[pdonis]

> 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]

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]

> 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]

Im genuinely curious how you are differentiating "using your brain". What does that look like for the real world?

[pdonis]

> 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]

Your response is dead for some reason and my vouch isnt enough to fix it.

Why cant the model be updated to include information about the concert? This is how basically every human works anyways.

It seems that yes, a bad model is not recoverable if you refuse to consider concerts or other data.

[perching_aix]

It's not that the model can't be fixed, but that it won't create and fix itself, unlike how people iterate on their own mental models.

You can build a more elaborate model that can, but then that's still structurally constrained in terms of how it can adapt.

You can build a maximally general model that automatically captures arbitrary patterns, and estimates using them, but:

- that's not what people do and mean when using Bayesian inference in argumentation; they're the ones capturing and maintaining the patterns instead, manually and arbitrarily

- it kind of renders the whole thing meaningless; you basically get a machine learning model out of that, and introspectibility is not really their thing + it becomes quite literally arbitrarily adapting (and thus arbitrarily inferring)

One may also recognize that to be resembling a human, with whom you get the same issues. Kind of how one gets to arguing about empiricals and logic in the first place. This is what I describe in my other comment that I linked to, that debates using these instruments are basically just long winded coordination processes between people in the best of times, not some sort of divination tools.

I'm not the guy from above btw, but I am the guy who had their comment turn dead. I don't know why it did though, I doubt this subthread sees that much traffic, and I did not see it being flagged since I posted it. I'd assume it was automatically or manually moderated out.

[s1artibartfast]

Like what are you thinking of that is so different? Can you give an example?

[perching_aix]

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

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.