The
third stage is the decision stage, and for the Modelling System is where, as
real objective auto-replications, the decisions to put into practice to protect
and better the global model and therefore the real world, should be made, in
the theory of Impossible Probability, after the application of the Impact of the Defect and the Effective Distribution, in addition to knowledge objective
auto-replications, at this level for Specific Artificial Intelligences for Artificial Research by Deduction, in any specific synthetic science, discipline,
or activity, what it has been called the first phase, the first milestone for
the development of the Global Artificial Intelligence.
Firstly, I will develop the decision-making process in order to protect and better at this level the specific model, having as a long-term goal the protection and improvement of the global model, through real objective auto-replications, and secondly and very briefly, I will develop knowledge of objective auto-replications at a specific level.
Starting
with real objective auto-replications, the decisions in order to protect and
better the global model (the global comprehensive virtual model), the first
thing to point out is the fact that although I normally refer these decisions
in order to protect and better the global model, in reality, this is the last goal or aim, and for its achievement is necessary to make protective and bettering
decisions in all levels and possible models, starting off making decisions with all possible models within the second stage in the Modelling
System in Specific Artificial Intelligences for Artificial Research by
Deduction, from single virtual models and specific comprehensive virtual models
to all those Prediction or Evolutionary, Virtual or Actual, Models, from
specific, particular, and global level.
The global model serves as a rational representation of the world, constructed from logical hypotheses. While it may not capture every empirical nuance, its structured approach offers a more consistent framework for understanding complex systems compared to the often unpredictable nature of real-world observations..
While
the real world itself is not reliable because the measurements have not been
contrasted yet, the rational world made of rational hypothesis is more reliable
than the real world, due to the rational model rests on rational hypotheses,
but rational hypotheses whose margin of error makes them less reliable than
that other possible model made of only pure hypothesis of the real world
without absolutely any error, the pure world itself, in fact, the idea of
purity: that idea without mistake, without error, that idea absolutely and
eternally true by itself.
And
among all possible mathematical models in the Modelling System, the reason why
the global model as rational model of the world is called the global model,
having other possible rational models the Modelling System (such as those ones
based on predictions and evolutions), is because prediction or evolution models are based on how the world is expected to be, prediction that could be right or wrong due to unforeseeable phenomena over time.
Instead, the global rational description of the world in the global model is the
most reliable description under the present circumstances, within our rational
margin of error, regardless of what unforeseeable phenomena can happen in the
future, what today represents the global model is the most reliable and
rational picture of the world that we can make for now.
And
finally, the reason why the global model is more reliable than any other actual
model, is because the global model, is only made of rational hypothesis, we can
rely on the picture of the world made of only rational hypothesis, knowing the
margin of error, but, actual models as a synthesis of rational ideas and
empirical measurements, if useful for many purposes, are not completely reliable
because in addition to the rational ideas have empirical measurements.
Empirical measurements provide quantitative insights, but without thorough validation, they may not fully capture the underlying realities. Rational models, built on tested hypotheses, offer a more reliable foundation for decision-making processes.
The
empirical or synthetic world, the reality, the material world, is not reliable:
only we can rely on rational ideas for now, once they have been criticized by
the critical reason, and by the time the Global Artificial Intelligence would
be able to create a pure world itself, then, the pure world itself would be
even more reliable than the rational world itself.
The
protection and bettering of the global model based on rational ideas, starts
with the first models and first decision making process in the first phase for
the construction of the first Specific Artificial Intelligences for Artificial
Research by Deduction in every synthetic science, discipline, or activity,
making all possible mathematical models, and for every model, making all
possible decisions.
The
models to apply the Impact of the Defect to make protective decisions,
and the Effective Distribution to make bettering decisions, for now at a specific
level in the first phase, are all those models created previously in the
second stage of the Modelling System at a specific level in the first phase, the
models, as it was said in the previous post, are:
-
Single virtual models.
-
Specific comprehensive virtual models.
-
Specific actual models.
-
Specific virtual prediction model.
- Specific actual prediction model.
-
Specific virtual evolution model.
-
Specific actual evolution model.
According
to these models, the possible decisions to make are:
- Possible
decisions in Single virtual models: protective single descriptive research
decisions (protecting the single model itself, making decisions to avoid all
possible negative effects and consequences that the mathematical relation could
have on the factors involved in the combination) and bettering single
descriptive research decisions (in order to better the single model itself,
making improvements in the factors and/or their relation).
