Proof of Immortality, VI

[Loss Leader]

Q: Why do we need a definition of something we all experience every day?

For the same reason I need to be able to tell the difference between my wife and a hat.

 

[Belz...]

It never worked before. You ask him to move on, and he just keeps doing that fringe reset thing.

The only reason he abandoned his shroud thread was because his close ally surrendered the fight and closed his own pro-authenticity shroud web site.

No, we've never done that as a group. Someone is always humouring him rather than just refusing to fall for his tricks.

 

[Belz...]

At this point I think we just call in the "Jabba Length." It's like a Planck Length in physics but it is the point at which an argument cannot be subdivided anymore.

That's where we reach singularity and loop back to the beginning with a fringe reset.

 

[carlitos]

If an argument gets sub-issued 6.02 times 10²³, it reaches homeopathic levels.


Sent from my iPhone using Tapatalk

 

[abaddon]

For the same reason I need to be able to tell the difference between my wife and a hat.

Ah. Sachs.

 

[Belz...]

If an argument gets sub-issued 6.02 times 10²³, it reaches homeopathic levels.

Does that mean that it starts being convincing?

 

[John Jones]

If an argument gets sub-issued 6.02 times 10²³, it reaches homeopathic levels.

Avogadro's argument.

 

[Jabba]

Dave and others,
- Moving right along -- hopefully, the rest of my premises:

11. To formally re-evaluate OOFLam, we can use the following formula from Bayesian statistics: P(H|E)=P(E|H)*P(H)/(P(E|H)*P(H)+P(E|~H)*P(~H)).
12. There are 3 variables in that formula -- we've already discussed P(E|H), the likelihood of the event occurring, given H (OOFLam).
13. Another variable is the prior probability of H (and ~H).
14. There is a reasonable probability of at least 1% for ~H -- and therefore, no more than 99% for H.
15. The remaining variable is P(E|~H), the likelihood of the event occurring, given ~H. For now, I'll suggest 99%.
16. Inserting the numbers, we get that the posterior probability of H, after adding E to the evidence is: P(H|E)=10-100*.99/(10-100*.99+.99*.01). And rounding off, we get P(H|E)=0/.099, or zero.
17. So, by adding this new info to the evidence for H and rounding off, we get that the probability of H being true is zero.

- That ought to give us some more disagreements to discuss.

 

[John Jones]

Dave and others,
- Moving right along -- hopefully, the rest of my premises:

11. To formally re-evaluate OOFLam, we can use the following formula from Bayesian statistics: P(H|E)=P(E|H)*P(H)/(P(E|H)*P(H)+P(E|~H)*P(~H)).
12. There are 3 variables in that formula -- we've already discussed P(E|H), the likelihood of the event occurring, given H (OOFLam).
13. Another variable is the prior probability of H (and ~H).
14. There is a reasonable probability of at least 1% for ~H -- and therefore, no more than 99% for H.
15. The remaining variable is P(E|~H), the likelihood of the event occurring, given ~H. For now, I'll suggest 99%.
16. Inserting the numbers, we get that the posterior probability of H, after adding E to the evidence is: P(H|E)=10-100*.99/(10-100*.99+.99*.01). And rounding off, we get P(H|E)=0/.099, or zero.
17. So, by adding this new info to the evidence for H and rounding off, we get that the probability of H being true is zero.

- That ought to give us some more disagreements to discuss.

This has been done to death. Move along.

 

[godless dave]

Jabba, you seem to be implying that this:

The 'thing' that recognizes or experiences existence. Whatever it is that is aware.

suggests this:

that which would be looking out two sets of eyes if it were actually duplicated.

Can you explain why the one suggests the other to you?

 

[Belz...]

Dave and others,
- Moving right along -- hopefully, the rest of my premises:

No. You've done that. We know what they are. Move along to the demonstration.

 

[JoeMorgue]

No Jabba no more "Premises." No more definitions. No more defining the terms. No more setting up the pieces.

 

[jond]

Dave and others,
- Moving right along -- hopefully, the rest of my premises:

11. To formally re-evaluate OOFLam, we can use the following formula from Bayesian statistics: P(H|E)=P(E|H)*P(H)/(P(E|H)*P(H)+P(E|~H)*P(~H)).
12. There are 3 variables in that formula -- we've already discussed P(E|H), the likelihood of the event occurring, given H (OOFLam).
13. Another variable is the prior probability of H (and ~H).
14. There is a reasonable probability of at least 1% for ~H -- and therefore, no more than 99% for H.
15. The remaining variable is P(E|~H), the likelihood of the event occurring, given ~H. For now, I'll suggest 99%.
16. Inserting the numbers, we get that the posterior probability of H, after adding E to the evidence is: P(H|E)=10-100*.99/(10-100*.99+.99*.01). And rounding off, we get P(H|E)=0/.099, or zero.
17. So, by adding this new info to the evidence for H and rounding off, we get that the probability of H being true is zero.

- That ought to give us some more disagreements to discuss.

You mean more disagreements for you to ignore.

