line mode you have to enter the oksimo.R texts manually. For the ACTUAL state this looks like this:

Enter ACTUAL description

Enter a Number [1-15] for Menu Option

4

Here you can describe an actual state S related to your problem.

Enter a NAME for the new state description:

Food1

Enter an expression for your state description in plain text:

Gerd is sitting in his office.

Expressions so far:
Gerd is sitting in his office.

Enter another expression or leave blank to proceed:

Gerd is hungry.

Expressions so far:
Gerd is sitting in his office.
Gerd is hungry.

Enter another expression or leave blank to proceed:

Name: Food1
Expressions:
Gerd is sitting in his office.
Gerd is hungry.

Note: In the Level 2 version an ACTUAL description is generally only called ‘state’.

Enter VISIONs text

Enter a Number [1-15] for Menu Option

1

Here you can describe your vision.

Enter a NAME for the new vision:

Food1-v1

Enter an expression for your vision in plain text:

Gerd is not hungry.

Expressions so far:
Gerd is not hungry.

Enter another expression or leave blank to proceed:

Your final vision document is now:
Name: Food1-v1
Expressions:
Gerd is not hungry.

Enter CHANGE rule

Enter the name of the new rules document:

Food1-Decision1

Enter condition:

Gerd is hungry.

Conditions so far:
Gerd is hungry.

Enter another condition or leave blank to proceed:

Enter a probability between 0.0 and 1.0:

1.0

(Comment: The ‘Probability’ feature at this point is now obsolete. Probabilities are handled more generally and flexibly. Examples follow.)

Enter positive effect:

Gerd leaves his office.

Positive Effects so far:
Gerd leaves his office.

Enter another positive effect or leave blank to proceed:

Enter negative effect:

Gerd is sitting in his office.

Negative Effects so far:
Gerd is sitting in his office.

Enter another negative effect or leave blank to proceed:

Summary:
Rule:Food1-Decision1
Conditions:Gerd is hungry.

Probability:
1.0
Positive Effects:
Gerd leaves his office.

Negative Effects:
Gerd is sitting in his office.

Test the effect of the theory

Test conclusions

The ‘core of an oksimo.R theory’ consists of the two components ACTUAL situation (here: state) and CHANGE rule (here: rule). By applying a rule to a state, a successor state is created, which is ultimately an ‘inference’ (a ‘theorem’) of the theory. The more complex the initial state is and the more change rules there are, the more diverse the set of possible consequences (‘inferences’, ‘theorems’) becomes. To keep track of these consequences, especially if the rules of change can be applied again and again to a successor state, so that an ever longer sequence of states emerges out of this, this can become very difficult.

Test target fulfillment

If you use an oksimo.R theory kernel together with a TARGET description, then during the inference process (the ‘simulation’) you can also check at any point how many ‘elements of the TARGET description’ already ‘occur’ in an inferred state. If ‘all’ elements of the GOAL-description occur, the theory is able to ‘infer’ 100% of the GOAL-description, otherwise less, down to 0% goal fulfillment.

Start an oksimo.R simulation

Enter a Number [1-15] for Menu Option

10

Here you can run a simulation SIM to check what happens with your initial state S when the change rules X will be applied repeatedly on the state S.

Available vision descriptions:

Food1-v1

Enter a name for a vision description you want to load. Use prefix col to load a collection:

Food1-v1

Visions selected so far:
Food1-v1

Add another vision or leave blank to proceed:

Enter a name for a state description you want to load. Use prefix col to load a collection:

Food1

States selected so far:
Food1

Add another state or leave blank to proceed:

Selected states:
Food1

Available rules:

Food1-Decision1

Enter a name for a rule or a ruledocument (with prefix doc) you want to load:

Food1-Decision1

Rules selected so far:
Food1-Decision1
Add another rule or leave blank to proceed:

Selected visions:
Food1-v1
Selected states:
Food1
Selected rules:
Food1-Decision1

Enter maximum number of simulation rounds

3

Your vision:
Gerd is not hungry.

Initial states:
Gerd is hungry.,Gerd is sitting in his office.

Round 1

Round 3

Save Simulation [S], Rerun simulation [R], export as text [T] or exit [leave blank]:

S

Enter Name for Simulation:

Food1-sim1

Saved!

Enter a Number [1-15] for Menu Option

12

Here you can load a previously saved simulation and rerun it. Add prefix dev for detailed developer-mode.

