Ape in the coat

Karma: 1,452

The Joke

Ape in the coat29 Nov 2025 10:51 UTC

32 points

2 comments4 min readLW link

(apeinthecoat102771.substack.com)

Ape in the coat 23 Oct 2025 10:48 UTC
2 points
0
in reply to: Eridu’s comment on: Universal Basic Income and Poverty
The loans add an extra complication it’s an extra variable that affects the economical equilibrium. But this effect is the same for both landlordism and capitalism, so it’s irrelevant for the question of difference between the two.
Meanwhile there is a factor that distinguishes landlordism and capitalism: the sufficiently intense competition to provide good/services at lower cost that doesn’t exist for landlordism, but does exist for capitalists even before we account for loans.
Essentially we have:
Capitalist Competiton Pressure = NL + L
Landlord Competiton Pressure = L
Where NL is the aspect of pressure without the loans and L is aspect of pressure specifically due to loans.
You think landlords are special because land is limited. But capital as a whole is limited at any given time.
So? Don’t you think that being limited at some point in time and being limited in principle is a meaningful distinction?

Ape in the coat 25 Sep 2025 6:14 UTC
2 points
0
in reply to: Eridu’s comment on: Universal Basic Income and Poverty
Loans/investments is a red herring here. Have you read Meditations on Moloch? Consider the example from there:
Suppose there’s a coffee plantation somewhere in Ethiopia that employs Ethiopians to grow coffee beans that get sold to the United States. Maybe it’s locked in a life-and-death struggle with other coffee plantations and want to throw as many values under the bus as it can to pick up a slight advantage.
But it can’t sacrifice quality of coffee produced too much, or else the Americans won’t buy it. And it can’t sacrifice wages or working conditions too much, or else the Ethiopians won’t work there. And in fact, part of its competition-optimization process is finding the best ways to attract workers and customers that it can, as long as it doesn’t cost them too much money. So this is very promising.
But it’s important to remember exactly how fragile this beneficial equilibrium is.
Suppose the coffee plantations discover a toxic pesticide that will increase their yield but make their customers sick. But their customers don’t know about the pesticide, and the government hasn’t caught up to regulating it yet. Now there’s a tiny uncoupling between “selling to Americans” and “satisfying Americans’ values”, and so of course Americans’ values get thrown under the bus.
Or suppose that there’s a baby boom in Ethiopia and suddenly there are five workers competing for each job. Now the company can afford to lower wages and implement cruel working conditions down to whatever the physical limits are. As soon as there’s an uncoupling between “getting Ethiopians to work here” and “satisfying Ethiopian values”, it doesn’t look too good for Ethiopian values either.
Or suppose someone invents a robot that can pick coffee better and cheaper than a human. The company fires all its laborers and throws them onto the street to die. As soon as the utility of the Ethiopians is no longer necessary for profit, all pressure to maintain it disappears.
Or suppose that there is some important value that is neither a value of the employees or the customers. Maybe the coffee plantations are on the habitat of a rare tropical bird that environmentalist groups want to protect. Maybe they’re on the ancestral burial ground of a tribe different from the one the plantation is employing, and they want it respected in some way. Maybe coffee growing contributes to global warming somehow. As long as it’s not a value that will prevent the average American from buying from them or the average Ethiopian from working for them, under the bus it goes.
I know that “capitalists sometimes do bad things” isn’t exactly an original talking point. But I do want to stress how it’s not equivalent to “capitalists are greedy”. I mean, sometimes they are greedy. But other times they’re just in a sufficiently intense competition where anyone who doesn’t do it will be outcompeted and replaced by people who do. Business practices are set by Moloch, no one else has any choice in the matter.
This sufficiently intense competition that pushes everyone to the worst practices just doesn’t exist for landlords.

