Computers are known as devices used to play complex games based on
intelligence, to write texts of different types, to make calculations, to
store and manage data, to send or receive information, to build and operate
symbolic models, etc.
A question occurs however: which is the principle of work of a computer?
If we do not interact with the computer via a primary programming language
(Assembler or machine language), I believe that it is impossible to find the
principle of work of the computer either from in- or outside of it.
The fundamental function of a computer is to do logical and arithmetical
operations with binary numbers with the help of an electronic device
(register) called 'accumulator'.
If we are in a text editor, for instance, and we press a key corresponding to
a letter, that letter will show on the screen. For the unaware, it is
difficult to imagine that by pressing a key associated with a letter, a
register-accumulator will make hundreds or thousands of logical and
arithmetical operations on binary numbers, only to have that letter shown on
the screen.
This example wants to illustrate that, based on the external analysis of what
is happening, it is impossible to figure out the principle of work of a
ridiculously simple device as a computer (ridiculously simple compared to the
brain of a dog, e.g.)
The method used in positive sciences is not the analysis of primary data. The
method of analysis works on extremely simple systems, which can be perceived
on image models as well. The method used in positive sciences is to guess a
symbolic model, based more or less on the interaction with external reality,
and to verify the model.
It results that the method of understanding the brain based on the analysis of
primary data is at least inefficient.
A fundamental problem of knowledge is that primary facts can be understood
only if a model to integrate them already exists. Without a model, we are
forced to build one on the spot. Thus, each fact of the external reality could
be understood based on a local model. The correlation between facts, each
understood in its local model is impossible. This is why a method of guessing
a fundamental model was imposed. Based on the single fundamental model the
facts are interpreted and reinterpreted. Such a method allows the correlation
of the primary facts.
If the model does not make good predictions, it will be modified and the
process restarted from scratch, until we find the model in which all the
primary facts can be understood. The process stops when the predictions are
true with an acceptable rate. In that phase we can talk about knowledge.
As a comment, we need to say that the analytical method is based on short-
range models (can be affected by schizophrenia and XSPC), while the synthetic
method is based on long-range models which allow not only correlation between
facts, but also a cross-check between the local models.
The need for a single fundamental model comes from the fact that any used word
needs to have a unique definition. This is true only in case of the existence
of a single fundamental model.
Example: what would happen if in common language everybody used different
definitions for the words used? The communication would not exist, everyone
talking his/her own language.
The conclusion is that any positive science is based on a single fundamental
model, stated from the very start. This symbolic model can be based on primary
facts, results of the interaction with external reality or on theoretical
principles (e.g. the principle of inertia in Newton's Mechanics cannot be
visibly connected by facts seen in the external reality).
However, as in the external reality there are a huge number of facts difficult
to correlate, the method to find the fundamental symbolic model is guessing.
Once the model built, this will order in a univoque manner all the primary
facts. Moreover, it will make predictions that will lead to new discoveries or
confirmations of itself.
As we have shown in the general theory, we reflect sections of the external
reality in models. The models make predictions. If the predictions are good,
we will use the model a next time too, as it proved to be useful.
Now we have the normal answer to a fundamental question asked for long time:
'why do the laws of nature exist?' or 'Why the world has an order?'
As it results from MDT, we reflect the external reality based on symbolic
models. These symbolic models need to be logical in order to be stable. If a
symbolic model associated to external reality will not reflect it correctly,
we will build a new model.
Example: The external reality can change due to the movement of some objects.
Then we will build a symbolic model containing the term 'velocity'. This
symbolic model will make good predictions provided the objects move at a
constant velocity. If the velocity is not constant, the model will not reflect
correctly the external reality. Then we will build a new symbolic model,
introducing a new element called 'acceleration'. This model will make correct
prediction for the objects that move at variable velocities as well. Thus, by
building of adequate models, the external reality is reflected by stable
models.
It is very easy to confuse the external reality with its 'image' generated by
a stable symbolic model.
As we know, we have no direct acces to external reality. We perceive it based
on some associated models. Thus, as a conclusion, the impresion that nature is
a structure based on stable laws and order comes from the fact that we reflect
the external reality based on logical and stable symbolic models.