List of your saved simulations:

Food1-sim1

Restart the saved simulation with 12 Load Simulation (Comment: The math-elements are deleted from the protocol because these will be used a little bit later):

our vision:
Gerd is not hungry.

Initial states: 
Gerd is hungry.,Gerd is sitting in his office.

Round 1

Current states: Gerd is hungry.,Gerd leaves his office.
Current visions: Gerd is not hungry.
Current values:

0.00 percent of your vision was achieved by reaching the following states:
None
And the following math visions:
None

Round 2

Current states: Gerd is hungry.,Gerd leaves his office.
Current visions: Gerd is not hungry.
Current values:

0.00 percent of your vision was achieved by reaching the following states:
None
And the following math visions:
None

One can easily see, that the state of round 2 is repeating. And there is no reason, that this will change in the future.

Rule application and inference concept

The preceding simple example was used to explain concretely what happens when a rule is applied to a given ACTUAL situation. A science which deals with such change processes by means of rule application(s) is ‘logic’. Logic considerations have been around for more than 2500 years, in many different forms. The most significant logic paradigms in retrospect are possibly the logic associated with the name of Aristotle, in which logical expressions were not yet considered in isolation from possible linguistic meanings, and modern formal logic, in which logical expressions have no connection to any linguistic meaning except with abstract ‘truth values’. The history of modern formal logic began in the 19th century about 150 years ago (Bool, de Morgan, Venn, Frege, Russell, …).

The central idea of any logic is to find a ‘procedure that allows the user to ‘derive’ from a set of ‘assumed to be (abstractly) true’ statements only those statements that are also ‘(abstractly) true’ again. The ‘abstract truth’ of modern formal logic is a ‘placeholder’ for an everyday language truth which cannot be expressed as such within a formal logic. Formal logic presupposes that there are ‘actors’ who ‘know’ what they are saying when they speak of a ‘true’ statement. Whether the formalization of ‘truth relations’ between different sets of expressions in the format of modern formal logic ‘adequately’ represent the meaning knowledge of the actors can therefore not be decided ‘within the logical system’, but only ‘from outside’, from the perspective of the ‘meaning knowledge of the acting actor’.

If one calls the initial set of linguistic expressions ‘assumed to be abstractly true’ an IS-description (in the style of the oksimo.R paradigm) and the set of possible ‘derived expressions assumed to be abstractly true’ the ‘inferred abstractly true expressions’, then one could formulate this in the style of formal logic as follows:

IS-STATEMENTS ⊢CHANGE-RULES GENERATED-POTENTIAL-IS-STATEMENTS.

or abbreviated:

X  R X‘

The character ‘⊢’ represents an inference term. This consists of a text describing how to apply a change rule from the set R to a given set of expressions X in such a way that a new set X’ is created as a result of the application to the given set X. The inference term must be of such a nature that it is completely unambiguous ‘what to do’.

The claim of the ‘pure formal logic’ of the modern times that all expressions, which are generated with the inference term, are also conform to the ‘assumed abstract truth value’, applies in the same way to the inference term of the oksimo.R theory software too. With the oksimo.R inference term it is guaranteed that all ‘generated expressions’ are ‘true’ in the sense of the ‘linguistically founded meaning knowledge’ of the involved ‘actors’! However, linguistically grounded meaning knowledge is ‘knowledge dependent’ and therefore can be empirically either ‘true’ or ‘false’ or ‘indeterminate’. This points to the fact that in general the actors are the ‘gatekeepers of the truth’. Actors formulate the change-rules R based on their linguistic knowledge. If these change-rules R are ‘true’, then this is also true for the linguistic expressions generated by means of inference. If the change rules R contain an ‘error’, then this error will necessarily be contained in the generated inference situation X’ as description element E. This expression element E as part of the prediction set X’ may then turn out to be either ‘true’ in the further course of comparison with the commonly shared empirical reality, or it will remain ‘indeterminate’ in the long run, since it neither becomes ‘true’ nor can be directly classified as ‘false’. In the case of modern formal logic, the empirical truth status of inferred expressions is completely indeterminate.

The oksimo.R inference concept united the formal advantages of modern formal logic with the meaning reference of Aristotelian logic and is understood as a ‘natural means of expression’ for an empirical theory with truth claim.

This post has a continuation (Pert 2) HERE.

