Probabilistic models can express whether many low-probabi... — Carmelics

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Probabilistic models can express whether many low-probability events combined can outweigh even the highest-probability worlds, while plausibilistic models cannot

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Reasons For

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1.Probabilistic models support aggregation of probabilities
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2.Aggregated probabilities play a key role in calculations of expected utility
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3.Plausibility semantics cannot express aggregated probability comparisons
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Reasons Against

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Reason against 1 of 2

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1.Plausibilistic models using Grove sphere systems can represent infinite orderings that track cumulative evidential weight across possibility classes.
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2.Spohn's ranking theory assigns negative ranks that aggregate across worlds, enabling comparative assessments structurally analogous to probabilistic summation.
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3.The expressive gap between plausibilistic and probabilistic models is one of representation format, not fundamental combinatorial capacity.
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