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Inverse View
It is not the case that Slot and van Emde Boas (1988) themselves noted that space measures are not robustly invariant across models, undermining the thesis's universality.
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Reasons For
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Reason for
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1.
Space measure variations across models may reflect measurement differences, not failure of underlying universality in algorithmic structure.
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2.
Most practical algorithms maintain relative ordering of space efficiency across models even if absolute measures differ numerically.
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3.
Non-robustness in one dimension doesn't falsify universality claims; time complexity shows similar variation yet remains theoretically robust.
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Reasons Against
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Reason against
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1.
Space complexity varies significantly between RAM, Turing machines, and pointer machines, showing model-dependence is empirically demonstrable.
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2.
If a thesis claims universality but key measures shift across models, the thesis loses explanatory power for real computational systems.
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3.
Slot and van Emde Boas's observation directly undermines claims that space efficiency is a model-invariant property of algorithms.
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