Ape in the coat 31 Aug 2025 11:36 UTC
10 points
1
on: Female sexual attractiveness seems more egalitarian than people acknowledge
But that amount of effort would be sufficient to elevate most women who have similar ages, similar BMIs, and a face in the top quartile.
That’s just trivially true, isn’t it? Among women who were already pre-selected to have similar faces, ages and BMI’s to movie starts most of them can be made extremely attractive, with the help of right makeup, clothes, context and so on.
But this is not how people talk about female sexual attractiveness! People, especially women, talk about it as if there’s a significant, native difference in supermodels’ broad appeal.
The difference is that some people happen to be part of this group of women with similar faces, ages and BMI’s to movie starts and some do not. There is no contradiction here.
She’s an outlier, but an outlier in the sense that someone who’s 6′2″ is an outlier, not an outlier like Michael Jordan is an outlier.
I’d say, more of an outlier than being 6′2, less of an outlier than Michael Jordan.
The reason women talk like this
Basically, because society as a whole actively conditions women that their looks is the most important thing about them. This includes some of the factors that you’ve mentioned.

Ape in the coat 31 Aug 2025 8:06 UTC
1 point
−1
in reply to: vae’s comment on: Contra Yudkowsky’s Ideal Bayesian
Yes, but such an interpretation falls outside of Yudkowsky’s view, as I understand it
Maybe! But I would expect him to change his view to something like this in case you managed to persuade him that there is some crucial flaw in Bayesianism. While your goal seems to be to propagate the toolbox-view as the only valid approach. So you might as well engage with a stronger version of law-view right now.
On Walker, on that paragraph he is criticizing the specific (common) practice of comparing separate Bayesian models and picking the best (via ratios or errors or some such) when there is uncertainty about the truth instead of appropriately representing this uncertainty about your sampling model in the prior.
Rolling a die is a bit of a nifty example here since it’s the case where you assign a separate probability to each label in the sample space, so that your likelihood is in fact fully general, which is where the idea for a Dirichlet prior comes from in an attempt to generalize this notion of covering all possible models for less trivial problems.
So, suppose that instead of assigning equal probabilities to each label of a die, I consider this as just one of multiple possible models from a set of models with different priors. According to one of them:
P(1) = ¹⁄₂, P(2) = P(3) = P(4)=P(5)=P(6)=1/10
According to another:
P(2) = ¹⁄₂, P(1)=P(3)=P(4)=P(5)=P(6)=1/10
And so on and so forth.
And then I assign equiprobable prior between these models and start collecting experimental data—see how well all of them perform. Do I understand correctly, that Walker considers such approach incoherent?
In which case, I respectfully disagree with him. While it’s true that this approach doesn’t represent our uncertainty about which label of an unknown die will be shown on a roll, it, nevertheless, represents the uncertainty about which bayesian model best approximates the behavior of this particular die. And there is nothing incoherent in modelling the latter kind of uncertainty instead of the former.
And likewise for more complicated settings and models. Whenever we have uncertainty about which model is the best one we can model this uncertainty and get a probabilistic answer to it via bayesian methods. And then get a probabilistic answer according to this model, if we want to.
On Bayesian finite-sample miscalibration, simply pick a prior which is sufficiently far off from the true value and your predictive intervals will be very bad for a long time
But why would the prior, capturing all your information about a setting, be sufficiently far off from the true value, in the first place? This seems to happen mostly when you misuse the bayesian method, by picking some arbitrary prior for no particular reason. Which is a weird complain. Surely we can also misuse frequentist methods in a similar fashion—p-hacking immediately comes to mind, or just ignoring bunch of data points altogether. But what’s the point in talking about this? We are interested in situations when the art fails us, not when we fail the art, aren’t we?
On minimax
Interesting! So is there a agreement among frequentists that probability of an unfair coin about which we know nothing else to land Tails is 1/2? Or is it more like: “Well we have a bunch of tools and here one of them says ¹⁄₂, but we do not have a principled reason to prefer it to other tools regarding the question of what probability is, so the question is still open”.
On your last comment, it seems like a bit of an open question to attribute the existence of practical intuition and reasoning about mathematical constructs like this to a Bayesian prior updating process.
Is it? I though everyone is in agreement that Bayes theorem naturally follows from the axioms of probability theory. In which case the only reason why such reasoning doesn’t follow Bayesian updating procedure is that, somehow, probability theory is not applicable to the reasoning about mathematical constructs in particular, but why would that be true?
Certainly I reason, and I change my mind, but to me personally I see no reason to imagine this was Bayesian in some way (or that those thoughts were expressed in credence-probabilities which I shifted by conditioning on a type of sense-data), nor that I would be ideally doing this instead.
Oh wait, you don’t think that probability theory is applicable to reasoning in general? Surely I’m misunderstanding you here? Could you elaborate on your position here? I feel that this is the most important crux of disagreement.

