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    Home/Original/inverse
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    Inverse View

    It is not the case that Parameter dependence is a necessary and sufficient condition for controllable probabilistic dependence in models where measurement outcome probabilities depend only on the pair's state and apparatus settings

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

    Reasons For

    2 perspectives
    Reason for 1 of 2
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    • 1.Outcome dependence can generate controllable probabilistic dependence even without parameter dependence, as Jarrett's original 1984 decomposition shows.
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    • 2.In models where outcome probabilities depend on distant outcomes (not just settings), signaling constraints are violated by outcome dependence, not parameter dependence alone.
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    • 3.Therefore parameter dependence is not necessary for controllable dependence, since outcome-dependent models can permit statistical correlations exploitable for signaling.
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    Reason for 2 of 2
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    • 1.Shimony's distinction between parameter and outcome dependence presupposes a sharp separation that contextual hidden variable models, like those of Bohr, systematically blur.
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    • 2.In contextual models, apparatus settings and outcomes are not cleanly separable variables, making the probability function's domain ill-defined for the parameter/outcome distinction.
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    • 3.The sufficiency claim therefore fails because the model class it targets—where probabilities depend only on λ and settings—excludes precisely those contextual models where the condition's behavior is most contested.
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    Reasons Against

    1 perspective
    Reason against
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    • 1.In such models, the probability of a distant measurement outcome depends only on the pair's state λ and the settings of the measurement apparatuses
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    • 2.Parameter dependence is defined as the dependence of the probability of the distant measurement outcome on the setting of the nearby measurement apparatus
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