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We define a prediction as
a statement that a certain process, referred to as a test
comes to a successful end, i.e.
to a certain, specified in advance, stage, after which
we simply do not care what happens to the process.
The prediction that a test T is successful will be denoted as T!.
We define formally a generalized model as anything
that produces one or more predictions.
When we speak of producing predictions, we have in mind, of course,
some objects that represent predictions, e.g. texts in a certain
language which enable us to reproduce the process that the prediction
is about. The objects representing processes are referred to
as their objectifications (see).
Formally, we can fit our general concept of prediction into the frame
of the modeling scheme, if we even further expand the range of possible
actions a, namely, allow for a being an arbitrary process which may
include both actions of the subject of the model,
and any other actions and processes.
Let the brain of the subject be always found in one of only two states,
let them have the names True and False. The representation
function M_a(w) will result in True if w is the end state of
the process a which succeeded, and False otherwise.
The modeling function M(r) will be universal and very simple:
it immediately produces the object True. Now the model we built makes
exactly one prediction : that the process a ends in success.
