Hmm, thanks. Seems similar to my description above, though as far as I can tell it doesn't deal with my criticisms. It is rather evasive when it comes to the question of what status models have in Bayesian calculations.
I am curious; what is the general LessWrong philosophy about what truth "is"? Personally I so far lean towards accepting an operational subjective Bayesian definition, i.e. the truth of a statement is defined only so far as we agree on some (in principle) operational procedure for determining its truth; that is we have to agree on what observations make it true or false.
For example "it will rain in Melbourne tomorrow" is true if we see it raining in Melbourne tomorrow (trivial, but also means that the truth of the statement doesn't depe...
Lol that is a nice story in that link, but it isn't a Dutch book. The bet in it isn't set up to measure subjective probability either, so I don't really see what the lesson in it is for logical probability.
Say that instead of the digits of pi, we were betting on the contents of some boxes. For concreteness let there be three boxes, one of which contains a prize. Say also that you have looked inside the boxes and know exactly where the prize is. For me, I have some subjective probability P( X_i | I_mine ) that the prize is inside box i. For you, all your s...
That sounds to me more like an argument for needing lower p-values, not higher ones. If there are many confounding factors, you need a higher threshold of evidence for claiming that you are seeing a real effect.
Physicists need low p-values for a different reason, namely that they do very large numbers of statistical tests. If you choose p=0.05 as your threshold then it means that you are going to be claiming a false detection at least one time in twenty (roughly speaking), so if physicists did this they would be claiming false detections every other day and their credibility would plummet like a rock.
Is there any more straightforward way to see the problem? I argued with you about this for a while and I think you convinced me, but it is still a little foggy. If there is a consistency problem, surely this means that we must be vulnerable to Dutch books doesn't it? I.e. they would not seem to be Dutch books to us, with our limited resources, but a superior intelligence would know that they were and would use them to con us out of utility. Do you know of some argument like this?
Very well, then i will wait for the next entry. But i thought the fact that we were explicitly discussing things the robot could not compute made it clear that resources were limited. There is clearly no such thing as logical uncertainty to the magic logic god of the idealised case.
No we aren't, we're discussing a robot with finite resources. I obviously agree that an omnipotent god of logic can skip these problems.
It was your example, not mine. But you made the contradictory postulate that P("wet outside"|"rain")=1 follows from the robots prior knowledge and the probability axioms, and simultaneously that the robot was unable to compute this. To correct this I alter the robots probabilities such that P("wet outside"|"rain")=0.5 until such time as it has obtained a proof that "rain" correlates 100% with "wet outside". Of course the axioms don't determine this; it is part of the robots prior, which is not det...
You haven't been very specific about what you think I'm doing incorrectly so it is kind of hard to figure out what you are objecting to. I corrected your example to what I think it should be so that it satisfies the product rule; where's the problem? How do you propose that the robot can possibly set P("wet outside"|"rain")=1 when it can't do the calculation?
Ok sure, so you can go through my reasoning leaving out the implication symbol, but retaining the dependence on the proof "p", and it all works out the same. The point is only that the robot doesn't know that A->B, therefore it doesn't set P(B|A)=1 either.
You had "Suppose our robot knows that P(wet outside | raining) = 1. And it observes that it's raining, so P(rain)=1. But it's having trouble figuring out whether it's wet outside within its time limit, so it just gives up and says P(wet outside)=0.5. Has it violated the product rule? Yes...
Hmm this does not feel the same as what I am suggesting.
Let me map my scenario onto yours:
A = "raining"
B = "wet outside"
A->B = "It will be wet outside if it is raining"
The robot does not know P("wet outside" | "raining") = 1. It only knows P("wet outside" | "raining", "raining->wet outside") = 1. It observes that it is raining, so we'll condition everything on "raining", taking it as true.
We need some priors. Let P("wet outside") = 0.5. We also need a ...
But it turns out that there is one true probability distribution over mathematical statements, given the axioms. The right distribution is obtained by straightforward application of the product rule - never mind that it takes 4^^^3 steps - and if you deviate from the right distribution that means you violate the product rule at some point.
This does not seem right to me. I feel like you are sneakily trying to condition all of the robots probabilities on mathematical proofs that it does not have a-priori. E.g. consider A, A->B, therefore B. To learn th...
Perhaps, though, you could argue it differently. I have been trying to understand so-called "operational" subjective statical methods recently (as advocated by Frank Lad and his friends), and he is insisting on only calling a thing a [meaningful, I guess] "quantity" when there is some well-defined operational procedure for measuring what it is. Where for him "measuring" does not rely on a model, he is refering to reading numbers off some device or other, I think. I don't quite understand him yet, since it seems to me that the numbers reported by devices all rely on some model or other to define them, but maybe one can argue their way out of this...
Thanks, this seems interesting. It is pretty radical; he is very insistent on the idea that for all 'quantities' about which we want to reason there must some operational procedure we can follow in order to find out what it is. I don't know what this means for the ontological status of physical principles, models, etc, but I can at least see the naive appeal... it makes it hard to understand why a model could ever have the power to predict new things we have never seen before though, like Higgs bosons...
An example of a "true number" is mass. We can measure the mass of a person or a car, and we use these values in engineering all the time. An example of a "fake number" is utility. I've never seen a concrete utility value used anywhere, though I always hear about nice mathematical laws that it must obey.
It is interesting that you choose mass as your prototypical "true" number. You say we can "measure" the mass of a person or car. This is true in the sense that we have a complex physical model of reality, and in one...
Sure, I don't want to suggest we only use the word 'probability' for epistemic probabilities (although the world might be a better place if we did...), only that if we use the word to mean different sorts of probabilities in the same sentence, or even whole body of text, without explicit clarification, then it is just asking for confusion.
Hmm, do you know of any good material to learn more about this? I am actually extremely sympathetic to any attempt to rid model parameters of physical meaning; I mean in an abstract sense I am happy to have degrees of belief about them, but in a prior-elucidation sense I find it extremely difficult to argue about what it is sensible to believe a-priori about parameters, particularly given parameterisation dependence problems.
I am a particle physicist, and a particular problem I have is that parameters in particle physics are not constant; they vary with re...
Hmm, interesting. I will go and learn more deeply what de Finetti was getting at. It is a little confusing... in this simple case ok fine p can be defined in a straightforward way in terms of the predictive distribution, but in more complicated cases this quickly becomes extremely difficult or impossible. For one thing, a single model with a single set of parameters may describe outcomes of vastly different experiments. E.g. consider Newtonian gravity. Ok fine strictly the Newtonian gravity part of the model has to be coupled to various other models to des...
Are you referring to De Finetti's theorem? I can't say I understand your point. Does it relate to the edit I made shortly before your post? i.e. Given a stochastic model with some parameters, you then have degrees of belief about certain outcomes, some of which may seem almost the same thing as the parameters themselves? I still maintain that the two are quite different: parameters characterise probability distributions, and just in certain cases happen to coincide with conditional degrees of belief. In this 'beliefs about beliefs' context, though, it is the parameters we have degrees of belief about, we do not have degrees of belief about the conditional degrees of belief to which said parameters may happen to coincide.
Keynes in his "Treatise on probability" talks a lot about analogies in the sense you use it here, particularly in "part 3: induction and analogy". You might find it interesting.