Ape in the coat 30 Aug 2025 7:48 UTC
1 point
−1
in reply to: vae’s comment on: Contra Yudkowsky’s Ideal Bayesian
On the question of ‘taking the best from both’, that is what Yudkowsky calls a “tool” view
Not necessary. You can have a law-view interpretation of such synthesis where we conceptualize Bayesianism as an imperfect approximation, a special case of the True Law, which should also capture all the good insights of Frequentism.
There is more responsibility on the Bayesian: she gets more out in the form of a posterior distribution on the object of interest. Hence more care needs to be taken in what gets put into the model in the first place. For the posterior to mean anything it must be representing genuine posterior beliefs, solely derived by a combination of the data and prior beliefs via the use of the Bayes theorem. Hence, the prior used must genuinely represent prior beliefs (beliefs without data). If it does not, how can the posterior represent posterior beliefs? So a “prior” that has been selected post data via some check and test from a set of possible “prior” distributions cannot represent genuine prior beliefs. This is obvious, since no one of these “priors” can genuinely represent prior beliefs. The posterior distributions based on such a practice are meaningless.
- Stephen J. Walker (first chapter of Bayesian Nonparametrics)
I’m not sure I see what exactly Walker is arguing here. Could you recreate the substance of the argument using a specific example, the roll of a 6 sided die, for instance?
We don’t know anything more about the die, do not have any data of the previous throws. Is Steven Walker confused where are we getting the equiprobable prior from?
Not only that, but insofar as you ‘want both’ (finite-sample) calibration and coherence, you are called to abandon one or the other—Insofar as there are Bayesian methods that can get you the former, they are not derived by prior distributions that represent your knowledge of the world (if they even exist in general, anyway—not something I know of).
I really don’t see why! As far as I know Bayes Theorem and Law of Large Numbers coexist perfectly. Could you give me some maximally simple example where such discrepancy happens?
On your query about coins, ¹⁄₂ is minimax for the squared error, I believe.
Minimax for the squared error of what? How do you calculate it if you don’t have any access to the information about the previous tosses of the coin, nor know how exactly biased it is? Could you present your reasoning here step by step? Also what is your claim here, in the first place? That I misunderstand Frequentist position on the question and they actually agree with Bayesians here?
that there are only good properties which a method can or can’t obtain.
Hmm… and how do you judge which methods have good properties and which properties are good in the first place? Doesn’t reasoning about this itself requires some initial intuition and accumulated data from previous experience? Therefore essentially satisfying Bayesian structure?

Ape in the coat 29 Aug 2025 7:11 UTC
3 points
0
on: Contra Yudkowsky’s Ideal Bayesian
I always feel that Bayesianism/Frequentism debates are somewhat misguided. Both categories are so vague, consisting of many individual elements, often for weird historical reasons. A lot of vibe based reasoning also involved, like what “smells very Frequentist” or vice versa. The global argument here appears more like a political one instead of being about math or epistemology.
It seem more useful to address specific individual disagreements and in the process develop a framework that deals with whatever problems Bayesianism and Frequentism have, taking all the best from both, instead of scoring points for these two frameworks and comparing which is better. What’s the point in arguing, whether coherence or calibration is more important? Clearly we want both!
A standard example of such specific disagreement is assigning probabilities to unfair coin about which you know nothing else. Here, as far as I know, Frequentism is unable to perform, while Bayesianism has a coherent answer: ¹⁄₂. Bayesianism does look better in this regard but this is beside the point. What is important, is that now we know what answer should the optimal framework produce. Do you know similar specific examples where Frequentism appears superior? If we collect enough of them in both directions, we will be able to conceptualize a strictly superior framework.

