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This is a deeply confused set of arguments. There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and

This is a deeply confused set of arguments. There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". While what we ultimately care about is the territory, which is fixed, we only have our imperfect map of the world with which to model it. There is nothing inherently "probabilistic" about a shuffled deck of cards - the cards have a specific configuration; the uncertainy is only present in the observer(s). Predicting the next draw from a deck of cards is subject to "out of context" model violations the same way that any other prediction over future world states is. The proposed escape hatch seems to be to dodge the question, but of course in reality you are not agnostic over all possible future events that haven't been ruled out by being "bad explanations". You can say, "well, don't make predictions with numbers on them", but by acting in the world you are making predictions with numbers on them all the time! Bayesian epistemology doesn't claim to be perfect, it claims to be the least inaccurate way of making those predictions and updating on new evidence. Now, there's a separate question of how bounded reasoners (i.e. humans) can best accomplish their goals, and "explicitly run Bayes to decide whether to get out of bed" probably isn't it. But superforecasters are an existence proof that explicit Bayesian calculations are still extremely useful even in highly uncertain domains. Mechanistic explanations are great but even having a mechanistic explanation doesn't let you say P(x) = 1. There can be enormous differences in reasonable mitigations to take between P(x) = 0.99 and 0.999999, depending on context.

ouid 4y ago
>There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". These are not the same, my observables being independent of each other is not the same thing as my not knowing about the correlated observable. In a practical sense, there are lots of ways that I could reason that there is a continent in the unexplored bit, and obviously no way to know which card will come up in a shuffle.
- comp_throw7 4y ago
  My point is that various observations appear "independent" (i.e. uncorrelated) only because of uncertainty in our map. (You could point to quantum randomness but it doesn't seem likely that this matters for most of the questions we're trying to answer, but even if it did there'd be no problem modeling it with Bayes.) It's possible I'm misunderstanding your objection, though.
- ineedasername 4y ago
  >I could reason that there is a continent in the unexplored bit, and obviously no way to know which card will come up in a shuffle. You can reason the continent exists, but you can't know. You're using knowledge you already have. Similarly, knowledge accumulates as you run through a deck, which can be used to reason that a specific card will come up next. I'm not sure I see a difference.
deleted 4y ago
[deleted]
narush 4y ago
> Predicting the next draw from a deck of cards is subject to "out of context" model violations the same way that any other prediction over future world states is. The observation that the uncertainty is only present in the observer is certainly true in the card context, but really questionable in the world states one. For one, don't we need to assume a really aggressive "deterministic evolution of future world states" to argue this uncertainty is only present in the observer? I, for one, am totally uncomfortable with this assumption... Also, there's a clear computational difference between these two settings. You kind of point this out by acknowledging that explicit Bayesian calculation are unreasonable in many settings - but in practice, I'm on a rationalist email thread where folks are trying to calculate explicit probabilities about the increased likelihood of nuclear war over the past 6 months. It's totally silly. We need to look at the actual way that Bayesian tools are actually used (and usable in practice) by it's adherents. As far as I've observed, it's mostly just silly signaling games where people make up numbers to justify whatever story they want to tell (given that the rationalist communities spend so much time worrying about confirmation bias as a fundamental one, I'm not sure this is even surprising). Also, superforcasters are most certainly not "proof that explicit Bayesian calculations are still extremely useful." There are literally _millions_ of experts who make prediction - there's no version of history where this isn't a random subset who performs dramatically better than average just due to statistical chance! Misunderstanding this as a proof of the usefulness of the probabilistic reasoning tool is the classic example of being fooled by randomness.
- ineedasername 4y ago
  >a really aggressive "deterministic evolution of future world states" What would it be if not deterministic? Chaotic sure, but that just means things are very sensitive to initial conditions. Unless we extend those initial conditions to include quantum uncertainty then things are pretty much deterministic. Of course that idea goes out the window depending on your opinions on free will.
  - goatlover 4y ago
    Even if it's entirely deterministic above the quantum/micro level, that doesn't mean we can predict anything without computing the entire future world, just like in the game of life. Also, the wavefunction is deterministic, so you could in principle compute the many worlds, but that wouldn't tell us which one we will observe.
tshaddox 4y ago
> There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". Can you explain this claim? It seems very clearly false to me, so perhaps we're thinking about things in very different terms here. To me, the extremely obvious and fundamental difference between the two is that we have a very good explanation (to use Deutsch's favorite term for emphasis, although that terminology isn't strictly important here) for why we know the probabilities of various events in a game of cards. Of course, our explanation of why we know those probabilities does depend on some assumptions of our model, namely some things about how the cards are shuffled and dealt, but the explanation doesn't really have anything to do with modeling subjective uncertainty of a particular observer. Sure, it is probably physically possible for some observer to have more certainty, like someone just outside the room with an extremely sensitive radar, but that observer would likely be understood to be violating the rules of the game (just like any card player who cheats using more feasible methods). To me, that's fundamentally different from invoking probability when discussing things like the existence of a specific thing in a specific unexplored part of the world, again assuming we don't have some explanation for why the existence of that thing in that place actually has a probability associated with it.
- comp_throw7 4y ago
  I'd have a hard time doing better than Jaynes: > Suppose you have a penny and you are allowed to examine it carefully, convince yourself that it's an honest coin; i.e. accurately round, with head and tail, and a center of gravity where it ought to be. Then, you're asked to assign a probability that this coin will come up heads on the first toss. I'm sure you'll say 1/2. > Now, suppose you are asked to assign a probability to the proposition that there was once life on Mars. Well, I don't know what your opinion is there but on the basis of all the things that I have read on the subject, I would again say about 1/2 for the probability. But, even though I have assigned the same "external" probabilities to them, I have a very different "internal" state of knowledge about those propositions. > To see this, imagine the effect of getting new information. Suppose we tossed the coin 5 times and it comes up tails every time. You ask me what's my probability for heads on the next throw; I'll still say 1/2. But if you tell me one more fact about Mars, I'm ready to change my probability assignment completely. There is something which makes my state of belief very stable in the case of the penny, but very unstable in the case of Mars. Source: https://books.google.com/books?id=tTN4HuUNXjgC&pg=PA553&lpg=PA553#v=onepage&q&f=false https://books.google.com/books?id=tTN4HuUNXjgC&pg=PA553&lpg=... Ultimately it's a question of how much each bit of evidence would cause you to update (that is, difference of degree, rather than kind).
rcthompson 4y ago
> by acting in the world you are making predictions with numbers on them all the time! This was a key insight for me several years ago. Pretty much any time you make a choice, you are implicitly making a probability calculation, something along the lines of "which of my options has the highest expected value/probability of success?" You may not be putting specific numbers on things, but you are necessarily making an inference about which of several numbers is largest. So the question is not whether you should be making predictions based on probability (you don't have a choice) but instead how you should be doing so.
- webmaven 4y ago
  > Pretty much any time you make a choice, you are implicitly making a probability calculation, something along the lines of "which of my options has the highest expected value/probability of success?" That's not quite right. The calculation is more along the lines of "which of my options has the highest risk-adjusted reward?" A 10% chance of getting $1,000 is more attractive than a 50% chance of getting $100. It should be noted though that there are other ways to model the decision, such as regret minimization and its variants.
  - rcthompson 4y ago
    It depends on the choice being made. If you're considering one of several routes on the way to work, you don't care about arrival time, you only care about probability of arriving before a certain time. In any case, the calculation isn't the point. My point is that in any non-trivial choice, you are necessarily making such a calculation, even if only approximately.

