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.
Bayesian inference and decision making are somewhat distinct steps. EV maxxing is a specific (and specifically simple) objective function that you can plug Bayesian estimates into for decision making. It removes the need to think about distributions.
I think it can be helpful to separate out the problems - eg I believe that there aren’t really any plausible alternatives to Bayesian inference (over future trajectories of the world as a function of your actions or similar) but I think there’s much more room for debate regarding the objective function.
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?)
Reason 2 (edit: now 3) is not a valid criticism of correctly applied Bayesian thinking. If your initial guesses are arbitrary and weak and you are aware, you have a very wide prior. So updating will bring your posterior arbitrarily close to the right answer. Of course it’s reasonable to criticise incorrectly applied Bayesian thinking or overconfidence in your prior, or too weak updating, or anchoring.
Have you thought about including an automatic inflation adjustment?
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.
People in EA should definitely read more Feyerabend! (Or ask llms what Feyerabend would say about a topic etc).
For example “a complete theory of scientific epistemology” is something he’d most likely reject even as an ideal.
Bayesian inference and decision making are somewhat distinct steps.
EV maxxing is a specific (and specifically simple) objective function that you can plug Bayesian estimates into for decision making. It removes the need to think about distributions.
I think it can be helpful to separate out the problems - eg I believe that there aren’t really any plausible alternatives to Bayesian inference (over future trajectories of the world as a function of your actions or similar) but I think there’s much more room for debate regarding the objective function.
The hedge fund industry is not based on this. Most hedge fund investors are sophisticated and they often pay more than 2%.
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?
Nice distinction!
I’d agree that pop-Bayesianism is overused while more rigorous Bayesianism is underused.
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?)
Reason 2 (edit: now 3) is not a valid criticism of correctly applied Bayesian thinking. If your initial guesses are arbitrary and weak and you are aware, you have a very wide prior. So updating will bring your posterior arbitrarily close to the right answer.
Of course it’s reasonable to criticise incorrectly applied Bayesian thinking or overconfidence in your prior, or too weak updating, or anchoring.
Yeah this has indeed never really been the definition of a hedge fund. Only a subset of hedge funds are approximately point-in-time market neutral.