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    Home/Original/inverse
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    Inverse View

    It is not the case that If the target system's state space is itself finite and discrete, a sufficiently fine-grained computational model can achieve a bijective mapping with physical states.

    ?Set your confidence on the premises below to see your aggregate.

    Reasons For

    1 perspective
    Reason for
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    • 1.Physical discreteness at one scale may emerge from continuous substrates; bijection requires identifying the 'true' fundamental level—which remains undetermined.
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      Think about whether this reason is strong or weak

    • 2.Representing a state and causally instantiating it are distinct; a map doesn't capture whether computation *implements* physics or merely describes it.
      ?

      Think about whether this reason is strong or weak

    • 3.Measurement contexts, observer dependence, and relational properties in quantum mechanics may resist bijection to context-independent computational states.
      ?

      Think about whether this reason is strong or weak

    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.Physical systems with finite discrete state spaces are in principle fully describable by finite information; computation can represent any finite structure.
      ?

      Think about whether this reason is strong or weak

    • 2.Bijective mappings require no causal efficacy—only structural isomorphism between computational and physical states, which finer granularity can approximate.
      ?

      Think about whether this reason is strong or weak

    • 3.Digital physics models successfully map quantum systems (discrete energy levels, spin states) to computational states without loss of predictive power.
      ?

      Think about whether this reason is strong or weak

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