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> And yet nobody seems to be aware of it, and both the popular and scientific press continue with the “where are they?” Fermi paradox headlines. A reasonable e

> And yet nobody seems to be aware of it, and both the popular and scientific press continue with the “where are they?” Fermi paradox headlines. A reasonable explanation here is that the paper is not correct because it relies on unfounded priors, which is generally where most Bayesian work falls flat.

DennisP 3y ago
So your prior is that Bayesian papers are likely to have unfounded priors. Now I'm wondering how well-founded your prior is.
_yb2s 3y ago
You have it backwards- the traditional Drake equation relies on unfounded priors, and by using a Bayesian approach they have avoided that problem. Instead of making up terms from nothing like the Drake equation, they are able to represent only the data we actually have, and leave the rest uncertain. The priors are carefully encoding the actual information they have, and incorporating the extreme lack of prior knowledge as uniform or nearly uniform priors over an extremely wide range for the terms we have no data on. That is the basic takeaway here- with almost no knowledge about a large number of factors (as the Drake equation is constructed), there is an extremely high chance that once of those unknown factors is actually nearly zero, even when your expectation value for each (e.g. what would have been used in the traditional Drake equation) is relatively high. N (number of civilizations) therefore approaches zero, even if there is no single term that you are pretty sure is near zero. The Bayesian approach here allows for a rigorous representation of our (extreme lack of) knowledge and gets to the truth of the matter: civilizations face a huge number of possible bottlenecks, each of which we know almost nothing about the probabilities of. This means, there is a strong chance at least one of those is a massive filter, even if we don't know which.
- vlovich123 3y ago
  While I agree with both the approach and result intuitively, the assumption of uniform unknown priors feels like it could be a huge source of errors
  - _yb2s 3y ago
    They are demonstrating a fundamental flaw in the logical reasoning behind the original Drake equation, that is robust to specific choices of distributions, or parameters to include or exclude. Anytime you multiply a large number of uncertain probability distributions, the resulting posterior will have most of the probability mass near zero. This result is not sensitive to which distribution or bounds you choose. The Drake equation is nonsense, because it is effectively assuming certainty about every single term- and that is the only way to produce a result much higher than zero. When you are multiplying seven unknown together, you can be fairly certain that the result is close to zero without knowing the value of any of the terms, unless you have some real information that none of terms can be near zero.
    - jncfhnb 3y ago
      This is some dumb second order Bayesian reasoning. You’re declaring a prior for arbitrary random variables as if their distributions themselves are sampled from a distribution. They are not. You cannot be certain that seven random things multiplied together is close to zero. That statement is very obviously wrong. Further “near zero” is a misleading term at best because it neglects to mention that we are multiplying it by a large number to get an expected value.
      - mjburgess 3y ago
        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.

DennisP 3y ago

So your prior is that Bayesian papers are likely to have unfounded priors. Now I'm wondering how well-founded your prior is.

_yb2s 3y ago

You have it backwards- the traditional Drake equation relies on unfounded priors, and by using a Bayesian approach they have avoided that problem. Instead of making up terms from nothing like the Drake equation, they are able to represent only the data we actually have, and leave the rest uncertain. The priors are carefully encoding the actual information they have, and incorporating the extreme lack of prior knowledge as uniform or nearly uniform priors over an extremely wide range for the terms we have no data on. That is the basic takeaway here- with almost no knowledge about a large number of factors (as the Drake equation is constructed), there is an extremely high chance that once of those unknown factors is actually nearly zero, even when your expectation value for each (e.g. what would have been used in the traditional Drake equation) is relatively high. N (number of civilizations) therefore approaches zero, even if there is no single term that you are pretty sure is near zero. The Bayesian approach here allows for a rigorous representation of our (extreme lack of) knowledge and gets to the truth of the matter: civilizations face a huge number of possible bottlenecks, each of which we know almost nothing about the probabilities of. This means, there is a strong chance at least one of those is a massive filter, even if we don't know which.

vlovich123 3y ago
While I agree with both the approach and result intuitively, the assumption of uniform unknown priors feels like it could be a huge source of errors
- _yb2s 3y ago
  They are demonstrating a fundamental flaw in the logical reasoning behind the original Drake equation, that is robust to specific choices of distributions, or parameters to include or exclude. Anytime you multiply a large number of uncertain probability distributions, the resulting posterior will have most of the probability mass near zero. This result is not sensitive to which distribution or bounds you choose. The Drake equation is nonsense, because it is effectively assuming certainty about every single term- and that is the only way to produce a result much higher than zero. When you are multiplying seven unknown together, you can be fairly certain that the result is close to zero without knowing the value of any of the terms, unless you have some real information that none of terms can be near zero.
  - jncfhnb 3y ago
    This is some dumb second order Bayesian reasoning. You’re declaring a prior for arbitrary random variables as if their distributions themselves are sampled from a distribution. They are not. You cannot be certain that seven random things multiplied together is close to zero. That statement is very obviously wrong. Further “near zero” is a misleading term at best because it neglects to mention that we are multiplying it by a large number to get an expected value.
    - mjburgess 3y ago
      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.