Probability is in the Mind

Reductionism 101

Monsterwithgirl_2

Followup toThe Mind Projection Fallacy

Yesterday I spoke of the Mind Projection Fallacy, giving the example of the alien monster who carries off a girl in a torn dress for intended ravishing—a mistake which I imputed to the artist's tendency to think that a woman's sexiness is a property of the woman herself, woman.sexiness, rather than something that exists in the mind of an observer, and probably wouldn't exist in an alien mind.

The term "Mind Projection Fallacy" was coined by the late great Bayesian Master, E. T. Jaynes, as part of his long and hard-fought battle against the accursèd frequentists.  Jaynes was of the opinion that probabilities were in the mind, not in the environment—that probabilities express ignorance, states of partial information; and if I am ignorant of a phenomenon, that is a fact about my state of mind, not a fact about the phenomenon.

I cannot do justice to this ancient war in a few words—but the classic example of the argument runs thus:

You have a coin.
The coin is biased.
You don't know which way it's biased or how much it's biased.  Someone just told you, "The coin is biased" and that's all they said.
This is all the information you have, and the only information you have.

You draw the coin forth, flip it, and slap it down.

Now—before you remove your hand and look at the result—are you willing to say that you assign a 0.5 probability to the coin having come up heads?

The frequentist says, "No.  Saying 'probability 0.5' means that the coin has an inherent propensity to come up heads as often as tails, so that if we flipped the coin infinitely many times, the ratio of heads to tails would approach 1:1.  But we know that the coin is biased, so it can have any probability of coming up heads except 0.5."

The Bayesian says, "Uncertainty exists in the map, not in the territory.  In the real world, the coin has either come up heads, or come up tails.  Any talk of 'probability' must refer to the information that I have about the coin—my state of partial ignorance and partial knowledge—not just the coin itself.  Furthermore, I have all sorts of theorems showing that if I don't treat my partial knowledge a certain way, I'll make stupid bets.  If I've got to plan, I'll plan for a 50/50 state of uncertainty, where I don't weigh outcomes conditional on heads any more heavily in my mind than outcomes conditional on tails.  You can call that number whatever you like, but it has to obey the probability laws on pain of stupidity.  So I don't have the slightest hesitation about calling my outcome-weighting a probability."

I side with the Bayesians.  You may have noticed that about me.

Even before a fair coin is tossed, the notion that it has an inherent 50% probability of coming up heads may be just plain wrong.  Maybe you're holding the coin in such a way that it's just about guaranteed to come up heads, or tails, given the force at which you flip it, and the air currents around you.  But, if you don't know which way the coin is biased on this one occasion, so what?

I believe there was a lawsuit where someone alleged that the draft lottery was unfair, because the slips with names on them were not being mixed thoroughly enough; and the judge replied, "To whom is it unfair?"

To make the coinflip experiment repeatable, as frequentists are wont to demand, we could build an automated coinflipper, and verify that the results were 50% heads and 50% tails.  But maybe a robot with extra-sensitive eyes and a good grasp of physics, watching the autoflipper prepare to flip, could predict the coin's fall in advance—not with certainty, but with 90% accuracy.  Then what would the real probability be?

There is no "real probability".  The robot has one state of partial information.  You have a different state of partial information.  The coin itself has no mind, and doesn't assign a probability to anything; it just flips into the air, rotates a few times, bounces off some air molecules, and lands either heads or tails.

So that is the Bayesian view of things, and I would now like to point out a couple of classic brainteasers that derive their brain-teasing ability from the tendency to think of probabilities as inherent properties of objects.

Let's take the old classic:  You meet a mathematician on the street, and she happens to mention that she has given birth to two children on two separate occasions.  You ask:  "Is at least one of your children a boy?"  The mathematician says, "Yes, he is."

What is the probability that she has two boys?  If you assume that the prior probability of a child being a boy is 1/2, then the probability that she has two boys, on the information given, is 1/3.  The prior probabilities were:  1/4 two boys, 1/2 one boy one girl, 1/4 two girls.  The mathematician's "Yes" response has probability ~1 in the first two cases, and probability ~0 in the third.  Renormalizing leaves us with a 1/3 probability of two boys, and a 2/3 probability of one boy one girl.