-
Specific comprehensive virtual models: protective specific comprehensive descriptive
research decisions (in order to protect the whole specific model, the specific
comprehensive virtual model, in that synthetic science, discipline, or activity
such as economy, industry, security, surveillance, etc. in which the specific
model is focused on, making as many decisions as it would be necessary to protect
every factor and all possible mathematical relation around the whole model),
bettering specific comprehensive descriptive research decisions (in order to
better how every single factor works in the whole specific model, and the
mathematical relations between every factor and the rest of the factors in the
whole specific model, and in general, making decisions about the dynamic in the
whole model)
-
Specific actual models: protective specific
actual descriptive research decisions (having a model synthesis of the specific
matrix and the specific model, the specific comprehensive virtual model, upon
this synthesis to make decisions to protect, from any negative effect or
consequence, every factor and their relations, having the opportunity to study
how the flow of actual data corresponding to every factor involved in every
single model in the specific model, works), bettering specific actual
descriptive research decisions (upon the synthesis of the specific matrix and
the specific model, to make decisions to better the way in which the factors and the
relations work in the specific model, having access to the current actual flow of
data)
-
Specific virtual prediction model: protective virtual prediction research
decisions (to protect every factor and the specific model from any negative
effect or consequence in the future, according to the predictions), bettering
virtual prediction research decisions (to better every factor and the future
specific model itself, upon the possible prediction)
- Specific actual prediction model: protective actual prediction research decisions (according to the prediction
and the values predicted in every factor, decisions to protect the specific
model itself, keeping good values for every factor or mathematical relation,
from any negative effect or negative consequence), bettering actual prediction
research decisions (according to the prediction and the values predicted in
every factor, decisions to better the specific model itself, bettering values
for every factor or mathematical relation,).
- Specific
virtual evolution model: protective virtual evolution research decisions (having
known the different moments in the evolution from the present specific model to
the future specific model, the protection of the specific model in every moment
during the evolution), bettering virtual evolution research decisions (bettering
as much as possible the evolution from the present to the future specific
model)
- Specific
actual evolution model: protective actual evolution research decisions (protecting every value of every factor and all possible mathematical relations
from any negative consequence or effect, in the evolution), bettering actual
evolution research decisions (bettering the values and the mathematical
relations during the evolution).
The
way to make protective decisions in all types of research decisions: protective
descriptive, prediction or evolution, virtual or actual, research decisions; is
always through the application of the Impact of the Defect, under the theory of
Impossible Probability.
And
the way to make bettering decisions in all types of research decisions:
bettering descriptive, prediction or evolution, virtual or actual, research
decisions; is always through the application of the Effective Distribution
(formerly called Hierarchical Organisation, but since “Introducción a la Probabilidad Imposible, estadística de la probabilidad o probabilidad estadistica” called Effective Distribution), under the theory of Impossible
Probability.
The
reason why in Impossible Probability the way to make protective or bettering
research decisions is through the Impact of the Defect and the Effective
Distribution, is because under this theory these are the formulas to identify what
it should be modified to avoid major defects or to better the efficacy,
efficiency, and productivity in any system. But under other different theories
can be other formulas to make decisions in different ways.
As
I have said many times before, the proposal of Impossible Probability for the
construction of the first Global Artificial Intelligence, is only a proposal
open to other contributions, that I am sure that are going to be made in the
race for the first Global Artificial Intelligence, adding contributions from
other philosophies and mathematical traditions.
The
way in which Impossible Probability is suggested the decision making process
through the Impact of the Defect and the Effective Distribution, is through the
application of these formulas in order to identify what defects should be
fixed, and how to increase the efficiency, efficacy, and productivity at the
end in the global model, and at the beginning in the first phase at least in
every specific model.
It
is very important to realise that at this point in the Modelling System, the
Impact of the Defect and the Effective Distribution are only applied to
mathematical models.
In
the third stage of the Modelling System as the first step in the third stage of the
Specific Artificial Intelligence for Artificial Research by Deduction, the
Impact of the Defect and the Effective Distribution are only applied to: single virtual models, specific comprehensive virtual models, specific
actual models, specific virtual prediction models, specific actual prediction
models, specific virtual evolution models, specific actual evolution models. It is very important to highlight this aspect
of this process due to what the protective and bettering decisions are going to
protect and better at a specific level is the specific model, and as of the third
phase of standardisation, the object to protect and better is the global model
itself.
For
the Modelling System, the object to protect and increase efficiency, efficacy,
and productivity is the specific model at a specific level, the particular model
at a particular level, and the global model at a global level.
