Can you say more about how a “frame” differs from a “model”, or a “hypothesis”?
(I understand the distinction between those three and “propositions”. It’s less clear to me how they differ from each other. And if they don’t differ, then I’m pretty sure you can just integrate over different “frames” in the usual way to produce a final probability/EV estimate on whatever proposition/decision you’re interested in. But I’m pretty sure you don’t need Garrabrant induction to do that, so I mostly think I don’t understand what you’re talking about.)
In the bayesian context, hypotheses are taken as mutually exclusive. Frames aren’t mutually exclusive, so you can’t have a probability distribution over them. For example, my physics frame and my biology frame could both be mostly true, but have small overlaps where they disagree (e.g. cases where the physics frame computes an answer using one approximation, and the biology frame computes an answer using another approximation). A tentative example: the physics frame endorses the “calories in = calories out” view on weight loss, whereas this may be a bad model of it under most practical circumstances.
By contrast, the word “model” doesn’t have connotations of being mutually exclusive, you can have many models of many different domains. The main difference between frames and models is that the central examples of models are purely empirical (i.e. they describe how the world works) rather than having normative content. Whereas the frames that many people find most compelling (e.g. environmentalism, rationalism, religion, etc) have a mixture of empirical and normative content, and in fact the normative content is compelling in part due to the empirical content. Very simple example: religions make empirical claims about god existing, and moral claims too, but when you no longer believe that god exists, you typically stop believing in their (religion-specific) moral claims.
Can you say more about how a “frame” differs from a “model”, or a “hypothesis”?
(I understand the distinction between those three and “propositions”. It’s less clear to me how they differ from each other. And if they don’t differ, then I’m pretty sure you can just integrate over different “frames” in the usual way to produce a final probability/EV estimate on whatever proposition/decision you’re interested in. But I’m pretty sure you don’t need Garrabrant induction to do that, so I mostly think I don’t understand what you’re talking about.)
In the bayesian context, hypotheses are taken as mutually exclusive. Frames aren’t mutually exclusive, so you can’t have a probability distribution over them. For example, my physics frame and my biology frame could both be mostly true, but have small overlaps where they disagree (e.g. cases where the physics frame computes an answer using one approximation, and the biology frame computes an answer using another approximation). A tentative example: the physics frame endorses the “calories in = calories out” view on weight loss, whereas this may be a bad model of it under most practical circumstances.
By contrast, the word “model” doesn’t have connotations of being mutually exclusive, you can have many models of many different domains. The main difference between frames and models is that the central examples of models are purely empirical (i.e. they describe how the world works) rather than having normative content. Whereas the frames that many people find most compelling (e.g. environmentalism, rationalism, religion, etc) have a mixture of empirical and normative content, and in fact the normative content is compelling in part due to the empirical content. Very simple example: religions make empirical claims about god existing, and moral claims too, but when you no longer believe that god exists, you typically stop believing in their (religion-specific) moral claims.