BOOK: oksimo.R Editor and Simulator for Theories. A Philosophical Essay

eJournal: uffmm.org
ISSN 2567-6458, 6.November 2022 – 24.January 2023
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

This post is part of the book project ‘oksimo.R Editor and Simulator for Theories’.

STRUCTURE OF THE TEXT

(Last change: 24.January 2023)

(Partially translated with www.DeepL.com/Translator (free version))

During the writing it becomes apparent that it is difficult to concretely specify the concrete contents at the beginning of the writing! The ‘cloud of knowledge’, out of which this book is written, is not a static object, but a space of ‘transient events’, which – in the first moment, seen from close – appear like ‘fixed contents’, but they are only momentary states of equilibrium in a manifold network of thought processes, which all take place at the same time, largely unconsciously (the way our brain works), and which produce ‘each other’. So it can happen that in a ‘living thought’ these transient states ‘rewrite’ themselves again and again. The ‘truth’ is then the total process, which can ‘connect’ with individual empirical events. Some may be ‘disturbed’ by this state of affairs, others feel ‘liberated’ as they begin to suspect that recognition, understanding, truth belong to a completely different dimension than the seemingly ‘clear, delimited, fixed facts’.

At this point one could be tempted to use the millennium old term ‘spirit’ (rather not ‘mind’) (Greek: ‘pneuma’, ‘πνευμα’) to denote this elusive ‘more’ of knowledge, but it would not help too much, would perhaps even complicate the whole state of affairs, since one then relates with a ‘known word’ something ‘not understandable’ to classical Greek thinking, which at that time did not and could not know the ‘context of its own thinking’. [0]

[0] Which does not detract from the fact that the classical Greek authors shine with a mental brilliance that can touch anyone who tries to think for himself. I myself am, among other things, very impressed by the texts attributed to Aristotle. Every minute that one can immerse oneself in his texts is a ‘gift to thinking’.

IDEA OF THE BOOK

(Last change: 3.January 2023)

START

(Last change: 8.January 2023)

What to assume for a minimal scenario?
The ‘inside’ of the ‘outside’ – a few hints
The ‘inside of the outside’, Part 2 (Last change: 18.January 2023)

INTRODUCTORY EXAMPLES and First Comments

EVERYDAY SCENES

(Last change: 28.November 2022)

CONNECTED SYSTEMS

(Last change: 15.November 2022)

  • Population
  • Water
  • Interconnecting population and water (world 1)
  • Nutrition
  • Interconnecting world 1 with nutrition (world 2)
  • Raw materials
  • Interconnecting world 2 with raw materials (world 3)
  • Energy
  • Interconnecting world 3 with energy (world 4)

EXPLANATION BOXES

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  • World, Space, Time (Last change: 25.November 2022)
  • Everyday language
  • Formal languages
  • Actors
  • Sign systems
  • Meta language
  • Logic
  • Formal Theories
  • Empirical Theories
  • System
  • Dynamic System
  • Networked Systems
  • Embedded Systems

oksimo.R PHILOSOPHY

(Last change: 15.November 2022)

  • Real world and linguistic description
  • True, false, indeterminate
  • Actors: humans and others
  • Communicative Processes
  • Virtualization of the world inside the actors
  • Competition of ‘dreams’
  • Emotions rule the mind
  • ‘Locked In’
  • Rescuing only through mistakes and catastrophes?
  • Evolution takes place
  • The ‘spiritual matter’
  • Epilogue

Book: oksimo.R – Editor and simulator for theories

eJournal: uffmm.org
ISSN 2567-6458, 10.November 2022 – 24.January 2023
Email: info@uffmm.org
Author: Gerd Doeben-Henisch
Email: gerd@doeben-henisch.de

CONTEXT

The English translation from the German source is partially generated with the www.DeepL.com/Translator (free version).

Main Text: oksimo.R Editor and Simulator for Theories …

(Last change: 24.January 2023)

Brief Description of the Book

(Last change: 6.November 2022)

When it comes ‘to the oath’, when it is the task of creating descriptions of the world which are ‘verifiable’ by others, and which allow ‘verifiable predictions/conclusions’, then there is so far in the cultural history of mankind only one format known that makes this possible, the format of an ‘Empirical Theory’. If you try to look up the term ‘Empirical Theory’ in the German or English Wikipedia, you will be disappointed: this term does not exist there (as of November 10, 2022). This should cause astonishment, because it is so far the only and hardest criterion for a ‘truthful theory’ found in the last thousands of years. There are endless articles and books on this subject.