Ape in the coat 13 Aug 2025 8:45 UTC
8 points
0
in reply to: Gordon Seidoh Worley’s comment on: Alcohol is so bad for society that you should probably stop drinking
The way you apply “Chesterton’s Fence” is bordering a fully general argument against any change. This is not how it’s supposed to be used. Chesterton’s Fence is an argument against getting rid of things, without having knowledge why they existed in the first place. We do have a good model why alcohol consumption coincided with civilization. Therefore, there is no strong argument of this form that can be made here.
You also seem to confuse voluntary teetotaling with government enforced prohibition. All the bad secondary effects are results of the latter, not the former.

Ape in the coat 13 Aug 2025 8:29 UTC
5 points
0
in reply to: Gordon Seidoh Worley’s comment on: Alcohol is so bad for society that you should probably stop drinking
The argument is to change your personal behaviour in order to modify the global multiagent equilibrium at least to some degree.

Ape in the coat 6 Aug 2025 8:22 UTC
2 points
0
in reply to: Leonard Holloway’s comment on: Doomsday Argument and the False Dilemma of Anthropic Reasoning
Not much. I have initially considered this thread “not worth getting into” as @avturchin’s line of reasoning is based on multiple different small confusions, addressing each of which would be a huge chore and is only tangetially relevant to the topic of the post, in the first place. I agree with this assessment today. But I will present the general outline of what is wrong with it for you and the future readers.
First of all, Gott’s version of DA is different from the version of DA, I’m talking about in this post. Its a different mathematical model, that is based on the number of years humanity exists, instead of number of humans and returns a different estimate for extinction: 97.5% confidence for extinction in the next 8 million years, assuming that humanity existed for 200000 years, regardless of birthrates. Suffice to say, these two version of DA produce different predictions, and by shifting some free parameters in the models we can get even more different predictions still. This is completely expected if DA arguments are wrong.
Likewise Laplace sunrise is yet another mathematical model and a certain interpretation of it produces vaguely similar result to Gott’s version of DA. Assuming LS being applicable, this isn’t really an argument in favor of GDA or by kind of anthropic reasoning. Imagine if the correct answer to a test question is ¹⁄₅₀₀₂, while your reasoning, which makes an extra assumption, produces an answer ¹⁄₅₀₀₀. Clearly, it doesn’t mean that your reasoning is correct, nor justifies the extra assumption.
And then there is a whole different question of applicability of LS to the situation at hand. Which also doesn’t fully capture our knowledge state, but at least it’s less wrong, in a sense, as it doesn’t make the particular mistake which I’m talking about in this post.

Ape in the coat 31 Jul 2025 10:08 UTC
7 points
0
on: My Empathy Is Rarely Kind
Don’t go bullshitting me about how a kind and compassionate life of mediocrity is a “different kind of strength” or some such cope.
There is, in fact, no reason why being compassionate should doom you to a life of mediocrity. A lot of very compassionate people manage to simultaneously be extremely self-critical, even beyond the point where it’s helpful for their productivity.
What is a “cope”, is an idea that you are either nice or brilliant. And you seem to be a victim of it. So in the spirit of tsuyoku naritai, stop coming up with excuses not to learn a valuable skill, deluding yourself into thinking that it somehow going to make you less successful in other domains and go put some effort into acquiring it.
When I try to empathize with that woman
That’s because you are not actually empathizing with the woman. You are empathizing with a man who notices the nail in the head. This is understandable because the point of the video is to make you do exactly that, it frames the situation in this particular manner that makes empathizing with the woman very hard, while empathizing with the man as easy as possible. Essentially you are being manipulated into empathizing with whoever the author of the video wants.
As a practicum of empathy and withstanding this sort of manipulation, try to re-frame the situation in such a way, where it’s the woman who is in the right. And no, just switching genders of the characters won’t do—that’s not the point of the exercise. The point is to come up with a situation in which a complaining character who wants to be listened to, is obviously in the right, while a character who is proposing a solution is obviously in the wrong. Just like in the video it’s obvious that the woman with the nail in the hand is wrong and stupid.

