3 ms·

> The entire point of the exercise is to establish that certain things are true And any domain where you can do that is not the real world. The real world is m

by pdonis 3mo ago

> The entire point of the exercise is to establish that certain things are true And any domain where you can do that is not the real world. The real world is messy and you can't prove things the way you can in nice, tidy, theoretical domains like math. And the kind of logic where you have fundamental axioms of reasoning and you can prove theorems is just a subset of math. In the real world, things don't exactly satisfy the axioms, and boundaries are fuzzy, not sharp. Trying to pretend otherwise, while it might not strictly be "dogma" in the usual religious sense, is just as counterproductive.

tcfhgj 3mo ago
Trying to ignore reality doesn't sound rational.
s1artibartfast 3mo ago
Im not sure what your criticism is directed at. In the real world, we can study and predict things. We can study the weather and predict if it will rain tomorrow. We can asses how accurate our predictions have been in the past. We can evaluate if it is true that smoking increases lung cancer, or that aspirin increases autism. Sometimes we dont have good answers or predictions, and that too can be studied.
- pdonis 3mo ago
  > We can evaluate if it is true that smoking increases lung cancer, or that aspirin increases autism. We can use real-world data to see if there are correlations. We can sometimes run controlled studies to get a better understanding of causal mechanisms. But we can't prove by logical argument based on "fundamental axioms of reasoning" that these things are "true".
  - s1artibartfast 3mo ago
    I see. I agree that there are major limitations to purely logical arguments. This is why unlike the rationists of the 17th century, modern "rationalists" predominately focus on empiricism (ironic i know). They are practically obsessed with Bayesian reasoning and probability of truth. This has the knock on effect of interest in prediction markets. Is your impression otherwise?
    - pdonis 3mo ago
      > They are practically obsessed with Bayesian reasoning and probability of truth. Which is still math, not the real world. Bayesian reasoning is useful, but it's not a drop-in replacement for your brain. "Rationalists" often seem to talk as though they think it is--just follow this recipe and your thinking will automatically give the right answer. And anyone who tries to talk about limitations of this is obviously a crackpot. > Is your impression otherwise? Not with respect to obsession with Bayesian reasoning, no. But I seem to have a different take on that than you do. See above.
      - s1artibartfast 3mo ago
        Im genuinely curious how you are differentiating "using your brain". What does that look like for the real world?
        pdonis 3mo ago
        > Im genuinely curious how you are differentiating "using your brain". I just mean that your brain can do lots of other things besides Bayesian reasoning, and most of them can't be reduced to Bayesian reasoning.
        s1artibartfast 3mo ago
        Like what are you thinking of that is so different? Can you give an example?
        perching_aix 3mo ago
        Inventing the hypotheses and the model in the first place. Bayesian inference updates probabilities over hypotheses already admitted by a model. If reality requires a structure that the model doesn't assign a prior probability to, such as an omitted variable, mechanism, or failure mode, ordinary Bayesian updating cannot recover it, no matter how much data is observed. For example: A forecasting model estimates restaurant demand from years of bookings. Then a major concert is announced next door. A local manager immediately expects a packed evening, while the model predicts an ordinary Tuesday because it has never encountered that situation, and was not prepared for it. To do so would have not been reasonable based on the data available to begin with, after all. So you can have someone opine about how well some argument is supported through Bayesian inference, and how another isn't, but that's only going to keep them honest in a limited sense. They can still miss the bigger picture by e.g. simply not knowing about it, and not knowing to check for it. If you don't either, they done misled you. This failure mode is identifiable without having to reason about probability theory as well by the way: https://news.ycombinator.com/item?id=48986444 https://news.ycombinator.com/item?id=48986444 If you're still unconvinced: - why would BAND have a concert here? no concerts in this area, ever - doesn't BAND have ties to this place? no sufficient evidence in the way of that - BAND lead singer has hung out here once and liked THING, decided to have a concert here for sure one day on a whim Individually, these have low local attributing info available, so you'd never be able to justify the inference. But not being able to justify the inference didn't make the claims wrong, just unfounded. Those are not the same thing. And so chasing probabilities like this, while maximizes how much your position is defensible, also boxes you in. You'll be reasonable in why you thought what you did, but that won't make you necessarily right, despite the suggestions otherwise.

