The question “How do we know which drugs work and are beneficial to patients?” is an applied epistemology question. Looking at how it gets answered by a sophisticated system tells you how epistemology actually works in practice instead of how philosophers think it’s supposed to work in their ivory tower. If you use Bayesian statistics you want an epistemology behind that use that guides you in how you use the statistics to reason.
Superforcasting is much more about Bayesian epistemology than about Bayesian statistics. You have Superforcasters who would they they are Bayesians but can’t write down Bayes theorem.
You seem to have some idea that epistemology is supposed to be “objective”. It’s supposed to give you the answer from God’s view. A lot of Western science is build around wanting to reach God’s view. The problem is that God doesn’t exist. According to Nietzsche, he’s dying. Bayesian epistemology is an epistomology without God, which means that you have to deal with your beliefs and other peoples beliefs.
The reason to bother with Bayesianism is not because it helps you to see the world from God’s view but because it has practical utility in applied epistemology with FDA drug approval and Superforcasting being two examples.
You seem to have some idea that epistemology is supposed to be “objective”. It’s supposed to give you the answer from God’s view.
Objective in a sense, but I’m not sure how I’ve given you the ‘God’s view’ impression. I think epistemology should be objective in that it should work universally via the same rules, and that people can discuss both ideas and the world (evidence) in such a way to reach agreement in an objective sense. But they can be wrong, there’s no infallible method of getting to the truth. They can also objectively agree on each other’s subjective states.
The reason to bother with Bayesianism is not because it helps you to see the world from God’s view but because it has practical utility in applied epistemology with FDA drug approval and Superforcasting being two examples.
Putting aside superforecasting because I don’t know much about it, using bayesian statistics for statistical analysis is fine.
But that’s not what’s happening on LW. When people on LW talk about their priors and updating them, they’re not talking about bayesian statistics, they’re talking about epistemology, about what ideas are true. I think those are fundamentally different things and they work in different ways (and it seems like you do, too). I’m here because LW is the largest bayesian forum (or if not the largest it’s better than reddit for discussion, point is, it’s the best option for talking to bayesians).
The idea that we should apply bayesian statistics to epistemic tasks is what I’m interested in discussing.
If superforecasting is important to discuss, can you link me to something that represents what you mean by it?
If you use frequentist statistics you can just claim that your data follows a normal distribution (which it objectively most likely isn’t, even the archtypical example of height violates a normal distribution because there are more people with dwarfism than the normal distribution assumes). On the other hand, to use Bayesian statistics you do need to decide on priors which does involve subjective decisions.
Objective in a sense, but I’m not sure how I’ve given you the ‘God’s view’ impression. I think epistemology should be objective in that it should work universally via the same rules, and that people can discuss both ideas and the world (evidence) in such a way to reach agreement in an objective sense.
The fact that you aren’t explicitly thinking of God doesn’t mean that the idea of objectivity does not come out of a Christian scientific tradition which had as a key motivation trying to see things from God’s view. Your ideas of how you think, it should work have that theistic origin.
Bayesianism as discussed on LessWrong is about how it would be good for an agent to reason and that’s a different goal. Eliezer was interested in it because he wants to know how what’s true about how agents effectively reason to be able to say things about superintelligence.
Philip E. Tetlock separately was interested in the epistemic task about how to reason about what to believe. After the Iraq war the US military thought they had problems with epistomology as shown by the fact that they got the WMD question so horribly wrong. Out of that there was an IARPA tournament where Tetlock’s team won. Tetlock runs GJOpen which does solve epistemic problems for entities that want probability for certain events happening. He got some grant money from OpenPhil. There’s also Metaculus that comes out of rationalist sphere. His book Superforcastingis a good resource if you want to understand how the kind of Bayesianism where people don’t explicitly use statistics works in practice.
The question “How do we know which drugs work and are beneficial to patients?” is an applied epistemology question. Looking at how it gets answered by a sophisticated system tells you how epistemology actually works in practice instead of how philosophers think it’s supposed to work in their ivory tower. If you use Bayesian statistics you want an epistemology behind that use that guides you in how you use the statistics to reason.
Superforcasting is much more about Bayesian epistemology than about Bayesian statistics. You have Superforcasters who would they they are Bayesians but can’t write down Bayes theorem.
You seem to have some idea that epistemology is supposed to be “objective”. It’s supposed to give you the answer from God’s view. A lot of Western science is build around wanting to reach God’s view. The problem is that God doesn’t exist. According to Nietzsche, he’s dying. Bayesian epistemology is an epistomology without God, which means that you have to deal with your beliefs and other peoples beliefs.
The reason to bother with Bayesianism is not because it helps you to see the world from God’s view but because it has practical utility in applied epistemology with FDA drug approval and Superforcasting being two examples.
Objective in a sense, but I’m not sure how I’ve given you the ‘God’s view’ impression. I think epistemology should be objective in that it should work universally via the same rules, and that people can discuss both ideas and the world (evidence) in such a way to reach agreement in an objective sense. But they can be wrong, there’s no infallible method of getting to the truth. They can also objectively agree on each other’s subjective states.
Putting aside superforecasting because I don’t know much about it, using bayesian statistics for statistical analysis is fine.
But that’s not what’s happening on LW. When people on LW talk about their priors and updating them, they’re not talking about bayesian statistics, they’re talking about epistemology, about what ideas are true. I think those are fundamentally different things and they work in different ways (and it seems like you do, too). I’m here because LW is the largest bayesian forum (or if not the largest it’s better than reddit for discussion, point is, it’s the best option for talking to bayesians).
The idea that we should apply bayesian statistics to epistemic tasks is what I’m interested in discussing.
If superforecasting is important to discuss, can you link me to something that represents what you mean by it?
If you use frequentist statistics you can just claim that your data follows a normal distribution (which it objectively most likely isn’t, even the archtypical example of height violates a normal distribution because there are more people with dwarfism than the normal distribution assumes). On the other hand, to use Bayesian statistics you do need to decide on priors which does involve subjective decisions.
The fact that you aren’t explicitly thinking of God doesn’t mean that the idea of objectivity does not come out of a Christian scientific tradition which had as a key motivation trying to see things from God’s view. Your ideas of how you think, it should work have that theistic origin.
Bayesianism as discussed on LessWrong is about how it would be good for an agent to reason and that’s a different goal. Eliezer was interested in it because he wants to know how what’s true about how agents effectively reason to be able to say things about superintelligence.
Philip E. Tetlock separately was interested in the epistemic task about how to reason about what to believe. After the Iraq war the US military thought they had problems with epistomology as shown by the fact that they got the WMD question so horribly wrong. Out of that there was an IARPA tournament where Tetlock’s team won. Tetlock runs GJOpen which does solve epistemic problems for entities that want probability for certain events happening. He got some grant money from OpenPhil. There’s also Metaculus that comes out of rationalist sphere. His book Superforcasting is a good resource if you want to understand how the kind of Bayesianism where people don’t explicitly use statistics works in practice.
I’m not sure what the best shorter source is but maybe https://www.gjopen.com/training/