Do you have any evidence that Warren was reasoning in this way rather than the less-charitable version, and if so, why didn't he say so explicitly
Sorry for taking so long to reply to this.
I think that a close and strict reading supports my interpretation. I don't see the need for an unduly charitable reading.
First, I assume the following context for the quote: Warren had argued for (or maybe only claimed) a high probability for the proposition that there is a Japanese fifth column within the US. Let R be this italicized proposition. Then Warren has argued that p(R) >> 0.
Given that context, here is how I parse the quote, line-by-line:
[A] questioner pointed out that there had been no sabotage or any other type of espionage by the Japanese-Americans up to that time.
I take the questioner to be asserting that there has been no observed sabotage or any other type of espionage by Japanese-Americans up to that time. Let E be this proposition.
Warren responds:
I take the view that this lack [of subversive activity] is the most ominous sign in our whole situation.
I take Warren to be saying that the expected cost of not interring Japanese-Americans is significantly higher after we update on E than it was before we updated on E. Letting D be the "default" action in which we don't inter Japanese-Americans, Warren is asserting that EU(D | E) << EU(D).
The above assertion is the conclusion of Warren's reasoning. If we can show that this conclusion follows from correct Bayesian reasoning from a psychologically realistic prior, plus whatever evidence he explicitly adduces, then the quote cannot serve as an example of the fallacy that Eliezer describes in this post.
Now, we may think that that "psychologically realistic prior" is very probably based in turn on "fear plus poor thinking". But Warren doesn't explicitly show us where his prior came from, so the quote in and of itself is not an example of an explicit error in Bayesian reasoning. Whatever fallacious reasoning occurred, it happened "behind the scenes", prior to the reasoning on display in the quote.
Continuing with my parsing, Warren goes on to say:
It [this lack of subversive activity] convinces me more than perhaps any other factor that the sabotage we are to get, the Fifth Column activities are to get, are timed just like Pearl Harbor was timed... I believe we are just being lulled into a false sense of security.
Let Q be the proposition that there is a Japanese fifth column in America, and it will perform a timed attack, but right now it is lulling us into a false sense of security.
I take Warren to be claiming that p(Q | E) >> p(Q), and that p(Q | E) is sufficiently large to justify saying "I believe Q".
It remains only to give a psychologically realistic prior distribution p such the claims above follow — that is, we need that
This will suffice to invalidate the Warren quote as an example for this post.
It is a mathematical fact that such priors exist in an abstract sense. Do you think it unlikely that such a prior is psychologically realistic for someone in Warren's position? I think that selection effects and standard human biases make it very plausible that someone in his position would have such a prior.
If you're still skeptical, we can discuss which priors are psychologically realistic for someone in Warren's position.
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.