I would be really interested to know what conclusion you have made about inferential distance.
Jennifer is suggesting that these ideas could be used to quantify inferential distances. A first attempt might be to say that a Speaker and a Listener are separated by a large inferential distance when the Speaker has a much larger value for P(U|T) than the Listener does.
There seems to me to be something important left out, though. I take inferential distances to be about differences in the plausibility of a conclusion to different people. Even if you understand my claim perfectly (ie, you've mapped my U to the proper T) you might still consider T to be almost certainly wrong, while I consider T to be an inevitable conclusion of self-evident premises, even if it takes a long chain of inferences to get to the conclusion from the premises.
Speaking from my personal experience, when I as a listener had problems accepting a conclusion which was considered natural and perhaps obvious by the speaker, it was rarely because I misinterpreted the meaning (now I am apeaking about conclusions which I have accepted as obvious later, so that I can judge whether I understood what has been said earlier). The reason was rather that I lacked some background knowledge or thinking habits which caused my P(T) being low, not P(U|T).
One of the shiniest ideas I picked up from LW is inferential distance. I say "shiny" because the term, so far as I'm aware, has no clear mathematical or pragmatic definition, no substantive use in peer reviewed science, but was novel to me and appeared to make a lot of stuff about the world suddenly make sense. In my head it is marked as "super neat... but possibly a convenient falsehood". I ran across something yesterday that struck me a beautifully succinct and helpful towards resolving the epistemic status of the concept of "inferential distance".
While surfing the language log archives I ran across a mailbox response to correspondence about comparative communication efficiency. The author, Mark Liberman, was interested in calculating the amount of information in text and was surprised to find that something about the texts, or the subjects, or his calculation lead to estimating different amounts of information in different translations of the same text (with English requiring 20%-40% more bits than Chinese to say the things in his example text).
Mr. Liberman was helped by Bob Moore who, among other things, noted:
Application to inferential distance is left as an exercise for the reader :-)