Nope, I wasn't familiar. Very interesting, thanks!
Probability assignments don't have truth value,
Sure they do. If you're a Bayesian, an agent truly asserts that the (or, better, his) probability of a claim is X iff his degree of belief in the claim is X, however you want to cash out "degree of belief". Of course, there are other questions about the "normatively correct" degrees of belief that anyone in the agent's position should possess, and maybe those lack determinate truth-value.
I don't see the relation between the two. It seems like you're pointing out that Jaynes/people here don't believe there are "objectively correct" probability distributions that rationality compels us to adopt. But this is compatible with there being true probability claims, given one's own probability distribution - which is all that's required.
That statement is too imprecise to capture Jaynes's view of probability.
Of course; it wasn't intended to capture the difference between so-called objective Bayesianism vs. subjective Bayesianism. The tension, if it arises at all, arises from any sort of Bayesianism. That the rules prescribed by Jaynes don't pick out the "true" probability distributions on a certain question is compatible with probability claims like "It will probably rain tomorrow" having a truth-value.
I don't understand where the tension is supposed to come in.
It just seems really weird to be able to correctly say that A caused B when, in fact, A had nothing to do with B. If that doesn't seem weird to you, then O.K.
The idea that causation is in the mind, not in the world is part of the Humean tradition
I think that's unclear; I side with those who think Hume was arguing for causal skepticism rather than some sort of subjectivism.
Causation, Probability and Objectivity
Most people here seem to endorse the following two claims:
1. Probability is "in the mind," i.e., probability claims are true only in relation to some prior distribution and set of information to be conditionalized on;
2. Causality is to be cashed out in terms of probability distributions á la Judea Pearl or something.
However, these two claims feel in tension to me, since they appear to have the consequence that causality is also "in the mind" - whether something caused something else depends on various probability distributions, which in turn depends on how much we know about the situation. Worse, it has the consequence that ideal Bayesian reasoners can never be wrong about causal relations, since they always have perfect knowledge of their own probabilities.
Since I don't understand Pearl's model of causality very well, I may be missing something fundamental, so this is more of a question than an argument.
In response to Evolutionary psychology: evolving three eyed monsters
No considerations are given for the strength of the advantage
I wish this were stressed more often. It's really easy to think up selective pressures on any trait and really hard to pin down their magnitude. This means that most armchair EP explanations have very low prior probabilities by default, even if they seem intuitively reasonable.
In response to Cult impressions of Less Wrong/SI
The word "cult" never makes discussions like these easier. When people call LW cultish, they are mostly just expressing that they're creeped out by various aspects of the community - some perceived groupthink, say. Rather than trying to decide whether LW satisfies some normative definition of the word "cult," it may be more productive to simply inquire as to why these people are getting creeped out. (As other commenters have already been doing.)
Don't mindkill their cached thoughts.
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Bayesian epistemology maintains that probability is degree of belief. Assertions of probabilities are therefore assertions of degrees of belief, which are psychological claims and therefore obviously have or can have truth-value. Of course, Bayesians can be more nuanced and take some probability claims to be about degrees of belief in the minds of some idealized reasoner; but "the degree of belief of an idealized reasoner would be X given such-and-such" is still truth-evaluable.
The question was primarily about the role of probability in Pearl's account of causality, not the basic meaning of probability in Bayesian epistemology.