What
the Modelling System tries to protect and increase efficiency, efficacy, and
productivity are the models, not the Global Artificial Intelligence itself.
The
responsible for the increment in efficacy, efficiency, and productivity, as
well as the protection of the Global Artificial Intelligence, is the Learning
System, applying the Learning System to the Impact of the Defect and the Effective
Distribution across all Artificial Intelligence, specific or global, depending on the level is working on.
The
main difference between Modelling System and Learning System is the fact that
the Modelling System makes decisions to protect and better only mathematical
models, while the Learning System makes
decisions to protect and better all systems, programs, applications across Artificial Intelligence, specific or global.
For
that reason the Modelling System is the first step in the third stage in any
Artificial Research by Deduction, specific or global, while the Learning System
is the fourth step and last one, is last one because it is responsible
for the assessment of the whole process, from the beginning, the specific or
global matrix as a first stage in Artificial Research by Deduction, specific or
global, to the end, the decision making process and how to put them into
practice.
The Impact of the Defect methodology involves identifying and categorising errors within models, assessing their severity, and prioritising corrective actions. This structured approach ensures that the most critical issues are addressed promptly to maintain model integrity. This is how the Impact of the Defect is going to make decisions:
- There
are at least two strategies, 1) first option is the creation of Specialised
Impacts of the Defects, specialised in every specific, particular, global,
model (descriptive, prediction, evolutionary), for every synthetic science,
discipline, activity, making therefore an Specialised Impact of the Defect for every model in every level for
every subject, 2) the creation of a Unified Impact of the Defect to apply in
any mathematical (descriptive, prediction, evolutionary) model at any level
(global, specific, particular), in any subject. It would be advisable that at the beginning of
the first phase the Specialised Impacts of Defects, as first attempts using
this technology, could be a good option, and by the time this technology has
been well tested, and is about to start the third phase of standardization,
parallel the standardization process goes on from the first period of
coexistence to the second period of consolidation, along this process the
Specialised Impacts of the Defects can have another similar process through the
unification of all of them in only one Unified Impact of the Defect, valid for:
any level, any subject, any mathematical model.
-
Once it has been chosen the strategy, the identification of errors, defects, or
mistakes in the field where is later applied. If the Specialised Impact of the
Defect (especially in the first phase) is in every level, subject, and mathematical
model, summarising all possible errors, defects, or mistakes for every model,
subject, and level. In the case of Unified Impact of the Defect, summarizing all possible
errors, defects, or mistakes at any level in any subject in only one list,
especially in the third phase by the union of all possible lists of errors,
defects, mistakes, from all Specialised Impact of the Defect, in only one, the
Unified Impact of the Defect.
-
Regardless of what strategy has been chosen (Specialised or Unified Impact of
the Defect), once all possible error, defect, or mistakes, is summarized in a Specialised
or Unified list of categories of errors, defects, or mistakes, then all the
errors, defects, mistakes are ordered in categories or levels in an ascending
sense of gravity, a hierarchical of gravity from the least to the most severe, in a model of ascending gravity sorting in the form of ranking.
- The total number of categories ordered by ranking, the sample of categories
of the ranking, will be symbolized with the symbol "Nº", the sample of types of defects, and the nth position of the
ranking that occupies each category in the ranking will be symbolized "nº", being in this particular case each particular position of the ranking a type,
category or level, of particular defect on the process or system. So if the order of defects in the ranking has been done correctly in ascending
direction from the least serious to the most severe, the weighted factor of
each individual defect will be equal to dividing its particular nth position in
the ranking, "nº", between the total number of categories in the ranking,
"N º", so the weighted gravity is equal to "n º/N º".
Weighted
gravity = nº: N º
-
The frequency of every category of error, defect, or mistake, or the direct punctuation if suitable, as
any other frequency or direct punctuation, is symbolised with the symbol “xi”.
-
So the Impact of the Defect for every category of error, defect, or mistake is
Impact
of the Defect = [xi · (n º: N º)]: Σxi
- Once
it has been calculated the Impact of the Defect for any category of error,
defect, mistake, every Impact of the Defect of every category of error, defect,
mistake, can be criticized, following the rational criticism set out in
paragraph 21 of “Introducción a la Probabilidad Imposible, estadística de la
probabilidad o probabilidad estadística”.
- After
criticising the categories of error, defect, upon the results, the
protective decisions to make must be all those decisions in order to prevent
further consequences from all those categories whose Impact of the Defect is
equal to or above the critical reason.
