I actually think of “questions” and “problems” as being fairly neat concepts.
Makes sense. IIRC many abstraction researchers (like Sam Eisenstat or John Wentworth) do the same.
One objection though: your definition doesn’t define what is an interesting question/problem.
Regarding ontology...I think of flywheels as being small, toy domains which give unusually clear, unambiguous answers, which can aid in the construction of theories applicable to messier domains. “High resolution” parts of reality, if you will.
So domains can be bigger/smaller (in terms of state space), harder/easier (in terms of computational complexity) or messier/clearer (in terms of measurement error and other problems with result interpretation). The last distinction seems less fundamental.[1] Do you really need to focus on it?
Separate question: would you say that e.g. Gettier cases are a toy subdomain of philosophy, allowing to test different definitions of knowledge?
(I’m trying to probe your conceptualization of flywheels in different ways to maybe help you write down some thoughts or inspire new ideas. If it doesn’t help feel free to say so or just ignore this message.)
“Messiness” seems to pop up only when you try to interpret domain A through domain B, but you’re confused about how to do it (e.g. you don’t know statistics) or lack capabilities to do it (e.g. you don’t have precise enough instruments).
“Messiness” seems to pop up only when you try to interpret domain A through domain B, but you’re confused about how to do it (e.g. you don’t know statistics) or lack capabilities to do it (e.g. you don’t have precise enough instruments).
Hmmm, so I think of messiness as being like...essentially, since the world is complex, the best models of most phenomena have many parameters.
But this means that, for long-running disagreements between world-models, most pieces of evidence won’t be able to resolve them. Because each world-model will have a lot of free parameters to interpret a new piece of evidence in any given way. This can be especially bad for controversial topics since people will engage in motivated reasoning, which there’s plenty of room for due to the abundance of free parameters. It’s also computationally expensive.
Thus to distinguish between world models, it’s often actually easier if the data comes from a small, artificially restricted domain so that there’s less wiggle room.
As an example, consider the “pots VS peoples” theories of the indo-europeans. That was definitively resolved by DNA data. Though people had plenty of archaeological data, it was not as disambiguating.
your definition doesn’t define what is an interesting question/problem
I think of interesting problems as being those whose answers (you think will) most help you disambiguate your global world model.
Gettier cases....yes there’s some similarity in examining an edge case. Lots of philosophical thought experiments are like that(but to be honest I never really understood the intuition that the weird JTB cases were not “knowledge” or why this distinction was important)
Makes sense. IIRC many abstraction researchers (like Sam Eisenstat or John Wentworth) do the same.
One objection though: your definition doesn’t define what is an interesting question/problem.
So domains can be bigger/smaller (in terms of state space), harder/easier (in terms of computational complexity) or messier/clearer (in terms of measurement error and other problems with result interpretation). The last distinction seems less fundamental.[1] Do you really need to focus on it?
Separate question: would you say that e.g. Gettier cases are a toy subdomain of philosophy, allowing to test different definitions of knowledge?
(I’m trying to probe your conceptualization of flywheels in different ways to maybe help you write down some thoughts or inspire new ideas. If it doesn’t help feel free to say so or just ignore this message.)
“Messiness” seems to pop up only when you try to interpret domain A through domain B, but you’re confused about how to do it (e.g. you don’t know statistics) or lack capabilities to do it (e.g. you don’t have precise enough instruments).
Hmmm, so I think of messiness as being like...essentially, since the world is complex, the best models of most phenomena have many parameters.
But this means that, for long-running disagreements between world-models, most pieces of evidence won’t be able to resolve them. Because each world-model will have a lot of free parameters to interpret a new piece of evidence in any given way. This can be especially bad for controversial topics since people will engage in motivated reasoning, which there’s plenty of room for due to the abundance of free parameters. It’s also computationally expensive.
Thus to distinguish between world models, it’s often actually easier if the data comes from a small, artificially restricted domain so that there’s less wiggle room.
As an example, consider the “pots VS peoples” theories of the indo-europeans. That was definitively resolved by DNA data. Though people had plenty of archaeological data, it was not as disambiguating.
I think of interesting problems as being those whose answers (you think will) most help you disambiguate your global world model.
Gettier cases....yes there’s some similarity in examining an edge case. Lots of philosophical thought experiments are like that(but to be honest I never really understood the intuition that the weird JTB cases were not “knowledge” or why this distinction was important)
Disambiguating world models is a good motivation to focus on messiness/clarity.
Unexpected and interesting definition idea. Will look forward to future posts.