“Um… there really aren’t any extremely strong arguments for majoritarianism. That position confuses conclusions with evidence.”
What’s more, it implies that human beliefs are normally distributed. I posit they are not, with extra weight being given to concepts that are exciting/emotional or arousing. We have a built in bias in the direction of things that are evolutionarily important (ie—babies, scarey stuff).
“I’m trying to comprehend how this is a dilemma… Science supposedly teaches that for any two theories that explain the same data, the simplest one is correct. Bayes can’t talk about explaining data without invoking the science that collected the data… Can he?”
That’s Occam’s razor, not Science. The scientific method >is taken to suggest< that an untestable theory is of no use. This isn’t the case, since every theory starts out untestable, until someone devises a test for it. What’s more, Occam’s razor isn’t some unmutable natural law: it’s just a probability—the simplest explanation is >usually< the right one, and so why not start there and move up the ladder of complexity as required: that way, you can cover the most likely (all other aspects being equal) explanations with the minimum amount of work.
I call false dichotomy on this Bayes vs Science lark. It’s perfectly reasonable to work with untestable theories, even ones that remain implicitly untestable, and even ones that go against observed phenomenon, as long as one recognizes that, somewhere, there is hole in the grand equation. “Spooky action at a distance”, anyone?
“Um… there really aren’t any extremely strong arguments for majoritarianism. That position confuses conclusions with evidence.”
What’s more, it implies that human beliefs are normally distributed. I posit they are not, with extra weight being given to concepts that are exciting/emotional or arousing. We have a built in bias in the direction of things that are evolutionarily important (ie—babies, scarey stuff).
“I’m trying to comprehend how this is a dilemma… Science supposedly teaches that for any two theories that explain the same data, the simplest one is correct. Bayes can’t talk about explaining data without invoking the science that collected the data… Can he?”
That’s Occam’s razor, not Science. The scientific method >is taken to suggest< that an untestable theory is of no use. This isn’t the case, since every theory starts out untestable, until someone devises a test for it. What’s more, Occam’s razor isn’t some unmutable natural law: it’s just a probability—the simplest explanation is >usually< the right one, and so why not start there and move up the ladder of complexity as required: that way, you can cover the most likely (all other aspects being equal) explanations with the minimum amount of work.
I call false dichotomy on this Bayes vs Science lark. It’s perfectly reasonable to work with untestable theories, even ones that remain implicitly untestable, and even ones that go against observed phenomenon, as long as one recognizes that, somewhere, there is hole in the grand equation. “Spooky action at a distance”, anyone?