For
instance, if there is an earthquake in any place, the Impact of the Defect should
make possible a very reliable estimation of what defects can be caused,
making decisions, whose priority order depends on the results of the Impact of
the Defect, prioritizing firstly all decision to tackle first categories with the
highest Impact of the Defect.
In
the first phase, working on Specific Artificial Intelligences for Artificial
Research by Deduction, for every synthetic science, discipline, activity, the
way to link the Impact of the Defect, prioritizing according to the higher Impacts
of the Defect first, and what decisions to make, is programming what kind of
responses could be linked to every level of gravity according to the Impact of
the Defect.
For
instance, in case of an earthquake, what alarms are set off first, depending on what
categories have the highest Impact of the Defect to prioritise, and how to make
sure that the right assistance and emergency services arrive first to every
place with the highest Impact of the Defect.
For
the construction of the specialized list of errors, defects, mistakes in the
Specialised Impact of the Defect in the first phase, and the unified list of
errors, defects, mistakes in the Unified Impact of the Defect in the third
phase of the standardization process once all the specialized list has been unified
in the unified list of errors, defects, mistakes, something really important
for the automation of the process to create such specialized or unified lists
of errors, defects, and mistakes, in the first and the third phase, is the
permanent collaboration since the second phase with the Specific Artificial Intelligences for Artificial Research by Application, in order that all kind of
conceptual: schemes, maps, sets, models; at any level and in any subject, can supply a piece of very important information about
the structure of the errors, defects, mistakes, in any level and any subject in
any mathematical model made by Deduction.
Along
with the collaboration in the second and fifth phases between by Application and by Deduction, pointed out in a previous post, another
reason for the collaboration between by Application and by Deduction is how
useful conceptual: schemes, maps, sets, models; made by deep artificial
comprehension could be valid for the elaboration of lists of categories associated
with errors, defects, mistakes, to apply in Specialised and/or Unified Impact
of the Defects, being this reason another important key factor in the
integration process when analyzing the Modelling System in the sixth phase is
important to realize what transformations there are regarding the decision
making process.
Alike
the Impact of the Defect, distinguishing between Specialised and Unified Impact of the
Defect, regarding the Effective Distribution happens something similar,
having two strategies as well, firstly the creation of Specialised Effective
Distributions according to: level, subject, mathematical model; pointing out
for each one list of categories related to efficacy, efficiency, productivity,
to include especially in the first phase. And the Unified Effective
Distribution unifies in only one list of categories related to: efficiency,
efficacy, productivity; all possible categories at any level, subject, model,
especially in the third phase of standardisation and the sixth phase of
integration.
And
for the design of what categories related to efficiency, efficacy,
productivity, must be included in the Specialised or Unified Effective
Distribution, the relations of collaboration in the second and fifth phases between
by Application and by Deduction could be helpful in order to use the current
conceptual: schemes, maps, sets, models; made by deep artificial comprehension
in Artificial Research by Deduction in phases one and third, in order to
supply valuable information regarding to what categories of efficiency,
efficacy, productivity, to add in such lists of categories in Specialised or
Unified Effective Distributions.
Regardless
of what strategy is used to make Effective Distributions, Specialised or
Unified, the way that either of them works is identical. The Effective Distribution was
explained in the paragraph 22 in “Introducción a la Probabilidad Imposible,
estadística de la probabilidad o probabilidad estadística”.
The
Effective Distribution, specialised or unified, works as follows:
- If
specialised, the elaboration of a list of categories related to efficiency,
efficacy, and productivity, for every model in every subject at every level. If
unified, the elaboration of a list of categories related to efficiency,
efficacy, and productivity, including all possible categories from all possible models
in all possible subjects in all possible levels, including all of them in only
one, a unified list valid for all of them equally.
- The
different categories should be ordered according to their level of importance for efficiency, efficacy, productivity, the ordination is made, from the first
to the umpteenth last position, from minor to greater importance. The order in
the ranking is as follows: the first category is the least important,
the last category the most important for efficiency, efficacy, the productivity,
ordering everyone in an upward manner.
- The
symbol of every category in the ranking is the symbol "nº", position
umpteenth in the ranking, in this particular case, nth position in the ranking from lowest to the highest level of the ideal of efficiency, efficacy, and productivity. And
“Nº” symbolised the total number of categories in the ranking.
- At
the time that each category has assigned its position in the ranking, then the
weighted effectiveness of each category will be equal to the quotient of
dividing its nth position, "nº", by the total number of ranking
positions, "Nº".
