Simon Marlow replied: http://www.haskell.org/pipermail/cvs-ghc/2009-November/050946.html ============= Also I received privately a very much appreciated and stimulating reply about the convolution of the competition for the shared resources in the external state machine. My quick (not deeply thought out) response is that the algorithm's target is not fixed (not a hard-coded upper bound), but rather the algorithm should be a gradient search for the local minima (which will always be a moving target, but that is okay). The problem is when it gets stuck in some local minima that is far from the global minima (e.g. some cycling resonance with the external competition), so to the degree that is a common, we occasionally we need to test some random position in the solution space (simulated annealing). That is analogous to how autonomous actors optimize problems in real-world, e.g. humans in their interaction with their shared reality with other actors, they bite off some goal and go for it, then they scrap the goal if it is useless local minima. In short, we trade speed of convergence for determinism of convergence, i.e. in annealing (very slow cooling), then ice has fewer cracks. (I learned this stuff 20+ years ago from reading about one model of how neural networks can learn and converge). So yes I agree that the nondeterminism can still creep back in as aliasing error in the algorithm's sampling for a global solution. But according to the fundamental theorems of the universe ( http://www.haskell.org/pipermail/haskell-cafe/2009-November/068432.html ), nothing is every finally optimized (except that "faith exists") to a globally closed solution (even if you have closed solution mathematical model which says it is, i.e. space-time did not finalize the later inconsistency of quantum theory), i.e. there is no global solution in life only a relative one (there is always some more complex external state, i.e. the universe trends to maximum disorder). But the practical motivation is that to the degree the gradient search is reasonably more deterministic in common usage (see my "KEY POINT" in response to Simon Marlow as for why I think that is likely achievable), then the space nondeterminism should in theory scale more continuously more often. Regarding the private point about overbooking, since the CPU is much faster than the hard disk, it should be true that even if the paging load is 100% of CPU allocation, then the CPU is not overbooked. And if the paging load is so overbooked due to competiting actors, you might as well just turn off your machine :). And I agree with the private point that the gradient search algorithm should incorporate a gradient reduction in it's velocity towards the target as it approaches the target minima, i.e. that systems do not perform reliabily if resource allocation strategies are edge discontinuous. I disagree that the programmer/user needs to be burdened with this, as is the case now. I am arguing it should be automatic, unless the programmer wants to do space profile optimization work. Maybe I am very wrong. It is research after all.