Ape in the coat 26 Jul 2025 5:44 UTC
2 points
0
in reply to: Wei Dai’s comment on: The Absent-Minded Driver
I believe I’ve solved the problem. I’m going to include this in my next post on probability theory fundamentals but here is the gist of it.
The problem is to come up with a general decision algorithm that both works (in the sense of making the right decisions) and (if possible) makes epistemic sense.
The meta-problem here is that people were looking for the answer in the wrong place, searching for a different decision making algorithm while what we actually needed is a satisfying epistemological account. The core crux isn’t in decision theory, but on a previous step—in probability theory.
UDT works but it doesn’t compute or make use of “probability of being at X” so epistemically it doesn’t seem very satisfying.
That should be a clue that “probability of being at X” isn’t, in fact, a thing. That event “I’m at X and not at Y” is ill defined. In other words, that the problem is with our intuition that mistakenly assumes that there should be such an event, and with the lack of a strict epistemological framework that would allow us to answer questions such as “Does this mathematical model fit the setting?” and “Is this event well-defined?”
Here I provide this framework. An event is a conditional statement of a belief updating algorithm, that has to return clear True or False in every iteration of probability experiment, approximating some process to the best of our knowledge—in our case Absent-Minded Driver problem. Statement “I’m at X and not at Y” doesn’t satisfy this condition for Absent-Minded Driver as in some iterations of the experiment the driver will be at both. Therefore it’s not an event, and can not lead to conditionalization.
The event that is well defined in every iteration of the experiment is “I’m at X or Y”. This event has probability 1 which means trivial conditionalization—on its realization credences of the driver do not change. Therefore everything adds up to normality.

PTF 102: Conditionalization and Events

Ape in the coat25 Jul 2025 6:07 UTC

8 points

0 comments8 min readLW link

Ape in the coat 23 Jul 2025 14:20 UTC
2 points
0
in reply to: TAG’s comment on: Zombies! Substance Dualist Zombies?
To show that physicalism isn’t necessarily true, I only need to show there is some plausibility to the existence of intrinsic subjectivity.
I’m not saying dualism is necessarily true, I’m saying physicalism isn’t necessarily true. The one is not a corollary of the other.
Okay, I think we have a long-going misunderstanding here, so let’s try to clear it once and for all.
We are, in fact, both in agreement that physicalism is not necessary true. Likewise, we are in agreement that dualism is also not necessary true.
Now consider these two statements:
1. Weak Zombie Argument: Zombies are conceivable, therefore physicalism is not necessary true
2. Strong Zombie Argument: Zombies are conceivable therefore physicalism is necessary false
I think the confusion that goes on between the two of us, is that when I say “Zombie Argument” I mean the strong one, while when you say “Zombie Argument”, you mean the weak one. If you agree that Strong Zombie Argument is wrong, then there is in fact, no substantial disagreement between the two of us on this matter!
So, are we in agreement here?

