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November 05, 2009 08:47 PM
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By definition, an axiom is an assumption which can neither be proven nor disproven, and which one makes as the basis of an argument or a construction. No computer program, or person, can thus determine if an axiom is true or false.
For example, Euclidean geometry has a set of axioms (see e.g. http://www.bymath.com/studyguide/geo/sec/geo5.htm ). These axioms can neither be proved nor disproved. If one was to define a space in which one or more of these axioms could be demonstrated to be false, that space would not follow Euclidean geometry. However, that would not mean Euclidean geometry could not be true in a different space.
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How could a computer program prove true or false an axiom?
Explain how a computer algorithm would determine, if an axiom is true of false.
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| November 05, 2009 09:34 PM |
For example, Euclidean geometry has a set of axioms (see e.g. http://www.bymath.com/studyguide/geo/sec/geo5.htm ). These axioms can neither be proved nor disproved. If one was to define a space in which one or more of these axioms could be demonstrated to be false, that space would not follow Euclidean geometry. However, that would not mean Euclidean geometry could not be true in a different space.
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• What is the purpose of identifying the axiom system?
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I listening too the lecture by Stephen Wolfram at MIT about cellular automata and towards the end of his lecture, Wolfram started talking about a computer algorithm that could navigate an axiom node and determine true or false assumptions. The axiom network seemed to be very deterministic.
From what I read there, he's not claiming to be able to disprove axioms, merely to identify which axiom systems support a particular mathematics (in his blog the example he uses is that the 50,000th axiom system in his enumeration proved to be Boolean logic).
"One might think this was crazy–like searching for our universe in the space of possible universes."
Is Wolfram connecting axiom systems which relate and calling the network, a searchable binary system?
1. There is complexity in nature. Perhaps, simple algorithms can create a new form of science primitives for understanding nature, as miniature universes of complexity
2. Mathematicians and scientist have used Math to explain nature. For discrete systems math seems to accurately describe nature (the beauty of partial differential equations). However, partial differential equations can be come extremely complex to calculate and discover. Perhaps, it would be better to find a simple algorithm that simulates the behavior of the system.
3. Cellular automa programs have been used to describe complex systems.
4. Other simple programs like binary search, bifurication, fluid dynamics, and Turing machines create complex patterns. Wolfram ran simple programs and observed how they behaved. For rule 110, Wolfram discovered complex random behavior which statistically proved to be random. The simple program had created complicated behavior.
5. Computer languages were designed to help people create primitives to describe what they wanted. Perhaps, there are primitives that describe nature. Language would reflect the rules for describing the natural constructs.