L. Jonathan Cohen's 'inductive probability' framework shows that Pascalian probability cannot capture the evidential completeness dimension, which tracks how many relevant considerations have been investigated.

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Evidential completeness(as the dimension that Pascalian probability allegedly fails to capture): The idea that how well-supported a conclusion is depends not just on what evidence you have, but on whether you've actually looked for and considered all the relevant information available.
Inductive probability(as Cohen's alternative framework to traditional probability): A way of thinking about probability that focuses on how much evidence supports a conclusion, based on what we've actually observed and investigated, rather than just abstract mathematical rules.
L. Jonathan Cohen(as the originator of the inductive probability framework): A British philosopher who specialized in logic and probability theory, and developed ideas about how we actually reason with evidence in real life rather than just using pure mathematics.
Pascalian probability(as the conventional probability theory Cohen critiques)

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