But suppose that instead you had asked, "Is your eldest child a boy?" and the mathematician had answered "Yes."  Then the probability of the mathematician having two boys would be 1/2.  Since the eldest child is a boy, and the younger child can be anything it pleases.

Likewise if you'd asked "Is your youngest child a boy?"  The probability of their being both boys would, again, be 1/2.

Now, if at least one child is a boy, it must be either the oldest child who is a boy, or the youngest child who is a boy.  So how can the answer in the first case be different from the answer in the latter two?

Or here's a very similar problem:  Let's say I have four cards, the ace of hearts, the ace of spades, the two of hearts, and the two of spades.  I draw two cards at random.  You ask me, "Are you holding at least one ace?" and I reply "Yes."  What is the probability that I am holding a pair of aces?  It is 1/5.  There are six possible combinations of two cards, with equal prior probability, and you have just eliminated the possibility that I am holding a pair of twos.  Of the five remaining combinations, only one combination is a pair of aces.  So 1/5.

Now suppose that instead you asked me, "Are you holding the ace of spades?"  If I reply "Yes", the probability that the other card is the ace of hearts is 1/3.  (You know I'm holding the ace of spades, and there are three possibilities for the other card, only one of which is the ace of hearts.)  Likewise, if you ask me "Are you holding the ace of hearts?" and I reply "Yes", the probability I'm holding a pair of aces is 1/3.

But then how can it be that if you ask me, "Are you holding at least one ace?" and I say "Yes", the probability I have a pair is 1/5?  Either I must be holding the ace of spades or the ace of hearts, as you know; and either way, the probability that I'm holding a pair of aces is 1/3.

How can this be?  Have I miscalculated one or more of these probabilities?

If you want to figure it out for yourself, do so now, because I'm about to reveal...

That all stated calculations are correct.

As for the paradox, there isn't one.  The appearance of paradox comes from thinking that the probabilities must be properties of the cards themselves.  The ace I'm holding has to be either hearts or spades; but that doesn't mean that your knowledge about my cards must be the same as if you knew I was holding hearts, or knew I was holding spades.

It may help to think of Bayes's Theorem:

P(H|E) = P(E|H)P(H) / P(E)

That last term, where you divide by P(E), is the part where you throw out all the possibilities that have been eliminated, and renormalize your probabilities over what remains.

Now let's say that you ask me, "Are you holding at least one ace?"  Before I answer, your probability that I say "Yes" should be 5/6.

But if you ask me "Are you holding the ace of spades?", your prior probability that I say "Yes" is just 1/2.

So right away you can see that you're learning something very different in the two cases.  You're going to be eliminating some different possibilities, and renormalizing using a different P(E).  If you learn two different items of evidence, you shouldn't be surprised at ending up in two different states of partial information.

Similarly, if I ask the mathematician, "Is at least one of your two children a boy?" I expect to hear "Yes" with probability 3/4, but if I ask "Is your eldest child a boy?" I expect to hear "Yes" with probability 1/2.  So it shouldn't be surprising that I end up in a different state of partial knowledge, depending on which of the two questions I ask.

The only reason for seeing a "paradox" is thinking as though the probability of holding a pair of aces is a property of cards that have at least one ace, or a property of cards that happen to contain the ace of spades.  In which case, it would be paradoxical for card-sets containing at least one ace to have an inherent pair-probability of 1/5, while card-sets containing the ace of spades had an inherent pair-probability of 1/3, and card-sets containing the ace of hearts had an inherent pair-probability of 1/3.

Similarly, if you think a 1/3 probability of being both boys is an inherent property of child-sets that include at least one boy, then that is not consistent with child-sets of which the eldest is male having an inherent probability of 1/2 of being both boys, and child-sets of which the youngest is male having an inherent 1/2 probability of being both boys.  It would be like saying, "All green apples weigh a pound, and all red apples weigh a pound, and all apples that are green or red weigh half a pound."

That's what happens when you start thinking as if probabilities are in things, rather than probabilities being states of partial information about things.

Probabilities express uncertainty, and it is only agents who can be uncertain.  A blank map does not correspond to a blank territory.  Ignorance is in the mind.

 

Part of the sequence Reductionism

Next post: "The Quotation Is Not the Referent"

Previous post: "Mind Projection Fallacy"

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Here is another example me, my dad and my brother came up with when we were discussing probability.

