I'm what David Chalmers would call a "Type-A materialist" which means that I deny the existence of "subjective facts" which aren't in some way reducible to objective facts.
The concerns Chalmers wrote about focused on the nature of phenomenal experience, and the traditional dichotomy between subjective and objective in human experience. That distinction draws a dividing line way off to the side of what I'm interested in. My main concern isn't with ineffable consciousness, it's with cognitive processing of information, information def...
The Bayesian calculation only needs to use the event "Tuesday exists"
I can't follow this. If "Tuesday exists" isn't indexical, then it's exactly as true on Monday as it is on Tuesday, and furthermore as true everywhere and for everyone as it is for anyone.
there doesn't seem to be any non-arbitrary way of deriving a distribution over centered worlds from a distribution over uncentered ones.
Indeed, unless you work within the confines of a finite toy model. But why go in that direction? What non-arbitrary reason is there not to ...
I suppose I'm being obtuse about this, but please help me find my way through this argument.
- The event "it is Monday today" is indexical. However, an "indexical event" isn't strictly speaking an event. (Because an event picks out a set of possible worlds, whereas an indexical event picks out a set of possible "centered worlds".) Since it isn't an event, it makes no sense to treat it as 'data' in a Bayesian calculation.
Isn't this argument confounded by the observation that an indexical event "It is Tuesday today"...
On further reflection, both Ancestor and each Descendant can consider the proposition P(X) = "X is a descendant & X is a lottery winner". Given the setup, Ancestor can quantify over X, and assign probability 1/N to each instance. That's how the statement {"I" will win the lottery with probability 1} is to be read, in conjunction with a particular analysis of personal identity that warrants it. This would be the same proposition each descendant considers, and also assigns probability 1/N to. On this way of looking at it, both Ance...
There need be no information transferred.
I didn't quite follow this. From where to where?
But anyway, yes, that's correct that the referents of the two claims aren't the same. This could stand some further clarification as to why. In fact, Descendant's claim makes a direct reference to the individual who uttered it at the moment it's uttered, but Ancestor's claim is not about himself in the same way. As you say, he's attempting to refer to all of his descendants, and on that basis claim identity with whichever particular one of them happens to win th...
I don't think personal identity is a mathematical equivalence relation. Specifically, it's not symmetric: "I'm the same person you met yesterday" actually needs to read "I was the same person you met yesterday"; "I will be the same person tomorrow" is a prediction that may fail (even assuming I survive that long). This yields failures of transitivity: "Y is the same person as X" and "Z is the same person as X" doesn't get you "Y is the same person as Z".
...Given that you know there will be a fut
Did I accuse someone of being incoherent? I didn't mean to do that, I only meant to accuse myself of not being able to follow the distinction between a rule of logic (oh, take the Rule of Detachment for instance) and a syntactic elimination rule. In virtue of what do the latter escape the quantum of sceptical doubt that we should apply to other tautologies? I think there clearly is a distinction between believing a rule of logic is reliable for a particular domain, and knowing with the same confidence that a particular instance of its application has bee...
Ah, thanks for the pointer. Someone's tried to answer the question about the reliability of Bayes' Theorem itself too I see. But I'm afraid I'm going to have to pass on this, because I don't see how calling something a syntactic elimination rule instead a law of logic saves you from incoherence.
Probabilities of 1 and 0 are considered rule violations and discarded.
What should we take for P(X|X) then?
And then what can I put you down for the probability that Bayes' Theorem is actually false? (I mean the theorem itself, not any particular deployment of it in an argument.)
What should we take for P(X|X) then?
The one that I confess is giving me the most trouble is P(A|A). But I would prefer to call that a syntactic elimination rule for probabilistic reasoning, or perhaps a set equality between events, rather than claiming that there's some specific proposition that has "Probability 1".
and then
...Huh, I must be slowed down because it's late at night... P(A|A) is the simplest case of all. P(x|y) is defined as P(x,y)/P(y). P(A|A) is defined as P(A,A)/P(A) = P(A)/P(A) = 1. The ratio of these tw
We're getting ahead of the reading, but there's a key distinction between the plausibility of a single proposition (i.e. a probability) and the plausibilities of a whole family of related plausibilities (i.e. a probability distribution).
Ok, that sounds helpful. But then my question is this-- if we have whole family of mutually exclusive propositions, with varying real numbers for plausibilities, about the plausibility of one particular proposition, then the assumption that that one proposition can have one specific real number as its plausibility is ca...
Perhaps it would be wiser to use complex numbers for instance.
Perhaps it might be wiser to use measures (distributions), or measures on spaces of measures, or iterate that construction indefinitely. (The concept of hyperpriors seems to go in this direction, for example.)
