--- In FairfieldLife@yahoogroups.com, grate.swan <no_re...@...> wrote: > > --- In FairfieldLife@yahoogroups.com, "curtisdeltablues" <curtisdeltablues@> wrote: > > > > --- In FairfieldLife@yahoogroups.com, "Richard M" <compost1uk@> wrote: > > > > > > --- In FairfieldLife@yahoogroups.com, "curtisdeltablues" > > > <curtisdeltablues@> wrote: > > > > > > > I don't believe that causality is ever experienced. It is > > > > belief that bridges the cause and the effect in a person's mind. > > > > > > Very elegantly put. > > > > > > But it leads to a wicked thought. Doesn't that make the idea of > > > "causality" and "scientific law" as much a PROJECTION on to the > > > shit that happens as is, say, the idea of deities, sprites, spirits, > > > and other "superstitious" what-not? They're just alternative > > > "language games" for the same thing (stuff-that-happens)? You > > > choose the one that floats your boat best down the shit stream. But the > > > one you choose is not necessarily TRUE, it's just the one that's more > > > or less able to get you from your chosen A to your chosen B? > > > > > > Curtis - I thought you had a more progessive epistemology than that! > > > > Scientific choices are not as random as that. Humans have been at it long enough to no longer need to use characters from literature as starting points for theories. This shift is historically called the "enlightenment" which makes Maharishi's misuse of his "Age of Enlightenment" which proposes going back to the pre-reason model, all the more ironically absurd. > > > > You fill in the gaps as best as you can in the scientific method. You give more or less weight to different descriptions as you discover if it applies to more areas that strengthen the overall theory. Then you test the shit out of all the falsifiable theories you can conjure up. Occasionally very good evidence that cannot be denied comes along and blows your theory up, and a new model is necessary to explain it and what you have discovered before. This is happening less and less, not more and more in science, because we do understand some stuff pretty well and we are building on that. > > > > > > Probability, statistics, and vaguely worded unfalsifiable predictions give Yagyas all the wiggle room needed for people who already "know" their effect and how they work to find all the evidence they need. We have so many cognitive gaps, and sometimes it is hard to face how poorly we are equipped to test such claims, especially after we have paid for them. > > > > And then you have A-hole scientists who sometimes subvert the process of inquiry into a way to support the latest pharmaceutical, only giving the method lip service(Not the kind that feels good) for some gold coins with "In God We Trust" stamped on them. > > > > And finally we have a complex mysterious world that has defied our ability to achieve complete knowledge with absolute certainty and this makes some people so nervous they turn to an explanation from a fairy tale to help them go to sleep. > > > > So epistemological humility is appropriate in facing the world. But that doesn't mean we don't know anything at all. We just don't everything. And we always have to be on the lookout for things we KNOW that aren't so. If we care about keeping it real, that is. > > > > > > Well, in a very real sense we KNOW nothing. We can only know what is NOT, not what IS. Its Hume's problem of induction, How many white swans do you need to see until you "know the truth" that all swans are white? 1000, one million, one billion? > > At one billion, you may say, "well, the statistical probability of knowing that there are no black swans is astronomically huge -- we have a sample of one billion. The probability that there are other than white swans is on the far far side of the tail (of the normal distibution). > > The problem is that the normal distribution accounts for some things nicely, and yet is hugely flawed as a representative distribution for far more things. You don't really know the distribution until you have seen the entire population, not just a sample. Many things have distributions with enormously fat tails. That is, they have a much higher probability of occurring than the normal distribution would predict. But hey, the white swan theory worked extremely well at predicting the color of swans. Everyone continued to see only white swans, "What a marvelous model we have", everyone beamed. Until one black swan was discovered. Then many. opps -- our poor normal distribution totally sucked and we fell for it. If we had be significantly on this model, we would hve bee nwiped out. > > The only thing we know now is that NOT all swans are white. We don't know what IS only what is NOT. A doctor can say he finds no evidence of disease in you. That is far far from saying ."I have evidence of no disease in you". >
Aren't we in bigger shit than this though in reality? To "know" the negative depends on "knowing" a positive viz. "Nabster in Tasmania has seen a black swan". But perhaps Nabster was pissed? perhaps it seemed like a swan, but wasn't really? Perhaps it was a white swan swimming in an oil slick? After all Nabster has been seeing some funny stars recently! So "We can only know what is NOT, not what IS" is too optimistic?