Suppose there are 4 card, an ace and 3 kings. They are shuffled and placed face side down. I didn't look at the cards, my dad looked at the first card, my brother looked at the first and second cards. What is the probability of the ace being one of the last 2 cards. For me: 1/2 For my dad: If he saw the ace it is 0, otherwise 2/3. For my brother: If he saw the ace it is 0, otherwise 1.

How can there be different probabilities of the same event? It is because probability is something in the mind calculated because of imperfect knowledge. It is not a property of reality. Reality will take only a single path. We just don't know what that path is. It is pointless to ask for "the real likelihood" of an event. The likelihood depends on how much information you have. If you had all the information, the likelihood of the event would be 100% or 0%.

So therefore a person with perfect knowledge would not need probability. Is this another interpretation of "God does not play dice?" :-)

I think this is the only interpretation of "God does not play dice."

At least in its famous context, I always interpreted that quote as a metaphorical statement of aesthetic preference for a deterministic over a stochastic world, rather than an actual statement about the behavior of a hypothetical omniscient being. A lot of bullshit's been spilled on Einstein's religious preferences, but whatever the truth I'd be very surprised if he conditioned his response to a scientific question on something that speculative.

This is more or less what I was saying, but left (perhaps too) much of it implicit.

If there were an entity with perfect knowledge of the present ("God"), they would have perfect knowledge of the future, and thus "not need probability", iff the universe is deterministic. (If there is an entity with perfect knowledge of the future of a nondeterministic reality, we have described our "reality" too narrowly - include that entity and it is necessarily deterministic or the perfect knowledge isn't).

GBM:

Q: What is the probability for a pseudo-random number generator to generate a specific number as his next output?

A: 1 or 0 because you can actually calculate the next number if you have the available information.

Q: What probability do you assign to a specific number as being it's next output if you don't have the information to calculate it?

Replace pseudo-random number generator with dice and repeat.

Even more important, I think, is the realization that, to decide how much you're willing to bet on a specific outcome, all of the following are essentially the same:

  • you do have the information to calculate it but haven't calculated it yet
  • you don't have the information to calculate it but know how to obtain such information.
  • you don't have the information to calculate it

The bottom line is that you don't know what the next value will be, and that's the only thing that matters.

Constant: The competent frequentist would presumably not be befuddled by these supposed paradoxes.

Not the last two paradoxes, no. But the first case given, the biased coin whose bias is not known, is indeed a classic example of the difference between Bayesians and frequentists. The frequentist says:

"The coin's bias is not a random variable! It's a fixed fact! If you repeat the experiment, it won't come out to a 0.5 long-run frequency of heads!" (Likewise when the fact to be determined is the speed of light, or whatever.) "If you flip the coin 10 times, I can make a statement about the probability that the observed ratio will be within some given distance of the inherent propensity, but to say that the coin has a 50% probability of turning up heads on the first occasion is nonsense - that's just not the real probability, which is unknown."

According to the frequentist, apparently there is no rational way to manage your uncertainty about a single flip of a coin of unknown bias, since whatever you do, someone else will be able to criticize your belief as "subjective" - such a devastating criticism that you may as well, um, flip a coin. Or consult a magic 8-ball.

Sudeep: If quantum mechanics is true, then ignorance/uncertainty is a part of nature and not just something that agents have.

A common misconception - Jaynes railed against that idea too, and he wasn't even equipped with the modern understanding of decoherence. In quantum mechanics, it's an objective fact that the blobs of amplitude making up reality sometimes split in two, and you can't predict what "you" will see, when that happens, because it is an objective fact that different versions of you will see different things. But all this is completely mechanical, causal, and deterministic - the splitting of observers just introduces an element of anthropic pseudo-uncertainty, if you happen to be one of those observers. The splitting is not inherently related to the act of measurement by a conscious agent, or any kind of agent; it happens just as much when a system is "measured" by a photon bouncing off and interacting with a rock.

There are other interpretations of quantum mechanics, but they don't make any sense. Making this fully clear will require more prerequisite posts first, though.

Eliezer:

"The coin's bias is not a random variable! It's a fixed fact! If you repeat the experiment, it won't come out to a 0.5 long-run frequency of heads!"

You're repeating the wrong experiment.

