Using Bayes to dismiss fringe phenomena

It would be a powerful tool to be able to dismiss fringe phenomena, prior to empirical investigation, on firm epistemological ground.

Thus I have elaborated on the possibility of doing so using Bayes, and this is my result:

Using Bayes to dismiss fringe phenomena

What do you think of it?

It would be a powerful tool to be able to dismiss fringe phenomena, prior to empirical investigation, on firm epistemological ground.

Thus I have elaborated on the possibility of doing so using Bayes, and this is my result:

Using Bayes to dismiss fringe phenomena

What do you think of it?

Going outside the argument, if you come up with 50% chance that any given observation of a UAP was actually observing a UAP, you've done something wrong.

More specifically,

P(UAP): What is the prior probability that a broadly unknown aerial phenomena UAP exists? In this case of UAP's, there exists large bodies of observation data. One governmental study of 3200 observations concluded that 22% of the observations could not be identified. These were separate from another 9% of cases which could not be identified due to insufficient observation data. Thus we can tentatively assess P(UAP) to be 0.22

Didn't click through to the study, but what is an 'observation' in this case? I make 3200 observations a day, but I don't observe 70 UAPs a day. It seems far more likely that this was 3200 instances of someone reporting a UAP - in other words, the 22% is pointing towards P(UAP|UO), not P(UAP). But "we couldn't identify this" doesn't necessarily mean it was a UAP, so this is still an overestimate.

We don't need to (and for the sake of internal consistency, shouldn't) estimate this probability on its own.

You probably should, for sanity checking. You calculate P(UO1) as 0.332, which is clearly way too high - I think that most people don't see a single thing in their lifetime that they think is a UAP. If you estimate something, and then calculate a number which is different my that many orders of magnitude, you can go back and check your workings.

Previously we decided P(UO1|UAP) to be 0.8 [...] Thus P(UO1|¬UAP) must be 1−0.8=0.2

No.

You're correct about the study. What they actually found was that a certain fraction of UFO reports (I.e., what loldrup calls UO) had reported descriptions that didn't match any known class of object. So yes, it's more like P(UAP|UO) in loldrup's notation; and yes, it's not "thing known not to be a known class of object" but "thing whose reported description we didn't find a good match for" which is of course consistent not only with what loldrup calls the UAP hypothesis but also with inaccurate reporting and with known classes of object having currently-unknown behaviour.

[EDITED to fix formatting.]

1[anonymous]12y

This percentage is conditional on P( observation | UAP ) being high (0.8). I guess only a minority of such observations (if any) are that high. [...] If this is so, then I can't use any empirical study to estimate P( UAP ). What value should I then guesstimate P( UAP ) to be? If I choose a generic 0.5, then the result will be an even higher P( UAP | observation ) than 0.53. Thus it would be even harder to dismiss the observation. If I instead make my own estimation, say, 0.0000001, then one could ask if this was still an epistemologically sound approach. My choice would be rather arbitrary. [...] Could you elaborate? Thanks for your feedback :)