Can you elaborate? Do you mean “should” in the sense that it possibly ought not to do that or do you mean it might not be close enough? (In the latter case it seems to be more about specific parameters in practice rather than the concept itself?)
I mean “do I expect to see, within the relevant time frame, enough information to make Bayesian updating with a very wrong (or just very wide) initial distribution useful rather than harmful?”.
very much intra-Bayesian and not a criticism of Bayesian reasoning?
Not really. It’s a criticism of a system that allows (and even encourages) you to pretend to know things when you don’t.
Of course, I’m a mathematician and I think Bayesian reasoning is fundamentally correct and is useful in some contexts. Just not the contexts EA uses it for.
Yeah I think the obvious Bayesian reply is: Just make your prior less informative if you know less? It’s fine to choose it to be close to uniform over a very wide range if you know close to nothing.
I think the main concern is that people often use the expected value of such a wide distribution in utility maximisation. So the concerning part is the interaction between eg utilitarianism and Bayesian inference.
Can you elaborate? Do you mean “should” in the sense that it possibly ought not to do that or do you mean it might not be close enough? (In the latter case it seems to be more about specific parameters in practice rather than the concept itself?)
I mean “do I expect to see, within the relevant time frame, enough information to make Bayesian updating with a very wrong (or just very wide) initial distribution useful rather than harmful?”.
I see, fair.
What’s the alternative to “wide distribution stays wide?” in practice?
Separately, “very wrong and narrow prior” is a problem of course, but very much intra-Bayesian and not a criticism of Bayesian reasoning?
Not really. It’s a criticism of a system that allows (and even encourages) you to pretend to know things when you don’t.
Of course, I’m a mathematician and I think Bayesian reasoning is fundamentally correct and is useful in some contexts. Just not the contexts EA uses it for.
Yeah I think the obvious Bayesian reply is: Just make your prior less informative if you know less? It’s fine to choose it to be close to uniform over a very wide range if you know close to nothing.
I think the main concern is that people often use the expected value of such a wide distribution in utility maximisation. So the concerning part is the interaction between eg utilitarianism and Bayesian inference.