Ape in the coat 21 Jul 2025 5:19 UTC
1 point
0
in reply to: JeffJo’s comment on: What’s the Deal with Logical Uncertainty?
Since what I said was that probability theory makes no restrictions on how to assign values, but that you have to make assignments that are reasonable based on things like the Principle of Indifference
That’s not what you’ve said at all. Re-read our comment chain. There is not a single mention of principle of indifference by you there. I’ve specifically brought up an example of a fair coin about which we know nothing else and yet you agreed that any number from 0 to 1 is the right answer to this problem. The first time you’ve mentioned principle of indifference under this post is in an answer to another person, which happened after my alleged misrepresentation of yours.
Now, I’m happy to assume that you’ve meant this all the time and just was unable to communicate your views properly. Please do better next time. But for now let me try to outline your views as far as I understand them, which do appear less ridiculous now:
You believe that probability theory only deals with whether something is a probability space at all—does it fit the axioms or not. And the question which valid probability space is applicable to a given problem based on some knowledge level, doesn’t have a “true” answer. Instead there is a completely separate category of “reasonableness” and some probability spaces are reasonable to a given problem based on the principle of uncertainty, but this is a completely separate domain from probability theory.
Please correct me, what I got wrong and then I hope we will be able to move forward.
You claim that “the probability that the nth digit of pi, in base 10, is ¹⁄₁₀ for each possible digit,” is assigning a non-zero probability to nine false statements, and probability that is less than 1 to one true statement.
No, I don’t. I’m ready to entertain this framework, but then I don’t see any difference compared to an example with empirical uncertainty. Do you see this difference? Do you think there is some fundamental reason why we can say that probability of a coin to be Heads is ¹⁄₂ and yet we can’t say the same about the probability of a particular unknown to us digit of pi to be even?
I am saying that it such probabilities apply only if we do not apply the formula that will determine that digit.
You mean, before we apply the formula, right? Suppose we are going to apply the formula in a moment, we can still say that before we applied it the probability for any digit is ¹⁄₁₀, can we? And after we applied the formula we know which digit it is so it’s 1 for this particular digit and 0 for all the rest.
I claim that the equivalent statement, when I flip a coin but place my hand over it before you see the result, is “the probability that the coin landed Heads is ¹⁄₂, as is the probability that it landed Tails.” And that the truth of these two statements is just as deterministic, if I lift my hand. Or if I looked before I hid the coin. Or if someone has a x-ray that can see it. That the probability in question is about the uncertainty when no method is applied that determine the truth of the statements, even when we know such methods exist.
No disagreement here. Do you agree that the scenarios with logical and empirical uncertainty work exactly the same way?
I’m saying that this is not a question in epistemology
If it’s neither the question of probability theory nor epistemology, what is it a question of? How long are you going to pass the buck of it before engaging with this question? And what is the answer?

