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Calculating Space

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This is an old revision of this page, as edited by 98.189.64.110 (talk) at 16:54, 25 November 2019 (Revised 'large amount' to 'great deal'; grammatical and compositional change.'Amounts' are not countable, while the number of writings on digital physics is great but countable. 'A great deal' retains the original spirit of the writing by making as slight a change as possible.). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

An elementary process in Zuse's Calculating Space: Two digital particles A and B form a new digital particle C.[1]

Calculating Space (Template:Lang-de) is Konrad Zuse's 1969 book on digital physics.[2] Zuse proposed that the universe is being computed by some sort of cellular automaton or other discrete computing machinery,[2] challenging the long-held view that some physical laws are continuous by nature. He focused on cellular automata as a possible substrate of the computation, and pointed out (among other things) that the classical notions of entropy and its growth do not make sense in deterministically computed universes.

Bell's theorem is sometimes thought to contradict Zuse's hypothesis, but it is not applicable to deterministic universes, as Bell himself pointed out. Similarly, while Heisenberg's uncertainty principle limits in a fundamental way what an observer can observe, when the observer is himself a part of the universe he is trying to observe, that principle does not rule out Zuse's hypothesis, which views any observer as a part of the hypothesized deterministic process. So far there is no unambiguous physical evidence against the possibility that "everything is just a computation," and a great deal has been written about digital physics since Zuse's book appeared.

See also

References

  1. ^ Rechnender Raum (PDF document), Elektronische Datenverarbeitung, 8: 336–344, 1967.
  2. ^ a b Mainzer, Klaus; Chua, Leon (September 2011). The Universe as Automaton: From Simplicity and Symmetry to Complexity. Springer. p. 6.


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