From the epilogue of the article: The methods we found for solving the first problem don’t appear to help with the second problem, and vice versa. In fact, the

From the epilogue of the article: The methods we found for solving the first problem don’t appear to help with the second problem, and vice versa. In fact, the two isolated solutions appear quite difficult to reconcile. The problem that these two papers leave open is: Can we get one algorithm that satisfies both properties at once? A few paragraphs up: The first property is about recognizing patterns about logical relationships between claims — saying “claim A implies claim B, so my probability on B must be at least my probability on A.” By contrast, the second property is about recognizing frequency patterns between similar claims — saying “I lack the resources to tell whether this claim is true, but 90% of similar claims have been true, so the base rate is 90%” (where part of the problem is figuring out what counts as a “similar claim”). My question would be, is the "similar claim" problem not a relationship between claims and therefore pertinent to the first property and providing a link between the two problems?