I don’t get how you’re actually proposing doing curve-fitting. Like, the axes on your chart seem fake, particularly ‘moral cases’. What does 2 vs 8 ‘moral cases’ mean? What is a concrete example of a decision where you do X without curve fitting, but Y with curve fitting?
Without an actual mathematical formalization or examples, I struggle to see what your proposal looks like in practice. This seems like another downside relative to threshold deontology, where it is comparatively intuitive to see what happens before and after the threshold.
Sorry if it wasn’t clear—this is literally just the moral case intuition, and the numbers are just meant to reflect another moral intuition that your curve can either align with or not.
Some concrete decision would be based on how one weights simplicity mathematically vs fitting data, etc. I wanted to stay agnostic about it in this post.
I think I disagree with this last point in that—it looks like threshold deontology is doing something like I am (in that it is giving two principles instead of one to fit more data), but it is often not cashing it out this way which makes it hard to figure out where you should start being more conseqentialist. One interpretation of what this proposal does is it makes it more explicit (given assumptions), so you know exactly where you’re going to jump from deontic constraints to consequences.
Like I said in the post, I think that this graph definitely doesn’t reflect all the complexities of normative theory building—it was a mere metaphor/​ very toy example. I do think that even if you think the graphic metaphor is merely that (a metaphor), you can still take my proposal conceptually seriously (as in, accept that there’s some trade-off here, and plausibly case intuitions can outweigh general principles.
I don’t think that the intuition behind ‘curve fitting’ will actually get you the properties you want, at least for the formalizations I can think of.
How would you smooth out a curve that contains the St. Petersburg paradox? Simply saying to take the average of normal intuition and expected-value calculus (which you refer to as fanaticism) doesn’t help. EV calculus is claiming an infinity. I’m not aware of curve fitting approaches that give understandable curves when you mix infinite & finite values.
Plus, again, what dimensions are you even smoothing over?
I don’t get how you’re actually proposing doing curve-fitting. Like, the axes on your chart seem fake, particularly ‘moral cases’. What does 2 vs 8 ‘moral cases’ mean? What is a concrete example of a decision where you do X without curve fitting, but Y with curve fitting?
Without an actual mathematical formalization or examples, I struggle to see what your proposal looks like in practice. This seems like another downside relative to threshold deontology, where it is comparatively intuitive to see what happens before and after the threshold.
Sorry if it wasn’t clear—this is literally just the moral case intuition, and the numbers are just meant to reflect another moral intuition that your curve can either align with or not.
Some concrete decision would be based on how one weights simplicity mathematically vs fitting data, etc. I wanted to stay agnostic about it in this post.
I think I disagree with this last point in that—it looks like threshold deontology is doing something like I am (in that it is giving two principles instead of one to fit more data), but it is often not cashing it out this way which makes it hard to figure out where you should start being more conseqentialist. One interpretation of what this proposal does is it makes it more explicit (given assumptions), so you know exactly where you’re going to jump from deontic constraints to consequences.
Like I said in the post, I think that this graph definitely doesn’t reflect all the complexities of normative theory building—it was a mere metaphor/​ very toy example. I do think that even if you think the graphic metaphor is merely that (a metaphor), you can still take my proposal conceptually seriously (as in, accept that there’s some trade-off here, and plausibly case intuitions can outweigh general principles.
I don’t think that the intuition behind ‘curve fitting’ will actually get you the properties you want, at least for the formalizations I can think of.
How would you smooth out a curve that contains the St. Petersburg paradox? Simply saying to take the average of normal intuition and expected-value calculus (which you refer to as fanaticism) doesn’t help. EV calculus is claiming an infinity. I’m not aware of curve fitting approaches that give understandable curves when you mix infinite & finite values.
Plus, again, what dimensions are you even smoothing over?
The curve is not measuring things in value but rather intuitive pull according to this data—simplicity trade-off!