...But intuitively it seems very likely that if you tell me two different propositions, that I can say either that one is more likely than the other, or that they are the same. Are there any special cases where one has to answer "the probabilities are uncomparable" that makes yo
- Can you think of further desiderata for plausible inference, or find issues with the one Jaynes lays out?
I find desideratum 1) to be poorly motivated, and a bit problematic. This is urged upon us in Chapter 1 mainly by considerations of convenience: a reasoning robot can't calculate without numbers. But just because a calculator can't calculate without numbers doesn't seem a sufficient justification to assume those numbers exist, i.e., that a full and coherent mapping from statements to plausibilities exists. This doesn't seem the kind of thing we ...
Perhaps this is beating a dead horse, but here goes. Regarding your two variants:
...1 Same as SSB except If heads, she is interviewed on Monday, and then the coin is turned over to tails and she is interviewed on Tuesday. There is amnesia and all of that. So, it's either the sequence (heads on Monday, tails on Tuesday) or (tails on Monday, tails on Tuesday). Each sequence has a 50% probability, and she should think of the days within a sequence as being equally likely. She's asked about the current state of the coin. She should answer P(H)=1/4.
Thanks for your response. I should have been clearer in my terminology. By "Iterated Sleeping Beauty" (ISB) I meant to name the variant that we here have been discussing for some time, that repeats the Standard Sleeping Beauty problem some number say 1000 of times. In 1000 coin tosses over 1000 weeks, the number of Heads awakenings is 1000 and the number of Tails awakenings is 2000. I have no catchy name for the variant I proposed, but I can make up an ugly one if nothing better comes to mind; it could be called Iterated Condensed Sleeping Bea...
Yet one more variant. On my view it's structurally and hence statistically equivalent to Iterated Sleeping Beauty, and I present an argument that it is. This one has the advantage that it does not rely on any science fictional technology. I'm interested to see if anyone can find good reasons why it's not equivalent.
The Iterated Sleeping Beaty problem (ISB) is the original Standard Sleeping Beauty (SSB) problem repeated a large number N of times. People always seem to want to do this anyway with all the variations, to use the Law of Large Numbers to gai...
Two ways to iterate the experiment:
- Replicate the entire experiment 1000 times. That is, there will be 1000 independent tosses of the coin. This will lead between 1000 and 2000 awakenings, with expected value of 1500 awakenings.
and
...
- Replicate her awakening-state 1000 times. Because her epistemic state is always the same on an awakening, from her perspective, it could be Monday or Tuesday, it could be heads or tails.
The distinction between 1 and 2 is that, in 2, we are trying to repeatedly sample from the joint probability distributions that she s
This sounds like the continuity argument, but I'm not quite clear on how the embedding is supposed to work, can you clarify? Instead of telling me what the experimenter rightly or wrongly believes to be the case, spell out for me how he behaves.
If the coin comes up Heads, there is a tiny but non-zero chance that the experimenter mixes up Monday and Tuesday.
What does this mean operationally? Is there a nonzero chance, let's call it epsilon or e, that the experimenter will incorrectly behave as if it's Tuesday when it's Monday? I.e., with probability ...
Your argument is, I take it, that these counts of observations are irrelevant, or at best biased.
No, I was just saying that this, lim N-> infinity n1/(n1+n2+n3), is not actually a probability in the sleeping beauty case.>
I maintain that it is. I can guarantee you that it is. What obstacle do you see to accepting that? You've made noises that this is because the counts are correlated, but I haven't seen any argument for this beyond bare assertion. Do you want to claim it is impossible for some reason, or are you just saying you haven't s...
The 1/3 solution makes the assumption that the probability of heads given an awakening is:
lim N-> infinity n1/(n1+n2+n3)
I'd quibble about calling it an assumption. The 1/3 solution notes that this is the ratio of observations upon awakening of heads to the total number of observations, which is one of the problematic facts about the experimental setup. The 1/3 solution assumes that this is relevant to what we should mean by "credence", and makes an argument that this is a justification for the claim that Sleeping Beauty's credence should ...
I don't follow your latest argument against thirders. You claim that the denominator
#(heads & monday) + #(tails & monday) + #(tails & tuesday)
counts events that are not mutually exclusive. I don't see this. They look mutually exclusive to me-- heads is exclusive of tails, and monday is exclusive of tuesday, Could you elaborate this argument? Where does exclusivity fail? Are you saying tails&monday is not distinct from tails&tuesday, or all three overlap, or something else?
You also assert that the denominator is not determined by...
I think the temptation is very strong to notice the distinction between the elemental nature of raw sensory inputs and the cognitive significance they are the bearers of. And this is so, and is useful to do, precisely to the extent that the cognitive significance will vary depending on context and background knowledge, such as light levels, perspective, etc. because those serve as dynamically updated calibrations of cognitive significance. But these calibrations become transparent with use, so that we see, hear and feel vividly and directly in three dime... (read more)