ouid 4y ago

comp_throw7 4y ago
My point is that various observations appear "independent" (i.e. uncorrelated) only because of uncertainty in our map. (You could point to quantum randomness but it doesn't seem likely that this matters for most of the questions we're trying to answer, but even if it did there'd be no problem modeling it with Bayes.) It's possible I'm misunderstanding your objection, though.
ineedasername 4y ago
>I could reason that there is a continent in the unexplored bit, and obviously no way to know which card will come up in a shuffle. You can reason the continent exists, but you can't know. You're using knowledge you already have. Similarly, knowledge accumulates as you run through a deck, which can be used to reason that a specific card will come up next. I'm not sure I see a difference.

deleted 4y ago

[deleted]

narush 4y ago

> Predicting the next draw from a deck of cards is subject to "out of context" model violations the same way that any other prediction over future world states is. The observation that the uncertainty is only present in the observer is certainly true in the card context, but really questionable in the world states one. For one, don't we need to assume a really aggressive "deterministic evolution of future world states" to argue this uncertainty is only present in the observer? I, for one, am totally uncomfortable with this assumption... Also, there's a clear computational difference between these two settings. You kind of point this out by acknowledging that explicit Bayesian calculation are unreasonable in many settings - but in practice, I'm on a rationalist email thread where folks are trying to calculate explicit probabilities about the increased likelihood of nuclear war over the past 6 months. It's totally silly. We need to look at the actual way that Bayesian tools are actually used (and usable in practice) by it's adherents. As far as I've observed, it's mostly just silly signaling games where people make up numbers to justify whatever story they want to tell (given that the rationalist communities spend so much time worrying about confirmation bias as a fundamental one, I'm not sure this is even surprising). Also, superforcasters are most certainly not "proof that explicit Bayesian calculations are still extremely useful." There are literally _millions_ of experts who make prediction - there's no version of history where this isn't a random subset who performs dramatically better than average just due to statistical chance! Misunderstanding this as a proof of the usefulness of the probabilistic reasoning tool is the classic example of being fooled by randomness.

