Principia Cybernetica Web


[Node to be completed]

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.

Copyright© 1991 Principia Cybernetica - Referencing this page

V. Turchin,

Sep 1991


Metasystem Transition Theory



Prev. Next


Add comment...