Maybe it’s not a law of straight lines, its a law of exponentially diminishing returns, and maybe it applies to any scientific endeavour. What we are doing in science is developing mathematical representations of reality. The reality doesn’t change, but our representations of it become ever more accurate, in an asymptotic fashion. What about Physics? In 2500 years we go from naive folk physics to Democritus, to Ptolemy, Galileo and Copernicus, to Newton, then Clerk Maxwell, Einstein, Schrodinger, Feynman and then the Standard Model, at every stage getting more and more accurate and complete. We now have a physics that is (as Sean Carroll points out) to all intents and purposes perfectly accurate in all it’s predictions for ALL physical phenomena happening at Terrestrial and Solar neighborhood scale. We can even measure accurately the collisions between black holes over a distance of a billion light years. Order of magnitude improvements in the scope and accuracy of a such a physics might just be logically impossible. So enormous effort is invested by some very bright people to make relatively small improvements in theory. So this is a long hanging fruit argument, but predicated on the assumption that theories in a mature science will progressively approach an asymptote of possible accuracy and completeness.
We now have a physics that is (as Sean Carroll points out) to all intents and purposes perfectly accurate in all it’s predictions for ALL physical phenomena happening at Terrestrial and Solar neighborhood scale.
That’s not really true. Predicting the weather of tomorrow is a problem of physical prediction but we don’t have perfectly accurate predictions.
It’s interesting to have a physics community that sees itself so narrow that it wouldn’t consider that physical phenomena.
Interesting proposition. I always thought that predicting stuff like weather was more of a question of incomplete data/ too many factors (is precise nanontech like this?). I mean we might get a results by a compromise between a ton of measurement devices (internet of things style) and some useful statistical approximations, but the computational need to accuracy ratio seems a bit much.
[sidenote:]Also, the post made me think about having little to no idea about career choice, and gave me the idea of working on this very question, on a higher level (mathemathics of human resources, I guess)… fucking overdue coding assignments, though. And my CS program in general.
The useful questions of physics that stay unsolved seem to be about complex systems as we live in a world most of the interesting phenomena aren’t due to individual particals but due to complex systems.
Hmm. I want to look up how the guys did thermodynamics and such. I doubt I could think of anything new, but using these tools in all sorts of different scenarios might be of use.
Maybe it’s not a law of straight lines, its a law of exponentially diminishing returns, and maybe it applies to any scientific endeavour. What we are doing in science is developing mathematical representations of reality. The reality doesn’t change, but our representations of it become ever more accurate, in an asymptotic fashion. What about Physics? In 2500 years we go from naive folk physics to Democritus, to Ptolemy, Galileo and Copernicus, to Newton, then Clerk Maxwell, Einstein, Schrodinger, Feynman and then the Standard Model, at every stage getting more and more accurate and complete. We now have a physics that is (as Sean Carroll points out) to all intents and purposes perfectly accurate in all it’s predictions for ALL physical phenomena happening at Terrestrial and Solar neighborhood scale. We can even measure accurately the collisions between black holes over a distance of a billion light years. Order of magnitude improvements in the scope and accuracy of a such a physics might just be logically impossible. So enormous effort is invested by some very bright people to make relatively small improvements in theory. So this is a long hanging fruit argument, but predicated on the assumption that theories in a mature science will progressively approach an asymptote of possible accuracy and completeness.
That’s not really true. Predicting the weather of tomorrow is a problem of physical prediction but we don’t have perfectly accurate predictions.
It’s interesting to have a physics community that sees itself so narrow that it wouldn’t consider that physical phenomena.
Interesting proposition. I always thought that predicting stuff like weather was more of a question of incomplete data/ too many factors (is precise nanontech like this?). I mean we might get a results by a compromise between a ton of measurement devices (internet of things style) and some useful statistical approximations, but the computational need to accuracy ratio seems a bit much.
[sidenote:]Also, the post made me think about having little to no idea about career choice, and gave me the idea of working on this very question, on a higher level (mathemathics of human resources, I guess)… fucking overdue coding assignments, though. And my CS program in general.
The useful questions of physics that stay unsolved seem to be about complex systems as we live in a world most of the interesting phenomena aren’t due to individual particals but due to complex systems.
Hmm. I want to look up how the guys did thermodynamics and such. I doubt I could think of anything new, but using these tools in all sorts of different scenarios might be of use.