Again, I cannot see how you can observe nothing and call it evidence. It is semantics, really, since it makes no difference for the equations, but it makes ~E a positive observation of something and E a negative observation, which is, to me, silly.
These are Q and R, respectively. They are not negations of each other. Do you agree?
Yes. Though, again, I'd rather R be "There is a 5th column" to keep it from being confusing.
With Q there was no evidence that the fifth column was coordinating a timed attack, yet Warren's strongest evidence for it was that there was no evidence for it.
Pearl harbor types of evidence are black swans. You can't just pull them out of the air and add them to your reasoning when you have no solid justification for it. There are a billion other black swans he could have used - what if the Japanese are actually all vampires and had designs on draining the Americans dry? You've got no evidence they aren't, so clearly they are just biding their time!
The former is slightly more reasonable, since something similar had happened recently (though in an entirely different context), but it is no more justified as evidence than the evidence in the vampire scenario.
You must look for other evidence that suggests the 5th column was planning an attack, the fact that you have not been attacked yet is not in any way evidence that they are planning an attack. It is only really evidence that, if they were planning something, they hadn't done it yet. That's all you can get from that - just a guess.
To that end, Warren had no evidence that an American chapter of the 5th Column even existed. There was secret evidence to that effect, but he was not privy to it. He was making the whole thing up because he was afraid.
It was completely unjustified.
Besides, it doesn't make sense. Timed attacks are designed to catch you off guard. After Pearl Harbor, people were always on guard. It wouldn't have had the same effect; a much more effective strategy would have been smaller, guerrilla-type sabotages from within, which they also had zero evidence of.
Again, I cannot see how you can observe nothing and call it evidence.
He didn't observe "nothing." He observed factories and shipyards and so forth, continuing to operate without apparent sabotage.
From Robyn Dawes’s Rational Choice in an Uncertain World:
Consider Warren’s argument from a Bayesian perspective. When we see evidence, hypotheses that assigned a higher likelihood to that evidence gain probability, at the expense of hypotheses that assigned a lower likelihood to the evidence. This is a phenomenon of relative likelihoods and relative probabilities. You can assign a high likelihood to the evidence and still lose probability mass to some other hypothesis, if that other hypothesis assigns a likelihood that is even higher.
Warren seems to be arguing that, given that we see no sabotage, this confirms that a Fifth Column exists. You could argue that a Fifth Column might delay its sabotage. But the likelihood is still higher that the absence of a Fifth Column would perform an absence of sabotage.
Let E stand for the observation of sabotage, and ¬E for the observation of no sabotage. The symbol H1 stands for the hypothesis of a Japanese-American Fifth Column, and H2 for the hypothesis that no Fifth Column exists. The conditional probability P(E | H), or “E given H,” is how confidently we’d expect to see the evidence E if we assumed the hypothesis H were true.
Whatever the likelihood that a Fifth Column would do no sabotage, the probability P(¬E | H1), it won’t be as large as the likelihood that there’s no sabotage given that there’s no Fifth Column, the probability P(¬E | H2). So observing a lack of sabotage increases the probability that no Fifth Column exists.
A lack of sabotage doesn’t prove that no Fifth Column exists. Absence of proof is not proof of absence. In logic, (A ⇒ B), read “A implies B,” is not equivalent to (¬A ⇒ ¬B), read “not-A implies not-B .”
But in probability theory, absence of evidence is always evidence of absence. If E is a binary event and P(H | E) > P(H), i.e., seeing E increases the probability of H, then P(H | ¬ E) < P(H), i.e., failure to observe E decreases the probability of H . The probability P(H) is a weighted mix of P(H | E) and P(H | ¬ E), and necessarily lies between the two.1
Under the vast majority of real-life circumstances, a cause may not reliably produce signs of itself, but the absence of the cause is even less likely to produce the signs. The absence of an observation may be strong evidence of absence or very weak evidence of absence, depending on how likely the cause is to produce the observation. The absence of an observation that is only weakly permitted (even if the alternative hypothesis does not allow it at all) is very weak evidence of absence (though it is evidence nonetheless). This is the fallacy of “gaps in the fossil record”—fossils form only rarely; it is futile to trumpet the absence of a weakly permitted observation when many strong positive observations have already been recorded. But if there are no positive observations at all, it is time to worry; hence the Fermi Paradox.
Your strength as a rationalist is your ability to be more confused by fiction than by reality; if you are equally good at explaining any outcome you have zero knowledge. The strength of a model is not what it can explain, but what it can’t, for only prohibitions constrain anticipation. If you don’t notice when your model makes the evidence unlikely, you might as well have no model, and also you might as well have no evidence; no brain and no eyes.
1 If any of this sounds at all confusing, see my discussion of Bayesian updating toward the end of The Machine in the Ghost, the third volume of Rationality: From AI to Zombies.