One simple heuristic formula for approximate problem difficulty I heard is this:
D = log(T)*N
D is difficulty, T is length of time the problem stayed open after being posed, N is the average number of people working on it.
Rationalle:
Since we expect P != NP, finding proofs is an exponential search problem. That means in time T we can only cover problems that have about log(T) shortest proof length. Assuming linear scaling with adding more processing, we have a factor of N (in reality, scaling is sublinear, but this is a heuristic..).
The point is that just because a field has open problems, doesn't mean those problems are necessarily very hard. It could be that either they haven't been open very long, or not enough people care.
Since we expect P != NP, finding proofs is an exponential search problem.
P != NP does not imply that one can't do better than an exponential search for finding proofs. For instance an O(n^log(n)) algorithm for finding proofs would not contradict P != NP.
About a month ago I accidentally found out that the LW idea of quining cooperation is already studied in academia:
1) Moshe Tennenholtz's 2004 paper Program Equilibrium describes the idea of programs cooperating in the Prisoner's Dilemma by inspecting each other's source code.
2) Lance Fortnow's 2009 paper Program Equilibria and Discounted Computation Time describes an analogue of Benja Fallenstein's idea for implementing correlated play, among other things.
3) Peters and Szentes's 2012 paper Definable and Contractible Contracts studies quining cooperation over a wider class of definable (not just computable) functions.
As far as I know, academia still hasn't discovered Loebian cooperation or the subsequent ideas about formal models of UDT, but I might easily be wrong about that. In any case, the episode has given me a mini-crisis of faith, and a new appreciation of academia. That was a big part of the motivation for my previous post.