Ape in the coat 20 Jul 2025 7:53 UTC
2 points
0
in reply to: JeffJo’s comment on: What’s the Deal with Logical Uncertainty?
Yes, you can make a valid probability space where the probability of Heads is any such number. What you can’t do, is say that probability space accurately represents the system.
Probability Theory only determines the requirements for such a space. It makes no mention of how you should satisfy them. And I assumed you would know the difference, and not try to misrepresent it.
What kind of misrepresentation you are talking about? You have literally just claimed that there is no way to know whether a particular probabilistic model correctly represents the system.
Frankly, I have no idea what is going on with your views. There must be some kind of misunderstanding but to clear it up I’ve came up with the clearest demonstration of the absurdity of your position and you decided to bite the bullet anyway.
Do you really not see that it completely invalidates all epistemology? That if we can only say that a map is valid but never able to claim anything about its soundness towards a particular territory, then the map is useless at navigation?
Because of responses like that last one, and this:
Okay, I misspoke. Because I also assumed you would at least try to understand.
So you came to a wrong conclusion about my views because you misspoke? And you misspoke because you assumed that I will be trying to understand? Okay, look, I think we need to calm down a bit, make a deep breath and one step back. This conversation seems entangled in combatitiveness and it affects your ability to explain yourself clearly and my ability to understand what you are talking about and probably vice versa. I’m happy to assume that you mean something reasonable by the things you are saying and it’s just the issue of miscommunication, if you are ready to give the same courtesy to me. I would be glad to understand your position. I promise to give my best effort to do that. But you also need to make your best effort in explaining yourself. I will probably keep misunderstanding you in all kind of ways, anyway, not because of ill intent, but simply because of inferential distances and some unresolved cruxes of disagreement to which we can’t get until we understand each other.
You need complete knowledge of an assumed (Baysean) or actual (Frequentist) system that can produce different results. But lack at least some knowledge of the one instance of the system in question; that is, one particular result.
Okay I definitely agree with “lack at least some knowledge” part. If we had a perfect map, then there would be no point in approximating the territory with probabilistic models.
And that’s exactly my point. In both logical and empirical uncertainty we do lack some knowledge. Therefore we use the approximate probability theoretic model and get an approximate result. So what is the problem? Where is the difference between logical and empirical uncertainty, that so many people are struggle with?
The system in the SB problem
Please, put the poor Beauty to rest for now. It has nothing to do with the topic of discussion and using it as an example to explain your point is very counterproductive as it’s probably one of the most controversial example between the two of us. Take something more simple like a fair coin toss or a dice roll.
And again, I don’t see how you can first claim that
What you can’t do, is say that probability space accurately represents the system.
But then start talking about a probabilistic description of the system, which I, suppose, you assume to be accurate? Or are you just saying: this is a valid probability space that satisfies the axioms, regardless of any actual process that can happen in the real world. - This I can agree with. It’s just a very weak statement and you seem to behave as if you mean something much stronger.
Because the system is “a digit in {0,1,2,3,4,5,6,7,8,9} is selected in a manner that is completely hidden from us at the moment, even if it could be obtained. The probability refers to what the result of calculating the nth digit could be when we do not apply n to the formula for pi, not when we do.
Well yes, exactly! We don’t know what digit is the one that an algorithm would produce, and we don’t have any reason to expect any particular digit better than chance, therefore we are indifferent between all the digits so ¹⁄₁₀ for every of them. Just as if we were dealing with empirical uncertainty.
If what you are suggesting were true, then the correct answer to “What is a probability of a fair coin toss, about which you know nothing else, to come Heads?”, would be: “It is either 0 or 1, but we can’t tell.
What? No, absolutely not! It would be: we lack any reason to expect any outcome more than another, so we are indifferent between both outcomes so ¹⁄₂ for both of them. The exact same kind of reasoning for both logical and empirical uncertainty.
I’m starting to suspect that we are actually in complete agreement about logical uncertainty and you simply didn’t properly read my opening post and assumed that I have an opposite position to yours for some reason. Could you, please, outline your model of my views on the subject? What do you think my stance on logical uncertainty is?

Ape in the coat 13 Jul 2025 12:28 UTC
4 points
2
in reply to: JeffJo’s comment on: What’s the Deal with Logical Uncertainty?
Well, it is true
Were it true, then the correct answer to “What is a probability of a fair coin toss, about which you know nothing else, to come Heads?”, would be: “Any real number between zero and one”.
And the same would go for any other probability theory problem, turning the whole domain pretty useless.
This is what probability is supposed to be—a measure of your lack of knowledge about a result, not a measure of absolute truth.
I have no idea how you managed to come to the conclusion that I don’t understand that probability is about lack of knowledge.
On the other hand, this distinction:
It is not about what what can be shown, deterministically, to be true when we have perfect knowledge of a system. It is about the different outcomes that could be true when your knowledge is imperfect.
Makes little sense. Things that we consider to be possible while our knowledge is imperfect can very well be shown to be either true or false, when our knowledge is perfect.
That’s kind of the point of the third axiom. After all the knowledge is acquired and all the conditionalizations are done, the resulting sample space consists of a single outcome and its probability is one, which, if you want to be dramatic about, you can call “absolute truth”.
Anyway, back to the actual question.
Consider your “opaque box” problem. You are not “assigning non-zero probability to a false statement,” you are assigning it to a possibility that could be true based on the incomplete knowledge you have.
And how is it different from the initial example with googolth digit of pi? Why can’t we say that we are not assigning non-zero probability to a false statement but to a possibility that could be true based on the incomplete knowledge that we have?

