Yes, I took a look at your discussion with MichaelStJules. There is a difference in reliability between:
probability that you assign to the Mugger’s threat
probability that the Mugger or a third party assigns to the Mugger’s threat
Although I’m not a fan of subjective probabilities, that could be because I don’t make a lot of wagers.
There are other ways to qualify or quantify differences in expectation of perceived outcomes before they happen. One way is by degree or quality of match of a prototypical situation to the current context. A prototypical situation has one outcome. The current context could allow multiple outcomes, each matching a different prototypical situation. How do I decide which situation is the “best” match?
a fuzzy matching: a percentage quantity showing degree of match between prototype and actual situation. This seems the least intuitive to me. The conflation of multiple types and strengths of evidence (of match) into a single numeric system (for example, that bit of evidence is worth 5%, that is worth 10%) is hard to justify.
a hamming distance: each binary digit is a yes/no answer to a question. The questions could be partitioned, with the partitions ranked, and then hamming distances calculated for each ranked partition, with answers about the situation in question, and questions about identifying a prototypical situation.
a decision tree: each situation could be checked for specific values of attributes of the actual context, yielding a final “matches prototypical situation X” or “doesn’t match prototypical situation X” along different paths of the tree. The decision tree is most intuitive to me, and does not involve any sums.
In this case, the context is one where you decide whether to give any money to the mugger, and the prototypical context is a payment for services or a bribe. If it were me, the fact that the mugger is a mugger on the street yields the belief “don’t give” because, even if I gave them the money, they’d not do whatever it is that they promise anyway. That information would appear in a decision tree, somewhere near the top, as “person asking for money is a criminal?(Y/N)”
Yes, I took a look at your discussion with MichaelStJules. There is a difference in reliability between:
probability that you assign to the Mugger’s threat
probability that the Mugger or a third party assigns to the Mugger’s threat
Although I’m not a fan of subjective probabilities, that could be because I don’t make a lot of wagers.
There are other ways to qualify or quantify differences in expectation of perceived outcomes before they happen. One way is by degree or quality of match of a prototypical situation to the current context. A prototypical situation has one outcome. The current context could allow multiple outcomes, each matching a different prototypical situation. How do I decide which situation is the “best” match?
a fuzzy matching: a percentage quantity showing degree of match between prototype and actual situation. This seems the least intuitive to me. The conflation of multiple types and strengths of evidence (of match) into a single numeric system (for example, that bit of evidence is worth 5%, that is worth 10%) is hard to justify.
a hamming distance: each binary digit is a yes/no answer to a question. The questions could be partitioned, with the partitions ranked, and then hamming distances calculated for each ranked partition, with answers about the situation in question, and questions about identifying a prototypical situation.
a decision tree: each situation could be checked for specific values of attributes of the actual context, yielding a final “matches prototypical situation X” or “doesn’t match prototypical situation X” along different paths of the tree. The decision tree is most intuitive to me, and does not involve any sums.
In this case, the context is one where you decide whether to give any money to the mugger, and the prototypical context is a payment for services or a bribe. If it were me, the fact that the mugger is a mugger on the street yields the belief “don’t give” because, even if I gave them the money, they’d not do whatever it is that they promise anyway. That information would appear in a decision tree, somewhere near the top, as “person asking for money is a criminal?(Y/N)”