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    Carmelics

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    Made withinDC&Austin
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    Home/Original/inverse
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    Inverse View

    It is not the case that 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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    Reasons For

    1 perspective
    Reason for
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    • 1.Mathematical frameworks (conditional probability, measure theory) already handle context-dependence through parameterized probability spaces without requiring foundational restructuring.
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    • 2.The claim conflates interpretive metaphysics with mathematical formalism; non-separability is a feature of *physical systems*, not proof that probability's domain itself is ill-defined.
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    • 3.Operational frameworks successfully define probabilities P(outcome|settings,initial-state) with perfectly well-defined domains, showing the separation problem is solvable rather than fundamental.
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    Reasons Against

    1 perspective
    Reason against
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    • 1.Bell test experiments show measurement settings causally influence outcomes in ways classical probability theory cannot cleanly partition into independent variables.
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    • 2.Contextual interpretations (like Kochen-Specker) demonstrate that assigning pre-existing values requires outcome values to depend on the full measurement context, not just apparatus settings alone.
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    • 3.Standard probability formalism assumes domain elements are defined independently; when context determines both what questions are meaningful and their answers, this foundational assumption fails.
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