However, it is also part of the truth that the formats of texts that have become known so far with the claim to realize a verifiable empirical theory ultimately work with a so-called ‘formalization’, i.e. the language of logic and mathematics is used. One consequence of this is that the amount of possible readers and users of such theories is severely limited by this language alone. This is a major disadvantage, since it effectively ‘locks out’ the majority of citizens in a society.

But scientists themselves have a problem too: for formalized empirical theories, there is no supporting software that allows the text of a theory to be checked ‘with the push of a button’ at any time in the form of a simulation. Although there are — partly highly complex — simulation programs for support, these are not theories as such, but only algorithms and have to be laboriously created alongside the theory itself. If the text of the theory changes, such a change must be transferred laboriously into the algorithm. In general: an algorithm is a ‘function’ which is neither true nor false; a theory is a ‘statement’ which as such can be true or false (or it can stay ‘undefined’ with regard to ‘truth’ or ‘falsehood’).

After many years of research — since about the mid 1980s and then experimentally for about four years — an approach has emerged which puts the concept of a testable empirical theory at the center, and takes as the starting language for an empirical description of the world the ‘normal language of everyday life’ (any is possible). This language can be ‘extended’ at will (which is common in science), but instead of virtually ‘throwing away’ the everyday language after the extension, in the new approach the everyday language is not thrown away but remains the main language.

This new approach — labeled with the acronym ‘oksimo.R’ [1] — enables the users — every kind of citizens — to write down a text which automatically represents everything known from — even formalized — theories. Above all: every citizen can read and understand texts written in the oksimo.R format normally. Every text in the oksimo.R format is automatically equivalent to a full empirical theory, even if the authors — arbitrary citizens — do not know exactly what an empirical theory is. Furthermore: at the push of a button, any theory in the oksimo.R format can be run as a ‘simulation’, where the term ‘simulation’ is very concrete here: the core of the simulation is formed by an ‘inference concept’, which computes the respective possible ‘continuations’ (= ‘inferences’) from given ‘world descriptions’ extended by possible ‘changes’, and this not only once, but ‘again and again’, until this inference process is stopped on the basis of a given criterion. Since every inference is linked to an empirical truth claim, every inference can also be checked for its empirical validity.

While up to now formalized empirical theories are very difficult to compare or even to ‘unify’, this is no problem for empirical theories in the oksimo.R format: one can unify arbitrary empirical theories in the oksimo.R format ‘at the push of a button’ to one text only and one can then directly view its effects by simulation. Of course, actual ‘interactions’ between the different ‘theory parts’ only arise if there are ‘linguistic points of contact’. But exactly this can be seen immediately with a unified simulation: Either there are no interactions at all or you detect some interactions. These ‘some interactions’ can be analyzed further with regard to the question, what interacts and how.

By the way, all forms of artificial intelligence (ai) in the format of ‘machine learning (ML)’ known today — which usually represent very simple algorithms and which are in addition extremely dependent on a formulated task — can be used fruitfully in the context of an oksimo.R theory text by using these AI algorithms to search the ‘state space’ of possible conclusions for possible optimizations.

Furthermore, one can arbitrarily extend the empirical references to the real world through appropriate sensor technology and data connections (e.g., IoT), all in ‘web real-time’.

Additionally, one can link any server with an oksimo.R theory to any other web application.

Instead of ‘only’ developing and testing theories, oksimo.R theories can also be used to control processes or as training and text environments. Even (online) games are possible.

This opens up interesting possibilities for a new level in people’s ‘collective knowledge’ that goes beyond mere ‘text sets’.

COMMENTS

[1] The acronym ‘oksimo.R’ points back to a language project called ‘oksimo’ (‘open knowledge simulation modeling’), which has been documented a little bit in the German wikipedia ( https://de.wikipedia.org/wiki/Oksimo ). This project initiated by Gerd Doeben-Henisch had been 2009 stopped by him despite a great public awareness because of lack of resources. The ‘new oksimo’ as ‘oksimo.R’ means ‘oksimo reloaded’; it has somehow a similar intention but is designed with a completely different internal structure. It does not produce ‘algorithms’, it does produce ‘theories’.