my goodness, do you have some fields medals to show for your confidence? and would you also downvote hume into the ground?
You discussed the riddle of induction, no?
as for the few substantial points:
alicorn:
the model is ontologically more parsimonous because it posits all standard entities minus causal relations.
I’d think we find ourselves in a seemingly ordered universe (or a patch) thereof because the cardinality of ordered regions is the same as that of the chaotic ones in this sort of infinite universe.
and i really don’t see why random events are more mysterious than deterministic ones. care to explain?
The theory is practically useless
Even if this were so, is it an argument against its truth?
the model is ontologically more parsimonous [sic] because it posits all standard entities minus causal relations.
That isn’t parsimony, that’s ontological promiscuity of the worst sort.
Say we have two universes. One of them looks like what you describe: infinity sub-universes going about their random business in space. The other has no further division into sub-universes, and it has tight, deterministic causality.
You can find out anything you might care to learn about universe 2 by fully describing one instant and a list of the laws of nature. Then, you can (without any further premises) extrapolate forwards and backwards in time indefinitely and get the right answer every time, at least if you are a superintelligence.
In order to know as much about the first universe, though, you can’t do that. The instant you pick to describe is more complicated than the one of the second universe to begin with, but let’s ignore that—if we ignore that, then at first it looks like universe 1 wins, because you just describe the instant and no rules. But what if you want to know what happens at instant+1? You can’t extrapolate: you have to do more positing. In fact, you have to individually posit every object, event, and area of empty space in universe 2, because they don’t follow from anything and have to stand alone, random pillars of acausality in a vast sea of acausality.
The list of things you need to know about universe 1 in order to be able to derive everything there is to know about universe 1 just is the list of everything there is to know about universe 1. The list of things you need to know about universe 2 in order to be able to derive everything there is to know about universe 2 is much more parsimonious. Universe 1, says, “here is a WHOLE LOT OF STUFF.” Universe 2 says, “here is some stuff, and the mechanisms by which it will change.”
As for mystery: you must have an atypical understanding of the word. Mystery is what happens when something cannot be explained. There is no explanation for why everything might happen at random: there is only one step between the question and the mystery. (“Why is this stick of gum yellow?” “It is yellow at random.” “Why is the color of the gum random?” “It is a mystery.”) A causal model of the universe does not eliminate mystery, but it postpones it and makes it less conspicuous. (“Why is this stick of gum yellow?” “It is yellow because it refracts light at a wavelength that the human eye sends to the brain, which interprets it as a color which is called yellow in English because the word is derived from Old English geolu.” Then you can follow the chain backwards pretty damn far in any number of directions: why does the substance reflect that wavelength? Why did light fall on the stick of gum? Why does the human organism interpret that wavelength as a color and not a temperature or a sound? Why is yellow its own color in English and not in some other languages? What’s the etymology of geolu? The mystery takes a long time to turn up. There is less of it.)
the cardinality of ordered regions is the same as that of the chaotic ones in this sort of infinite universe
Accepting this statement for the moment, I should point out that cardinality is probably not the best measure of the size of the sets for this purpose. For a simpler case, the natural numbers, see natural density.
The model is less complex, possibly, but this is not the sole criterion on which it should be judged. Occam’s Razor discourages complexity, but it encourages precision, and a Humeiform universe avoids the first only at the cost of utterly failing the second.
To use a metaphor: you have to pay for everything you put in your model, but your return on that investment is related to how much accuracy the model gives. More expensive (complex) models can still win if they give a bigger enough payoff (sufficiently complete prediction of the observations).
Guys,
my goodness, do you have some fields medals to show for your confidence? and would you also downvote hume into the ground? You discussed the riddle of induction, no?
as for the few substantial points:
alicorn:
the model is ontologically more parsimonous because it posits all standard entities minus causal relations.
I’d think we find ourselves in a seemingly ordered universe (or a patch) thereof because the cardinality of ordered regions is the same as that of the chaotic ones in this sort of infinite universe.
and i really don’t see why random events are more mysterious than deterministic ones. care to explain?
Even if this were so, is it an argument against its truth?
That isn’t parsimony, that’s ontological promiscuity of the worst sort.
Say we have two universes. One of them looks like what you describe: infinity sub-universes going about their random business in space. The other has no further division into sub-universes, and it has tight, deterministic causality.
You can find out anything you might care to learn about universe 2 by fully describing one instant and a list of the laws of nature. Then, you can (without any further premises) extrapolate forwards and backwards in time indefinitely and get the right answer every time, at least if you are a superintelligence.
In order to know as much about the first universe, though, you can’t do that. The instant you pick to describe is more complicated than the one of the second universe to begin with, but let’s ignore that—if we ignore that, then at first it looks like universe 1 wins, because you just describe the instant and no rules. But what if you want to know what happens at instant+1? You can’t extrapolate: you have to do more positing. In fact, you have to individually posit every object, event, and area of empty space in universe 2, because they don’t follow from anything and have to stand alone, random pillars of acausality in a vast sea of acausality.
The list of things you need to know about universe 1 in order to be able to derive everything there is to know about universe 1 just is the list of everything there is to know about universe 1. The list of things you need to know about universe 2 in order to be able to derive everything there is to know about universe 2 is much more parsimonious. Universe 1, says, “here is a WHOLE LOT OF STUFF.” Universe 2 says, “here is some stuff, and the mechanisms by which it will change.”
As for mystery: you must have an atypical understanding of the word. Mystery is what happens when something cannot be explained. There is no explanation for why everything might happen at random: there is only one step between the question and the mystery. (“Why is this stick of gum yellow?” “It is yellow at random.” “Why is the color of the gum random?” “It is a mystery.”) A causal model of the universe does not eliminate mystery, but it postpones it and makes it less conspicuous. (“Why is this stick of gum yellow?” “It is yellow because it refracts light at a wavelength that the human eye sends to the brain, which interprets it as a color which is called yellow in English because the word is derived from Old English geolu.” Then you can follow the chain backwards pretty damn far in any number of directions: why does the substance reflect that wavelength? Why did light fall on the stick of gum? Why does the human organism interpret that wavelength as a color and not a temperature or a sound? Why is yellow its own color in English and not in some other languages? What’s the etymology of geolu? The mystery takes a long time to turn up. There is less of it.)
Accepting this statement for the moment, I should point out that cardinality is probably not the best measure of the size of the sets for this purpose. For a simpler case, the natural numbers, see natural density.
The model is less complex, possibly, but this is not the sole criterion on which it should be judged. Occam’s Razor discourages complexity, but it encourages precision, and a Humeiform universe avoids the first only at the cost of utterly failing the second.
To use a metaphor: you have to pay for everything you put in your model, but your return on that investment is related to how much accuracy the model gives. More expensive (complex) models can still win if they give a bigger enough payoff (sufficiently complete prediction of the observations).