Ape in the coat 8 Jul 2025 2:22 UTC
3 points
1
on: Sleeping Beauty and the Forever Muffin
Good overall, but you are making a serious mistake: confusing single halfism, with double halfism.
SSA is a generalized single halfism reasoning. It’s very obviously wrong in Sleeping Beauty as it implies that if the Beauty knows that she is awakened on Monday, she expects that there is ³⁄₂ chance that the coin is Heads. Generally if you actually do the math, SSA can’t produce correct betting odds for Sleeping Beauty problem. It’s, in a sense, even worse than SIA for SB, but ultimately both of them are based on the flawed and unjustified framework of “centred possible worlds”.
Double halfism is the correct approach and has no problem with correct betting odds. All your argument in favor of halfism are, in fact, double halfer reasoning. However you mistakenly credit them to SSA.

Ape in the coat 5 Jul 2025 12:07 UTC
3 points
0
in reply to: TAG’s comment on: Probability Theory Fundamentals 102: Territory that Probability is in the Map of
So if a the territory is branching , the map.should, too.
Of course not. The territory can be made of rocks and dirt, but it doesn’t mean that the map also has to be.
That’s a disadvantage, because the same map can’t represent any territory.
I’m not saying that it represents every territory. I’m saying that it represents a more general class of territories without loosing any advantages of the framework of possible worlds.
In the post I’ve even specifically outlined what are the territories that my framework can represent:
“So the territory that probability is in the map of is...”
All the processes in the real world, such that my knowledge state about them works like a weighted sample of n elements.
( there may be an ontological neutral way of doing probability calculations, but it’s not a map, for that reason....more of a tool)
Now it seems that you are finally starting to get it. It is a map, of course, but that’s really beside the point. If you want to put it in a separate category of “tools” (as if a map is not a tool?) then whatever suits your needs. Yes, what I’m doing is providing an ontologically neutral framework for probability theory that works better than a framework of possible worlds.
The problem is the implied ontology.
Once again, no ontology is actually implied. It’s absolutely trivial to describe behavior of indetermenistic processes in terms of probability experiment. I’m concentrating on deterministic cases simply because they are trickier.
If there is a referent for it in the territory, it is entirely reasonable to say “possible worlds exist”.
I’m not saying that it’s unreasonable to say, conditionally on using this term. I’m saying that usage of the term, to begin with, is a bad idea as it keeps leading people doing probability theory astray.
Is it really a win to admit the substance of existing possible worlds, but under a different name?
When your goal is to separate the substance from the harmful rubbish, it absolutely is a win.

Ape in the coat 5 Jul 2025 7:07 UTC
2 points
0
in reply to: TAG’s comment on: Probability Theory Fundamentals 102: Territory that Probability is in the Map of
You keep missing the point. It’s as if you haven’t even read the post and simply noticed a couple of key words.
The map that corresponds to a deterministically branching multiversal has possible worlds.
Some do.
I’m proposing a better map, capable to talk about knowledge states and uncertainty, in any circumstances, having all the advantages of maps using the concept of possible worlds, without their weak point.
If you think that the framework of probability experiment that I’m outlining in the post fails to account for something that the frameworks of possible worlds manage to account for—please specify it. Bring up a setting in which you think my framework fails to describe the territory.
Refusing to.ever talk about possible worlds is dangerous, because they might exist (they do in MWI ) and they might be useful otherwise.
Possible world is a term from a map. There may be a referent for it in a territory, true. But it doesn’t mean that we have to use this particular term to talk about this referent. We may have a better term, instead.

Ape in the coat

The Joke

PTF 102: Con­di­tion­al­iza­tion and Events

PTF 102: Conditionalization and Events