The correct experiment for a frequentist to repeat is one where a coin is chosen from a pool of biased coins, and tossed once. By repeating that experiment, you learn something about the average bias in the pool of coins. For a symmetrically biased pool, the frequency of heads would approach 0.5.

So your original premise is wrong. A frequentist approach requires a series of trials of the correct experiment. Neither the frequentist nor the Bayesian can rationally evaluate unknown probabilities. A better way to say that might be, "In my view, it's okay for both frequentists and Bayesians to say "I don't know.""

I think EY's example here should actually should be targeted at the probability as propensity theory of Von Mises (Richard, not Ludwig), not the frequentist theory, although even frequentists often conflate the two.

The probability for you is not some inherent propensity of the physical situation, because the coin will flip depending on how it is weighted and how hard it is flip. The randomness isn't in the physical situation, but in our limited knowledge of the physical situation.

The argument against frequentist thinking is that we're not interested in a long term frequency of an experiment. We want to know how to bet now. If you're only going to talk about long term frequencies of repeatable experiments, you're not that useful when I'm facing one con man with a biased coin.

That singular event is what it is. If you're going to argue that you have to find the right class of events in your head to sample from, you're already halfway down the road to bayesianism. Now you just have to notice that the class of events is different for the con man than it is for you, because of your differing states of knowledge, you'll make it all the way there.

Notice how you thought up a symmetrically biased pool. Where did that pool come from? Aren't you really just injecting a prior on the physical characteristics into your frequentist analysis?

If you push frequentism past the usual frequentist limitations (physical propensity, repeated experiments), you eventually recreate bayesianism. "Inside every Non-bayesian, there is a bayesian struggling to get out".

I always found it really strange that EY believes in Bayesianism when it comes to probability theory but many worlds when it comes to quantum physics. Mathematically, probability theory and quantum physics are close analogues (of which quantum statistical physics is the common generalisation), and this extends to their interpretations. (This doesn't apply to those interpretations of quantum physics that rely on a distinction between classical and quantum worlds, such as the Copenhagen interpretation, but I agree with EY that these don't ultimately make any sense.) There is a many-worlds interpretation of probability theory, and there is a Bayesian interpretation of quantum physics (to which I subscribe).

I need to write a post about this some time.

There is a many-worlds interpretation of probability theory, and there is a Bayesian interpretation of quantum physics (to which I subscribe).

Both of these are false. Consider the trillionth binary digit of pi. I do not know what it is, so I will accept bets where the payoff is greater than the loss, but not vice versa. However, there is obviously no other world where the trillionth binary digit of pi has a different value.

The latter is, if I understand you correctly, also wrong. I think that you are saying that there are 'real' values of position, momentum, spin, etc., but that quantum mechanics only describes our knowledge about them. This would be a hidden variable theory. There are very many constraints imposed by experiment on what hidden variable theories are possible, and all of the proposed ones are far more complex than MWI, making it very unlikely that any such theory will turn out to be true.

I think that you are saying that there are 'real' values of position, momentum, spin, etc., but that quantum mechanics only describes our knowledge about them.

I am saying that the wave function (to be specific) describes one's knowledge about position, momentum, spin, etc., but I make no claim that these have any ‘real' values.

In the absence of a real post, here are some links:

By the way, you seem to have got this, but I'll say it anyway for the benefit of any other readers, since it's short and sums up the idea: The wave function exists in the map, not in the territory.

The wave function exists in the map, not in the territory.

Please explain how you know this?

ETA: Also, whatever does exist in the territory, it has to generate subjective experiences, right? It seems possible that a wave function could do that, so saying that "the wave function exists in the territory" is potentially a step towards explaining our subjective experiences, which seems like should be the ultimate goal of any "interpretation". If, under the all-Bayesian interpretation, it's hard to say what exists in the territory besides that the wave function doesn't exist in the territory, then I'm having trouble seeing how it constitutes progress towards that ultimate goal.

I have not read the latter link yet, though I intend to.

I am saying that the wave function (to be specific) describes one's knowledge about position, momentum, spin, etc., but I make no claim that these have any ‘real' values.

What do you have knowledge of then? Or is there some concept that could be described as having knowledge of something without that thing having an actual value?

From Baez:

Probability theory is the special case of quantum mechanics in which ones algebra of observables is commutative.

