It is impossible for A and ~A to both be evidence for B. If a lack of sabotage is evidence for a fifth column, then an actual sabotage event must be evidence against a fifth column.
This is not correct.
One explanation (call it A) for why there fails to be sabotage is that the Fifth Column is trying to be sneaky and inflict maximum damage later on when no one expects it. The probability of that is greater than 0, so it is a legitimate potential explanation for the apparent absence of sabotage. But, on further thought, there is this other possible explanation (call it B): the absence of a Fifth Column will produce an absence of sabotage. The probability of this is also greater than 0.
So here we have the event (Fifth Column exists) constituting evidence for (absence of sabotage) (perhaps the probability is low, but not zero). Surely it is fair to take it for granted that ~(Fifth Column exists) also constitutes evidence for (absence of sabotage). So that's an example where an event and its negation can potentially both be evidence for something.
I think what you really mean to say is that since P(no sabotage) = P(no sabotage | Fifth Column) P(Fifth Column) + P(no sabotage | no Fifth Column) P(no Fifth Column), and since no sabotage has been observed, making P(no sabotage) = 1, this must imply that P(no sabotage | Fifth Column) P(Fifth Column) = 1 - P(no sabotage | no Fifth Column) P(no Fifth Column).
If we then make the (perhaps unwarranted) assumption that the prior probabilities are equal, i.e. P(Fifth Column) = P(no Fifth Column), then when deciding via a maximum a posterior decision rule which hypothesis to believe, we wind up with P(no sabotage | Fifth Column) = 1 - P(no sabotage | no Fifth Column), and thus we simply select the hypothesis corresponding to whichever conditional probability is larger... and from this, our intuitions about basic logic would say it doesn't make sense to assign probabilities in such a way that (no Fifth Column) is less likely to cause no sabotage than (Fifth Column), and this is what creates the effect you are noting that some event A and its negation ~A shouldn't both be evidence for the same thing.
Compactly, it's only fair to claim that A and ~A cannot both be evidence for B in some very special situations. In general, though, A and ~A definitely can both serve as supporting evidence for B, it's just that they will corroborate B to different degrees and this may or may not be further offset by the prior distributions of A and ~A.
But it is important not to assert the incorrect generalization that "It is impossible for A and ~A to both be evidence for B."
Did you take the other replies to Tom McCabe's comment, which raise the same question you do but offer the opposite answer, into consideration? The appeal to intuition that a fifth column might be refraining from sabotage in order to create more effective sabotage later does not let you take both A and ~A as evidence for B. Any way you verbally justify it, you will still be dutch-bookable and incoherent.
Without losing the generality of the theorems of probability, let me address your particular narrative: If you believe that, if a fifth column exists, it ...
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.