ineedasername 4y ago
>a really aggressive "deterministic evolution of future world states" What would it be if not deterministic? Chaotic sure, but that just means things are very sensitive to initial conditions. Unless we extend those initial conditions to include quantum uncertainty then things are pretty much deterministic. Of course that idea goes out the window depending on your opinions on free will.
- goatlover 4y ago
  Even if it's entirely deterministic above the quantum/micro level, that doesn't mean we can predict anything without computing the entire future world, just like in the game of life. Also, the wavefunction is deterministic, so you could in principle compute the many worlds, but that wouldn't tell us which one we will observe.

tshaddox 4y ago

> There's no fundamental ontological difference between "give me your likelihood ratios between drawing cards [a and b] as the next draw from this deck of cards" and "give me your likelihood ratios between finding and not finding a continent in an unexplored part of the world". Can you explain this claim? It seems very clearly false to me, so perhaps we're thinking about things in very different terms here. To me, the extremely obvious and fundamental difference between the two is that we have a very good explanation (to use Deutsch's favorite term for emphasis, although that terminology isn't strictly important here) for why we know the probabilities of various events in a game of cards. Of course, our explanation of why we know those probabilities does depend on some assumptions of our model, namely some things about how the cards are shuffled and dealt, but the explanation doesn't really have anything to do with modeling subjective uncertainty of a particular observer. Sure, it is probably physically possible for some observer to have more certainty, like someone just outside the room with an extremely sensitive radar, but that observer would likely be understood to be violating the rules of the game (just like any card player who cheats using more feasible methods). To me, that's fundamentally different from invoking probability when discussing things like the existence of a specific thing in a specific unexplored part of the world, again assuming we don't have some explanation for why the existence of that thing in that place actually has a probability associated with it.

comp_throw7 4y ago
I'd have a hard time doing better than Jaynes: > Suppose you have a penny and you are allowed to examine it carefully, convince yourself that it's an honest coin; i.e. accurately round, with head and tail, and a center of gravity where it ought to be. Then, you're asked to assign a probability that this coin will come up heads on the first toss. I'm sure you'll say 1/2. > Now, suppose you are asked to assign a probability to the proposition that there was once life on Mars. Well, I don't know what your opinion is there but on the basis of all the things that I have read on the subject, I would again say about 1/2 for the probability. But, even though I have assigned the same "external" probabilities to them, I have a very different "internal" state of knowledge about those propositions. > To see this, imagine the effect of getting new information. Suppose we tossed the coin 5 times and it comes up tails every time. You ask me what's my probability for heads on the next throw; I'll still say 1/2. But if you tell me one more fact about Mars, I'm ready to change my probability assignment completely. There is something which makes my state of belief very stable in the case of the penny, but very unstable in the case of Mars. Source: https://books.google.com/books?id=tTN4HuUNXjgC&pg=PA553&lpg=PA553#v=onepage&q&f=false https://books.google.com/books?id=tTN4HuUNXjgC&pg=PA553&lpg=... Ultimately it's a question of how much each bit of evidence would cause you to update (that is, difference of degree, rather than kind).

rcthompson 4y ago

> by acting in the world you are making predictions with numbers on them all the time! This was a key insight for me several years ago. Pretty much any time you make a choice, you are implicitly making a probability calculation, something along the lines of "which of my options has the highest expected value/probability of success?" You may not be putting specific numbers on things, but you are necessarily making an inference about which of several numbers is largest. So the question is not whether you should be making predictions based on probability (you don't have a choice) but instead how you should be doing so.

webmaven 4y ago
> Pretty much any time you make a choice, you are implicitly making a probability calculation, something along the lines of "which of my options has the highest expected value/probability of success?" That's not quite right. The calculation is more along the lines of "which of my options has the highest risk-adjusted reward?" A 10% chance of getting $1,000 is more attractive than a 50% chance of getting $100. It should be noted though that there are other ways to model the decision, such as regret minimization and its variants.
- rcthompson 4y ago
  It depends on the choice being made. If you're considering one of several routes on the way to work, you don't care about arrival time, you only care about probability of arriving before a certain time. In any case, the calculation isn't the point. My point is that in any non-trivial choice, you are necessarily making such a calculation, even if only approximately.