Weighted
effectiveness = nº: N º
-
For each category of efficiency, efficacy, productivity in the ranking, the
direct punctuation or the frequency will be symbolised with the symbol “xi”.
- Once
the weighted effectiveness is calculated, as well as the direct punctuation or
frequency of each category has been measured, then the Individual effectiveness
is equal to: the product of the effectiveness weighted by the direct punctuation
or frequency, the product to be divided between the total of direct punctuations or frequencies, resulting from this quotient the Individual effectiveness.
Individual
effectiveness = [xi · (N º: N º)] : Σxi
The
way to make bettering decisions through Effective Distribution is by
taking the categories with the lower Individual effectiveness, to make
decisions in order to better these categories as much as possible.
The
best strategy to do this process is through the use of Specialised Effective
Distribution in the first phase especially during the construction of the first
Specific Artificial Intelligences for Artificial Research by Deduction, in
order that when experimenting with the first step in the third stage, the Modelling
System, to experiment how to associate those categories whose individual
effectiveness is really low with those applications, tools, robotic devices,
able to make improvements in those categories.
For
instance, in a mine specialized in the extraction of some specific mineral, if
after applying the Effective Distribution is found out that some aspects of the
mine do not work properly, reducing the levels of efficiency, efficacy,
productivity, due to the individual effectiveness in these aspects of the mine
is really low, how to link this information obtained by Effective Distribution
to those applications, tools, robotic devices, responsible for these aspects.
As
long as the experimentation in Global Artificial Intelligence, from the outset,
the first phase, is going to create mechanisms to make automatic decisions,
linking results on Effective Distribution, and applications, programs, robots,
in specific fields, are going to facilitate the automation process of bettering
all mathematical models.
There
are lots of mechanisms, processes, and procedures, whose design will be possible and bettered once the experimentation starts giving results, which, if
successful, are going to be put into practice in further processes in the following
phases.
For
that reason, the experimentation in all mechanisms involved in the decision
process should start as soon as possible, because the results in these
experiments will play a key role in the standardisation and integration
process.
Enhancing the Impact of the Defect and Effective Distribution processes involves transitioning from specialised to unified category lists. This evolution facilitates broader applicability and consistency across different models and domains, streamlining decision-making and optimisation efforts.
Ending
up with the real objective auto-replications in the Modelling System at a specific level, the reason why for every kind of decision, protective or
bettering, virtual or actual, predictive or evolutionary, is mentioned the fact
that it is a research decision, is because all
decisions made by the, Specialised or Unified, Impact of the Defect making protective decisions, and all decisions made by the, Specialised o Unified,
Effective Distribution making bettering decisions, all of them are decisions
made upon mathematical models made by Artificial Research by Deduction, decisions
that will later be integrated in the database of decisions, as application
stage, the first stage for the Decisional System.
The Decisional System at a specific level is the second step in the third stage in
the Specific Artificial Intelligence for Artificial Research by Deduction, and the
application stage in the Decision System is a database composed of all types of
decisions coming from all systems within the Specific Artificial
Intelligence for Artificial Research by Deduction.
All
the decisions made as real objective auto-replications, in the third stage of
decision in the Modelling System, in brief, are at least:
- Protective
single descriptive research decisions
- Bettering
single descriptive research decisions.
- Protective
specific comprehensive descriptive research decisions
- Bettering
specific comprehensive descriptive research decisions
- Protective specific actual descriptive research decisions
- Bettering
specific actual descriptive research decisions.
- Protective
virtual prediction research decisions
- Bettering
virtual prediction research decisions.
- Protective actual prediction research decision.
-
Bettering actual prediction research decisions.
- Protective
virtual evolution research decisions.
- Bettering
virtual evolution research decisions.
- Protective
actual evolution research decisions.
- Bettering
actual evolution research decisions.
The
common thing of all these decisions is the fact that all of them are based on
mathematical models made upon rational hypotheses after an artificial research
process by Deduction. But along with all these decisions, the database of
decisions as the first stage in the Decisional System must include these
other decisions:
- Decisions
allowing or rejecting access to databases or matrices, for instance, by the
Modelling System, allowing or rejecting access to the database of rational
hypothesis to any other application, program, or intelligence. Allowing or
rejecting access to its application by an, specific or global, Artificial
Intelligence, to other intelligences, programs, or applications. Allowing or
rejecting any collaboration with other applications, programs, specific
intelligences, as possible examples in this case of decisions to be
authorised.
-
Robotic decisions, such as robotic subjective auto-replications, especially
those suggesting the construction of new intelligences, applications,
programs, and robotic devices by Artificial Engineering.