by pdonis 3mo ago

tcfhgj 3mo ago

Trying to ignore reality doesn't sound rational.

s1artibartfast 3mo ago

Im not sure what your criticism is directed at. In the real world, we can study and predict things. We can study the weather and predict if it will rain tomorrow. We can asses how accurate our predictions have been in the past. We can evaluate if it is true that smoking increases lung cancer, or that aspirin increases autism. Sometimes we dont have good answers or predictions, and that too can be studied.

pdonis 3mo ago
> We can evaluate if it is true that smoking increases lung cancer, or that aspirin increases autism. We can use real-world data to see if there are correlations. We can sometimes run controlled studies to get a better understanding of causal mechanisms. But we can't prove by logical argument based on "fundamental axioms of reasoning" that these things are "true".
- s1artibartfast 3mo ago
  I see. I agree that there are major limitations to purely logical arguments. This is why unlike the rationists of the 17th century, modern "rationalists" predominately focus on empiricism (ironic i know). They are practically obsessed with Bayesian reasoning and probability of truth. This has the knock on effect of interest in prediction markets. Is your impression otherwise?
  - pdonis 3mo ago
    > They are practically obsessed with Bayesian reasoning and probability of truth. Which is still math, not the real world. Bayesian reasoning is useful, but it's not a drop-in replacement for your brain. "Rationalists" often seem to talk as though they think it is--just follow this recipe and your thinking will automatically give the right answer. And anyone who tries to talk about limitations of this is obviously a crackpot. > Is your impression otherwise? Not with respect to obsession with Bayesian reasoning, no. But I seem to have a different take on that than you do. See above.
    - s1artibartfast 3mo ago
      Im genuinely curious how you are differentiating "using your brain". What does that look like for the real world?
      - pdonis 3mo ago
        > Im genuinely curious how you are differentiating "using your brain". I just mean that your brain can do lots of other things besides Bayesian reasoning, and most of them can't be reduced to Bayesian reasoning.
        s1artibartfast 3mo ago
        Like what are you thinking of that is so different? Can you give an example?
        perching_aix 3mo ago
        Inventing the hypotheses and the model in the first place. Bayesian inference updates probabilities over hypotheses already admitted by a model. If reality requires a structure that the model doesn't assign a prior probability to, such as an omitted variable, mechanism, or failure mode, ordinary Bayesian updating cannot recover it, no matter how much data is observed. For example: A forecasting model estimates restaurant demand from years of bookings. Then a major concert is announced next door. A local manager immediately expects a packed evening, while the model predicts an ordinary Tuesday because it has never encountered that situation, and was not prepared for it. To do so would have not been reasonable based on the data available to begin with, after all. So you can have someone opine about how well some argument is supported through Bayesian inference, and how another isn't, but that's only going to keep them honest in a limited sense. They can still miss the bigger picture by e.g. simply not knowing about it, and not knowing to check for it. If you don't either, they done misled you. This failure mode is identifiable without having to reason about probability theory as well by the way: https://news.ycombinator.com/item?id=48986444 https://news.ycombinator.com/item?id=48986444 If you're still unconvinced: - why would BAND have a concert here? no concerts in this area, ever - doesn't BAND have ties to this place? no sufficient evidence in the way of that - BAND lead singer has hung out here once and liked THING, decided to have a concert here for sure one day on a whim Individually, these have low local attributing info available, so you'd never be able to justify the inference. But not being able to justify the inference didn't make the claims wrong, just unfounded. Those are not the same thing. And so chasing probabilities like this, while maximizes how much your position is defensible, also boxes you in. You'll be reasonable in why you thought what you did, but that won't make you necessarily right, despite the suggestions otherwise.