This is horribly misleading. Bayesian probability can be applied perfectly well in a universe that obeys MWI while being kept completely separate mathematically from the quantum mechanical uncertainty.

Probability theory is the special case of quantum mechanics in which ones algebra of observables is commutative.

This is horribly misleading. Bayesian probability can be applied perfectly well in a universe that obeys MWI while being kept completely separate mathematically from the quantum mechanical uncertainty.

As a mathematical statement, what Baez says is certainly correct (at least for some reasonable mathematical formalisations of ‘probability theory’ and ‘quantum mechanics’). Note that Baez is specifically discussing quantum statistical mechanics (which I don't think he makes clear); non-statistical quantum mechanics is a different special case which (barring trivialities) is completely disjoint from probability theory.

Of course, the statement can still be misleading; as you note, it's perfectly possible to interpret quantum statistical physics by tacking Bayesian probability on top of a many-worlds interpretation of non-statistical quantum mechanics. That is, it's possible but (I argue) unwise; because if you do this, then your beliefs do not pay rent!

The classic example is a spin-1/2 particle that you believe to be spin-up with 50% probability and spin-down with 50% probability. (I mean probability here, not a superposition.) An alternative map is that you believe that the particle is spin-right with 50% probability and spin-left with 50% probability. (Now superposition does play a part, as spin-right and spin-left are both equally weighted superpositions of spin-up and spin-down, but with opposite relative phases.) From the Bayesian-probability-tacked-onto-MWI point of view, these are two very different maps that describe incompatible territories. Yet no possible observation can ever distinguish these! Specifically, if you measure the spin of the particle along any axis, both maps predict that you will measure the spin to be in one direction with 50% probability and in the other direction with 50% probability. (The wavefunctions give Born probabilities for the observations, which are then weighted according to your Bayesian probabilities for the wavefunctions, giving the result of 50% every time.)

In statistical mechanics as it is practised, no distinction is made between these two maps. (And since the distinction pays no rent in terms of predictions, I argue that no distinction should be made.) They are both described by the same ‘density matrix’; this is a generalisation of the notion of quantum state as a wave vector. (Specifically, the unit vectors up to phase in the Hilbert space describe the pure states of the system, which are only a degenerate case of the mixed states described by the density matrices.) A lot of the language of statistical mechanics is frequentist-influenced talk about ‘ensembles’, but if you just reinterpret all of this consistently in a Bayesian way, then the practice of statistical mechanics gives you the Bayesian interpretation.

I am saying that the wave function (to be specific) describes one's knowledge about position, momentum, spin, etc., but I make no claim that these have any ‘real' values.

What do you have knowledge of then? Or is there some concept that could be described as having knowledge of something without that thing having an actual value?

This is the weak point in the Bayesian interpretation of quantum mechanics. I find it very analogous to the problem of interpreting the Born probabilities in MWI. Eliezer cannot yet clearly answer these questions that he poses:

What are the Born probabilities, probabilities of? Here's the map - where's the territory?

And neither can I (at least, not in a way that would satisfy him). In the all-Bayesian interpretation, the Born probabilities are simply Bayesian probabilities, so there's no special problems about them; but as you point out, it's still hard to say what the territory is like.

My best answer is simply what you suggest, that our maps of the universe assign probabilities to various possible values of things that do not (necessarily) have any actual values. This may seem like a counterintuitive thing to do, but it works, and we have no other way of making a map.

By the way, I've thought of a couple more references:

Baez (1993) is where I really learnt quantum statistical mechanics (despite having earlier taken a course in it), and my first (subtle) introduction to the Bayesian interpretation (not made explicit here). Note the talk about the ‘post-Everett school’, and recall that Everett is credited with founding the many-worlds interpretation (although he avoided the term ‘MWI’). The Bayesian interpretation could have been understood in the 1930s (and I have heard it argued, albeit unconvincingly, that it is what Bohr really meant all along), but it's really best understood in light of the modern understanding of decoherence that Everett started. We all-Bayesians are united with the many-worlders (and the Bohmians) in decrying the mystical separation of the universe into ‘quantum’ and ‘classical’ worlds and the reality of the ‘collapse of the wavefunction’. (That is, we do believe in the collapse of the wavefunction, but not in the territory; for us, it is simply the process of updating the map on the basis of new information, that is the application of a suitably generalised Bayes's Theorem.) We just think that the many-worlders have some unnecessary ontological baggage (like the Bohmians, but to a lesser degree).