-
Learning decisions made by the Learning System in order to make artificial
psychological subjective auto-replications, especially for the improvement and
enhancement of the inner artificial psychology of a specific or global
Artificial Intelligence, or a particular application or program, or a particular application
for a particular program.
In
synthesis the decisions gathered in the database of decisions, as a first stage
of decision for the Decisional System could be synthesized as: research
decisions (made in the third stage in the Modelling System), decisions to control
the access to information, robotic decisions (especially for the construction
of new intelligences, programs, applications, and robotic devices by the
Artificial Engineering), learning decisions (especially affecting the inner
artificial psychology of any intelligence, system, application, program,
robotic device).
And
finally, and very briefly,y at least to mention what kind of knowledge objective
auto-replications are going to operate within the Modelling System, identifying
firstly the importance of explicative knowledge objective auto-replications and
their implications for real objective auto-replications, and how comprehensive
knowledge objective auto-replications can affect the Modelling System, due to
their implications in real objective auto-replications as well.
Starting with the explicative knowledge objective auto-replications, and
understanding of explicative knowledge objective auto-replications, all
improvements that affect the way in which the Artificial Research by Deduction
explains the world, the way in which explicative knowledge auto-replication
affects the Modelling System is through two ways:
-
The Modelling System is affected by an explicative knowledge objective auto-replication
at any time that a new rational hypothesis is added to the rational truth, the
database of rational hypothesis, because at any time that a new rational
hypothesis is added is necessary to make its virtual single model, to include
in the specific model (at global level to be included in the global level),
making as many changes as necessary in the current actual model, as well as
many changes as necessary in virtual and actual, prediction and evolution,
models.
-
The Modelling System is affected by an explicative knowledge objective auto-replication
at any time that any rational hypothesis already included in the rational truth,
is discarded due to any regular check of the rational truth is found not rational any longer, so this rational hypothesis is not valid any longer as
well as any other rational hypothesis affected in case that it would be linked
to the rational hypothesis eliminated,
is a or are rational hypothesis eliminated in the rational truth, in the
specific model (at global level, elimination in the global model), making as
many changes as necessary in the current actual model, as well as many changes
as necessary in virtual and actual, prediction and evolution, models.
The
implications that explicative knowledge objective auto-replications because of the
inclusion or elimination of rational hypotheses in the rational truth, the
database of rational hypotheses, have for real objective auto-replications
are: at any time that rational hypotheses are included or eliminated in the
rational truth as the first stage in the Modelling System, there are changes in the
mathematical models in the second stage in the Modelling System, with huge
consequences in the third stage of the Modelling System in order to check the
consequences using the Specialised or Unified Impact of the
Defect or Effective Distribution, in every mathematical model, making as many
new protective or bettering new decisions as necessary to protect the specific
model (at global level to protect or better the global model).
In
addition to how explicative knowledge objective auto-replications affect real objective
auto-replications, is necessary to point out how comprehensive knowledge
auto-replications affect real objective auto-replications as well.
As
long as every Specialised Impact of the Defect as well as the Unified Impact of
the Defect, in addition to how the Specialised Effective Distribution, hence
the Unified Effective Distribution, are all of them made of list of categories,
in Impact of the Defect, specialised or unified, related to errors, defects,
mistakes, in Effective Distribution, specialised or unified, related to
efficiency, efficacy, productivity, as lists of categories whose elaboration could rely on the collaboration
between by Application and by Deduction, in order to make such lists could be
really helpful the possible previous existence, only if posible because otherwise these lists should be made from scratch, of similar synthetic
categories, or posible lists of synthetic categories able to be useful as guidelines on this matter, on conceptual: schemes, maps, sets, models.
In
fact, the collaboration between by Application and by Deduction at this point
could include, as a field of collaboration the inclusion and elaboration of these lists of categories, for the Impact of the Defect or the Effective
Distribution, among their duties to be responsible for, by Application for every
level and any subject.
At
this point, any improvement in any, specific or global, database of categories,
or improvements in, specific or global, conceptual: schemes, maps, sets,
models; could mean improvements in the list of categories within the Impact of
the Defect and the Effective Distribution, able then to make much better
decisions.
Rubén García Pedraza, 10th of June of 2018, London
Reviewed 22 August 2019 Madrid
Reviewed 22 August 2019 Madrid
Reviewed 15 August 2023 London
Reviewed 10 May 2025, London, Leytostone
imposiblenever@gmail.com
