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    Probabilistic models support aggregation of probabilities — Carmelics
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    Supports→Probabilistic models can express whether many low-probability events combined can outweigh even the highest-probability worlds, while plausibilistic models cannot

    Probabilistic models support aggregation of probabilities

    Modality & PossibilityTruth & Knowledge
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    Modality & PossibilityTruth & Knowledge

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    Aggregated probabilities play a key role in calculations of expected utilityPlausibility semantics cannot express aggregated probability comparisonsProbabilistic models can express whether many low-probability events combined ca...

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    Probabilistic models can express whether many low-probability events c...74%Prior probabilities together with likelihood values jointly constrain ...72%Mixture models allow an empirical modeler to apply a range of decision...71%Mixture models allow deployment of a wide range of decision functions ...71%

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    Both probabilistic and plausibilistic perspectives can express that some alternative is more likely than another. However, there are also conceptual differences between the two frameworks. Probabilistic models can aggregate, allowing their logic to express, for instance, whether many low-probability events combined can outweigh even the highest-probability worlds. Aggregated probabilities play a key role, for instance, in calculations of expected utility. Yet no such thing can be expressed in pl

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