Bartels (1998) is my first attempt to explain the Bayesian interpretation (on Usenet), albeit not a very good one. It's overly mathematical (and poorly so, since W*-algebras make a better mathematical foundation than C*-algebras). But it does include things that I haven't said here, (including mathematical details that you might happen to want). Still (even for the mathematics), if you read only one, read Baez.

Edit: I edited to use the word ‘world’ only in the technical sense of an interpretation.

Who else thinks we should Taboo "probability", and replace it two terms for objective and subjective quantities, say "frequency" and "uncertainty"?

The frequency of an event depends on how narrowly the initial conditions are defined. If an atomically identical coin flip is repeated, obviously the frequency of heads will be either 1 or 0 (modulo a tiny quantum uncertainty).

I think that we should follow Jaynes and insist upon 'probability' as the name of the subjective entity. But so-called objective probability should be called 'propensity'. Frequency is the term for describing actual data. Propensity is objectively expected frequency. Probability is subjectively expected frequency. That is the way I would vote.

Thinking of probabilities as levels of uncertainty became very obvious to me when thinking about the Monty Hall problem. After the host has revealed that one of the three doors has a booby prize behind it, you're left with two doors, with a good prize behind one of them.

If someone walks into the room at that stage, and you tell them that there's a good prize behind one door and a booby prize behind another, they will say that it's a 50/50 chance of selecting the door with the prize behind it. They're right for themselves, however the person who had been in the room originally and selected a door knows more and therefore can assign different probabilities - i.e. 1/3 for the door they'd selected and 2/3 for the other door.

If you thought that the probabilites were 'out there' rather than descriptions of the state of knowledge of the individuals, you'd be very confused about how the probability of choosing correctly could at the same time be 2/3 and 1/2.

Considering the Monty Hall problem as a way for a part of the information in the hosts head to be communicated to the contestant becomes the most natural way of thinking about it.

Stephen R. Diamond, there are two distinct things in play here: (i) an assessment of the plausibility of certain statements conditional on some background knowledge; and (ii) the relative frequency of outcomes of trials in a counterfactual world in which the number of trials is very large. You've declared that probability can't be (i) because it's (ii) -- actually, the Kolmogorov axioms apply to both. Justification for using the word "probability" to refer to things of type (i) can be found in the first two chapters of this book. I personally call things of type (i) "probabilities" and things of type (ii) "relative frequencies"; the key is to recognize that they need different names.

On your further critiques:
(1) Eliezer is a determinist; see the quantum physics sequence.
(2) True. A logical argument is only as reliable as its premises, and every method for learning from empirical information is only as reliable as its inductive bias. Unfortunately, every extant practical method of learning has an inductive bias, and the no free lunch theorems give reason to believe that this is a permanent state of affairs.

I'm not sure what you mean in your last sentence...

So, I've been on this site for awhile. When I first came here, I had never had a formal introduction to Bayes' theorem, but it sounded a lot like ideas that I had independently worked out in my high school and college days (I was something of an amateur mathematician and game theorist).

A few days ago I was reading through one of your articles - I don't remember which one - and it suddenly struck me that I may not actually understand priors as well as I think I do.

After re-reading some fo the series, and then working through the math, I'm now reasonably convinced that I don't properly understand priors at all - at least, not intuitively, which seems to be an important aspect for actually using them.

I have a few weird questions that I'm hoping someone can answer, that will help point me back towards the correct quadrant of domain space. I'll start with a single question, and then see if I can claw my way towards understanding from there based on the answers:

Imagine there is a rational, Bayesian AI named B9 which has been programmed to visually identify and manipulate geometric objects. B9's favorite object is a blue ball, but B9 has no idea that it is blue: B9 sees the world through a black and white camera, and has always seen the world through a black and white camera. Until now, B9 has never heard of "colors" - no one has mentioned "colors" to B9, and B9 has certainly never experienced them. Today, unbeknownst to B9, B9's creator is going to upgrade its camera to a full-color system, and see how long it takes B9 to adapt to the new inputs.

The camera gets switched in 5 seconds. Before the camera gets switched, what prior probability does B9 assign to the possibility that its favorite ball is blue?