 

[Belz...]

Jabba, you seem to be implying that this:



suggests this:



Can you explain why the one suggests the other to you?

Just ask him to move on to the demonstration. There's nothing more to be gleaned from his repeating of premises and definitions.

 

[Dave Rogers]

14. There is a reasonable probability of at least 1% for ~H -- and therefore, no more than 99% for H.

Nether orifice origin fallacy.

15. The remaining variable is P(E|~H), the likelihood of the event occurring, given ~H. For now, I'll suggest 99%.

And a far more egregious nether orifice origin fallacy.

The crux of your argument is, mathematically, the assertion that the probability of your existence is so much greater if the naturalistic hypothesis is untrue than if it is true that the naturalistic hypothesis cannot be true. In order to support that argument, you'll need, at the very least, a reasonable justification as to why your existence is in fact any more probably given that the naturalistic hypothesis is untrue. Absent any such justification, even without taking into account all the other fatal flaws in your reasoning (which have been expounded in excruciating detail over the last five years), you still have no argument.

This is a perfect example of what I termed the Unevaluated Inequality fallacy; the assertion, without justification, that one of two completely unknown numbers must, despite a complete lack of knowledge of either value or any relationship between them, must be greater than the other. Your invocation is perhaps the strongest invocation I've yet seen because it implies that one of the two numbers, P(E|~H), is so greater than the other, P(E|H), that the other can be neglected by comparison. Yet your sole source for an inequality, supposedly, of many orders of magnitude, is your own unsupported assertion. This makes it quite possibly the worst argument I've ever seen on this forum, which is saying a lot.

Dave

 

[Toontown]

For the same reason I need to be able to tell the difference between my wife and a hat.

You need a description of your wife to tell the difference between her and a hat?

You can't even do it. The wife that can be described is not the true wife.

 

[Spektator]

If it complains when you put it on your head, at least you know it's not a hat.

 

[Humots]

7. Often, however, all of the alternative possible results/events produced by the particular situation are extremely unlikely -- in such a case, the unlikelihood of the particular event produced is not evidence
against the hypothesis.

This seems to be something of a concession by Jabba: that even if the odds of all possible events are extremely low,someevent will be produced.

i.e. even a 10¹⁰⁰ sided dice will come up on some side.

8. In such a case, in order to be evidence against the hypothesis, the particular event needs to be "set apart" from most of the other possible results in a way that is meaningful to the particular hypothesis. A good example is when a lottery is won by the second cousin of the lottery controller.

No, bad example.

Jabba: you suggested second cousin. You could easily have named dozens, nay hundreds, of relationships, each equally "significant" for your purposes.

Texas. Sharpshooter. Fallacy.

Situation:
(1) Person A controls the lottery.
(2) Person B wins the lottery.

OK gang! Let's find a "relationship" between A and B. Time to play Six Degrees of Separation!

We can chain together family relations, marital relations, professional relations, friendships, car pools, class lawsuits, office proximity, whatever

Perhaps B is the third cousin of the wife of the man who fixes the car of a friend of a guy who carpools with a lawyer who handled a class lawsuit against the city that holds the lottery A controls.

AHA!

9. Consequently, in order for my current existence to be evidence against OOFLam, I need to be set apart in a way meaningful to OOFLam.
10. That is the case.

Is it? How so? Your last suggestion was a repeat of your "If I wasn't here, the universe might as well not exist" argument.

 

[Loss Leader]

17. So, by adding this new info to the evidence for H and rounding off, we get that the probability of H being true is zero.

There is no new evidence. You're lying so you can fill whatever you want into your equation.

You need a description of your wife to tell the difference between her and a hat?

I've had both on my head.

 

[MRC_Hans]

Dave and others,
- Moving right along -- hopefully, the rest of my premises:

11. To formally re-evaluate OOFLam, we can use the following formula from Bayesian statistics: P(H|E)=P(E|H)*P(H)/(P(E|H)*P(H)+P(E|~H)*P(~H)).
12. There are 3 variables in that formula -- we've already discussed P(E|H), the likelihood of the event occurring, given H (OOFLam).
13. Another variable is the prior probability of H (and ~H).
14. There is a reasonable probability of at least 1% for ~H -- and therefore, no more than 99% for H.
15. The remaining variable is P(E|~H), the likelihood of the event occurring, given ~H. For now, I'll suggest 99%.
16. Inserting the numbers, we get that the posterior probability of H, after adding E to the evidence is: P(H|E)=10-100*.99/(10-100*.99+.99*.01). And rounding off, we get P(H|E)=0/.099, or zero.
17. So, by adding this new info to the evidence for H and rounding off, we get that the probability of H being true is zero.

- That ought to give us some more disagreements to discuss.

Jabba, this is nonsense, and you know it. I have done statistics professionally. Do you know the saying: "Garbage in, garbage out"? You must carefully support every figure you put into the calculation with verifiable evidence, otherwise it is entirely worthless. Even one unverifiable parameter, and your calculation is crud.

Hans