Modelling Systems

Now that we have the link between systems theory and information theory explicitly on the table, there are a couple of other interesting topics we can introduce. For example, the famous Turing machine can both:

  1. Be viewed as a system.
  2. Model (aka simulate) any other possible system.

And it is on the combination of these points that I want to focus. First, I shall define the size of a system as the total number of bits that are needed to represent the totality of its information. This can of course change as the entropy of the system changes, so the size is always specific to a particular state of a system.

With this definition in hand (and considering as an example the Turing machine above), we can say that a system can be perfectly simulated by any other system whose maximum size is at least as large as the maximum size of the system being simulated. The Turing machine, given its unlimited memory, has an infinite maximum size and can therefore simulate any system. This leads nicely to the concept of being Turing complete.

(Note that an unlimited memory is not in itself sufficient for Turing completeness. The system’s rules must also be sufficiently complex or else the entropy over time of the system reduces to a constant value.)


One thought on “Modelling Systems

  1. Pingback: The Nature of the Brain | Grand Unified Crazy

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