If I physically think of slippiness then its about the object being near where I would have predicted to be but not quite. Maybe it could be due to ignoring air resistance or static friction being different from gracing friction. Those factors would seem that they probably are independent from each other. But they share the property in that I am not accounting for them.
So if one knows what correct reasoning looks like one can detect when a thing is not quite it. It might be tempting to focus on the “correct way” of doing reasoning if one has a great wish to bring more of it. But this can interfere with modelling thoughts of those that are doing it “incorrectly”. So if there is a unexplained force it might be worth it to ask what is the force. This might lead to being frustrated with the limitations with others (and I guess self). And because it doesn’t result in vulgar errors it might be tempting to keep the reference to correct reasoning. Because its “only slippery” then engaging in a “pure” way should still get you somewhere around the correct area. But at the same time at some step the approximation might break to an outrigth flaw.
If you are waling on a bridge and it creaks you have the option to get curious why it creaks. And it might be important to notice that you don’t know why it creaks. You also have the option to get habituated and accept that it creaks. If you are in a hurry to go from A to B ignorance might prodive you with speed. If you don’t model it if it collapses it is goning to do so unacceptedly. And it might not be possible to deduce from the creak how the bridge needs to be repaired (you might actually need to look at support beams etc).
I’m not sure if this is relevant, since “slipperiness” is only an analogy, but: I don’t normally think of slipperiness as a modeling failure, but as a control failure.
“Slipperiness” evokes the sort of problem where you are applying sufficient force to accomplish your goal, but your goal is not being achieved, because your force is being subtly redirected into an unintended direction (hence, the soap moves sideways instead of up).
This doesn’t necessarily involve surprise; maybe I thought I only had a 10% chance of successfully picking up the soap, so the soap slipping sideways was always the expected outcome. You might reason “if you knew it wasn’t going to work, why did you do it?” but it could be that I just don’t have a better strategy available.
However, it does feel significant that many examples from the post involve someone being unaware that their attempt at rationality has failed. That implies both a failure of control and a subsequent failure of perception. (Then again, that second problem seems to crop up all over the place in epistemology, so maybe it’s not specifically related to the “slipperiness” thing.)
I would suppose that if one had better model of traction control then soaps would not slip from hands. Jugglers are doing grabbing very accurately. Trying to grab 3 objects at once as a non-juggler will result in one of the objects slipping away largely because the object tracking is overwhelming.
I think my model for soap is approximately as detailed as my models for rocks or sticks, but the soap remains harder to pick up. That seems like a fact about the soap, rather than about my model of the soap.
If one is slipping on a ice road its too much speed for the driving conditions. On the other hand its possible to drift even on hot dry asphalt. A stun driver might burn rubber but have centimeter control to make the car go exactly where he wants. Intoxicated people slip more for standard walking tests (an DUI has different people have different control levels on the same environment so that seems to be a property of the drivers rather than the roads). You can also break from run to a stop by sliding on your two feet (for example on a gravel road). When you do this you are not often called to be “slipping”.
Also freudian slips are called slips. Usually the formed words are perfectly understandable ie fully pronounced. A lot of slip words are not challenging on the basis of their letters. And for example japanese people slipping more on english R and L is not really a property of the letters themselfs as other nationalities “grip” on them more strongly.
Extending the analog made me realise that “optimal gripping” that John Vaervake is so much a fanboy of, fits the pattern. And in that vein of memes, having arguments whether a feature is subjective or objective can be confusing if the phenomenon in question is transjective. Whether a being can effectively relate to a feature of the world depends upon the shape of both. “Slip” in this picture can be thought of as ineffectualness or wastefulness in the relatedness, an inferior stance for which a better relationship exists and is known.
I no longer believe that I understand what point you are trying to make, but a few remarks that each seem relevant to at least one of those examples:
Slipperiness is a continuum, not a binary trait.
Slipperiness is just one factor that affects your probability of success. For instance, a skilled ice-skater has less chance of falling down than a newbie, but a given patch of ice is equally “slippery” for both. (As I use the word.)
I could buy that slipperiness is a function of two surfaces interacting, rather than something that a single surface can unambiguously be said to possess. (Though knowing just one of the inputs seems to provide a lot of information about the output; e.g. wet soap is unusually slippery when combined with lots of other surfaces, not just a few.)
All of the above seem to me to be fully compatible with my original model of slipperiness as a control problem rather than a modeling problem. Climbing a steep hill is harder than climbing a gentle slope for reasons that have nothing to do with epistemology. Climbing a slippery hill is also harder for reasons that have nothing to do with epistemology.