Very low, because B9 has to hypothesize a causal framework involving colors without any way of observing anything but quantitatively varying luminosities. In other words, they must guess that they're looking at the average of three variables instead of at one variable. This may sound simple but there are many other hypotheses that could also be true, like two variables, four variables, or most likely of all, one variable. B9 will be surprised. This is right and proper. Most physics theories you make up with no evidence behind them will be wrong.

I think I'm confused. We're talking about something that's never even heard of colors, so there shouldn't be anything in the mind of the robot related to "blue" in any way. This ought to be like the prior probability from your perspective that zorgumphs are wogle. Now that I've said the words, I suppose there's some very low probability that zorgumphs are wogle, since there's a probability that "zorgumph" refers to "cats" and "wogle" to "furry". But when you didn't even have those words in your head anywhere, how could there have been a prior? How could B9's prior be "very low" instead of "nonexistent"?

Eliezer seems to be substituting the actual meaning of "blue". Now, if we present the AI with the English statement and ask it to assign a probability...my first impulse is to say it should use a complexity/simplicity prior based on length. This might actually be correct, if shorter message-length corresponds to greater frequency of use. (ETA that you might not be able to distinguish words within the sentence, if faced with a claim in a totally alien language.)

"Renormalizing leaves us with a 1/3 probability of two boys, and a 2/3 probability of one boy one girl." help me with this one, i'm n00b. If one of the kids is known to be a boy (given information), then doesn't the other one has 50/50 chances to be either a boy or a girl? And then having 50/50 chances for the couple of kids to be either a pair of boys or one boy one girl?

I think there's still room for a concept of objective probability -- you'd define it as anything that obeys David Lewis's "Principal Principle" which this page tries to explain (with respect to some natural distinction between "admissible" and "inadmissible" information).

I'm not sure the many-worlds interpretation fully eliminates the issue of quantum probability as part of objective reality. You can call it "anthropic pseudo-uncertainty" when you get split and find that your instances face different outcomes. But what determines the probability you will see those various outcomes? Just your state of knowledge? No, theory says it is an objective element of reality, the amplitude of the various elements of the quantum wave function. This means that probability, or at least its close cousin amplitude, is indeed an element of reality and is more than just a representation of your state of knowledge.

For aficionados of interpretations of QM, this relates to an old debate, whether the so-called "Born rule" can be derived from the MWI. Various arguments have been offered for this, including one by Robin, and some have claimed that these now work so well that the argument is settled. However I don't think the larger physics/philosophy community is convinced.

If I'm being asked to accept or reject a number meant to correspond to the calculated or measured likelihood of heads coming up, and I trust the information about it being biased, then the only correct move is to reject the 0.5 probability.

Alas, no. Here's the deal: implicit in all the coin toss toy problems is the idea that the observations may be modeled as exchangeable. It really really helps to have a grasp on what the math looks like when we assume exchangeability.

In models where (infinite) exchangeability is assumed, the concept of long-run frequency can be sensibly defined. (Long-run frequency may or may not be a cogent concept in models without exchangeability.) The probability of heads in any one toss is the expectation of a probability density function (pdf) which encodes our knowledge about the long run frequency. (Roughly. There are some technical conditions for the existence of a pdf that I'm ignoring.)

Conrad, your idea that 0.5 is not an allowable probability is almost correct. In fact, the correct expression of this idea is that the pdf of the long-run frequency must be equal to zero at 0.5. But! -- its values in the neighborhood of 0.5 are not constrained, so the pdf may have a removable singularity.

Suppose our information about bias in favour of heads is equivalent to our information about bias in favour of tail. Our pdf for the long-run frequency will be symmetrical about 0.5 and its expectation (which is the probability in any single toss) must also be 0.5. It is quite possible for an expectation to take a value which has zero probability density. We can refuse to believe that the long-run frequency will converge to exactly 0.5 while simultaneously holding a probability of 0.5 for any specific single toss in isolation.

Silas: My post wasn't meant to be "shockingly unintuitive", it was meant to illustrate Eliezer's point that probability is in the mind and not out there in reality in a ridiculously obvious way.

Am I somehow talking about something entirely different than what Eliezer was talking about? Or should I complexificationafize my vocabulary to seem more academic? English isn't my first language after all.