I initially interpreted “abstract entropy” as meaning statistical entropy as opposed to thermodynamic or stat-mech or information-theoretic entropy. I think very few people encounter the phrase “algorithmic entropy” enough for it to be salient to them, so most confusion about entropy in different domains is about statistical entropy in physics and info theory. (Maybe this is different for LW readers!)
This was reinforced by the introduction because I took the mentions of file compression and assigning binary strings to states to be about (Shannon-style) coding theory, which uses statistical entropy heavily to talk about these same things and is a much bigger part of most CS textbooks/courses. (It uses phrases like “length of a codeword”, “expected length of a code [under some distribution]”, etc. and then has lots of theorems about statistical entropy being related to expected length of an optimal code.)
After getting that pattern going, I had enough momentum to see “Solomonoff”, think “sure, it’s a probability distribution, presumably he’s going to do something statistical-entropy-like with it”, and completely missed the statements that you were going to be interpreting K complexity itself as a kind of entropy. I also missed the statement about random variables not being necessary.
I suspect this would also happen to many other people who have encountered stat mech and/or information theory, and maybe even K complexity but not the phrase “algorithmic entropy”, but I could be wrong.
A disclaimer is probably not actually necessary, though, on reflection; I care a lot more about the “minimum average” qualifiers both being included in statistical-entropy contexts. I don’t know exactly how to unify this with “algorithmic entropy” but I’ll wait and see what you do :)
I initially interpreted “abstract entropy” as meaning statistical entropy as opposed to thermodynamic or stat-mech or information-theoretic entropy. I think very few people encounter the phrase “algorithmic entropy” enough for it to be salient to them, so most confusion about entropy in different domains is about statistical entropy in physics and info theory. (Maybe this is different for LW readers!)
This was reinforced by the introduction because I took the mentions of file compression and assigning binary strings to states to be about (Shannon-style) coding theory, which uses statistical entropy heavily to talk about these same things and is a much bigger part of most CS textbooks/courses. (It uses phrases like “length of a codeword”, “expected length of a code [under some distribution]”, etc. and then has lots of theorems about statistical entropy being related to expected length of an optimal code.)
After getting that pattern going, I had enough momentum to see “Solomonoff”, think “sure, it’s a probability distribution, presumably he’s going to do something statistical-entropy-like with it”, and completely missed the statements that you were going to be interpreting K complexity itself as a kind of entropy. I also missed the statement about random variables not being necessary.
I suspect this would also happen to many other people who have encountered stat mech and/or information theory, and maybe even K complexity but not the phrase “algorithmic entropy”, but I could be wrong.
A disclaimer is probably not actually necessary, though, on reflection; I care a lot more about the “minimum average” qualifiers both being included in statistical-entropy contexts. I don’t know exactly how to unify this with “algorithmic entropy” but I’ll wait and see what you do :)