[mjburgess]

Distributions are sampled from distributions -- it is this problem which makes global scepticism an even minimally interesting problem.

When faced with "global, recursive" epistemic problems one arrives at an extremely power-law asymmetric distribution where the "bayesian value" of almost all evidence is near zero.

We live our entire lives in this "nero zero" range, and i'd suppose, this makes a "pure bayesian" solution to the problem of knowledge deficient. Since we succeed in knowing, so we succeed in making hyperfine determinations.

This sort of "hyperfine epistemology" works globally to allow us to "know at all", but as you're sensing here -- it's pretty much useless for any local problem.

Perhaps this is just the single up-side of the bayesian approach to the drake eqn: it shows how impossible it is to state such an eqn, let alone evaluate it. We cannot, a priori, make such hyperfine determiniations on such circumstantial matters.

[jncfhnb]

This post is full of fancy word nonsense.

“Distributions are sampled from distributions” is meaningless because you cannot define the meta distribution. But more importantly, the Drake equation is not a RANDOM SAMPLE from a population of distributions. So the idea of sampling distributions is irrelevant even if true. The naive math of multiplying them together is invalid.

[mjburgess]

It really doesnt matter what the "meta-distribution" is, bar trivial ones. My whole point is that we can augment bayesianism by a-priori choosing these meta-distrbutins.

Is my hand in front of me? Is what's in front of me real? Are my perceptions indicative of reality? etc. -- keep recursing

If you're drawing from any sort of epistemically plausible (ie., any plausible model of subjective uncertaintiy) distribution on each of these points, you'll "recurse" to some extreme distribution --- where all possible evidence basically makes no difference.

This is why there cant really be an "evidential" case for realism --- and why bayesianism is an incomplete epistemology.

You have to assert the truth of some basic facts, and thereby focus in on a "region" of this "extreme distribution" which is near-zero. And say only, "simply by being above zero, i'll believe it".

That's the solution to the problem of scepticism.

But this doesn't work for local issues, because locally there really isnt any kind of non-bayesian a-priori analysis which can say, "here, believe the non-zero".

ie., you can 'complete' bayesianism globally by meta-theoretical concerns, but not locally. Meaning that 'from ignorance, only ignorance' everywhere, esp. the drake eqn.

The failure of bayesianism is an indictment of darke-like reasoning -- this only works on genuienly global matters.

eg., "a priori, the world exists, therefore the meta-distributin must be so constrainted..."

[jncfhnb]

Do you read your own writing here?

[mjburgess]

I'm talking to a very rarified audience, for sure. Giving a bayesian gloss on moorean epistemology is not really a project for a hacker news comment.

[karpierz]

There's the bonus problem of: even if you magically have correct priors, you still need to assume that Drake's Equation is a good model for the generation process of civilizations. If the equation is missing terms or has extra terms, no amount of Bayesian reasoning helps correct for that.

It's like thinking that you can use Bayesian reasoning to determine the likelihood of Russell's Teapot existing.

[mjburgess]

Yes, this is essentially what i mean by "distributions are sampled from distributions", ie., there's a subjective uncertainty in the choice of model but also an uncertainty in the very determination of that uncertainty.

You can model a plausible "final stable distribution", after all these recursions, with a power law.

This makes intuitive sense, if you consider how science works: all the confirmatory evidence in the world doesn't help, all the information lies in the single refutative point. This is how power laws work.

So basically we're always operating under a heavily under-determined region with high uncertainty, and we can only improve that by disconfirmatory apparent outliers.

[3abiton]

It's always fun to see frequentists/bayesians infights in HN comments. It's almost a guilty pleasure for me.

[jncfhnb]

Everyone in this thread appears to prefer Bayesian