You think it is more important to emphasise the control problem and I think that modelling is better / there is no need to priotise control. Its going to be coupled because control will utilise feedback so its hard to tease appart.
In principle I could look at the sole of my shoe with a microscope and do the same for the ground. However often I decide not to gather this information (which is a quite sensible decision) and choose to be ignorant about the microstructures. Rather I use a quick sketch of guess of the statistical properties. The cost of the ignorance is that reality does take the details into account, so in that aspect I lose (partially) track of reality. Unexpected behaviour is when that model leakage becomes apparent to me.
With the iceskating it provides a very clear example of a clear surface between metal and ice burshing against each other. This is where slipperyness should shine but if the skater retains control we don’t call this slipping. There is also the phenomenon where the ice unexpectedly lacking in slipperiness can make a skater trip (slips due to stickyness have their own unique name). I am really not exited about the concepts that would asssign “inherent difficulty” to parts of terrain so easy/hard not a fan of (stuff like friction coefficients are real though). With ice for example a skater can travel unhindered in smooth ice and has trouble covering bumpy ice while a pedestrian maintains easy access in bumpy ice and slips on smooth ice. This is a counterexample on newbie vs pro iceskater.
If I physically think of slippiness then its about the object being near where I would have predicted to be but not quite. Maybe it could be due to ignoring air resistance or static friction being different from gracing friction. Those factors would seem that they probably are independent from each other. But they share the property in that I am not accounting for them.
So if one knows what correct reasoning looks like one can detect when a thing is not quite it. It might be tempting to focus on the “correct way” of doing reasoning if one has a great wish to bring more of it. But this can interfere with modelling thoughts of those that are doing it “incorrectly”. So if there is a unexplained force it might be worth it to ask what is the force. This might lead to being frustrated with the limitations with others (and I guess self). And because it doesn’t result in vulgar errors it might be tempting to keep the reference to correct reasoning. Because its “only slippery” then engaging in a “pure” way should still get you somewhere around the correct area. But at the same time at some step the approximation might break to an outrigth flaw.
If you are waling on a bridge and it creaks you have the option to get curious why it creaks. And it might be important to notice that you don’t know why it creaks. You also have the option to get habituated and accept that it creaks. If you are in a hurry to go from A to B ignorance might prodive you with speed. If you don’t model it if it collapses it is goning to do so unacceptedly. And it might not be possible to deduce from the creak how the bridge needs to be repaired (you might actually need to look at support beams etc).
I’m not sure if this is relevant, since “slipperiness” is only an analogy, but: I don’t normally think of slipperiness as a modeling failure, but as a control failure.
“Slipperiness” evokes the sort of problem where you are applying sufficient force to accomplish your goal, but your goal is not being achieved, because your force is being subtly redirected into an unintended direction (hence, the soap moves sideways instead of up).
This doesn’t necessarily involve surprise; maybe I thought I only had a 10% chance of successfully picking up the soap, so the soap slipping sideways was always the expected outcome. You might reason “if you knew it wasn’t going to work, why did you do it?” but it could be that I just don’t have a better strategy available.
However, it does feel significant that many examples from the post involve someone being unaware that their attempt at rationality has failed. That implies both a failure of control and a subsequent failure of perception. (Then again, that second problem seems to crop up all over the place in epistemology, so maybe it’s not specifically related to the “slipperiness” thing.)
I would suppose that if one had better model of traction control then soaps would not slip from hands. Jugglers are doing grabbing very accurately. Trying to grab 3 objects at once as a non-juggler will result in one of the objects slipping away largely because the object tracking is overwhelming.
I think my model for soap is approximately as detailed as my models for rocks or sticks, but the soap remains harder to pick up. That seems like a fact about the soap, rather than about my model of the soap.
If one is slipping on a ice road its too much speed for the driving conditions. On the other hand its possible to drift even on hot dry asphalt. A stun driver might burn rubber but have centimeter control to make the car go exactly where he wants. Intoxicated people slip more for standard walking tests (an DUI has different people have different control levels on the same environment so that seems to be a property of the drivers rather than the roads). You can also break from run to a stop by sliding on your two feet (for example on a gravel road). When you do this you are not often called to be “slipping”.
Also freudian slips are called slips. Usually the formed words are perfectly understandable ie fully pronounced. A lot of slip words are not challenging on the basis of their letters. And for example japanese people slipping more on english R and L is not really a property of the letters themselfs as other nationalities “grip” on them more strongly.
Extending the analog made me realise that “optimal gripping” that John Vaervake is so much a fanboy of, fits the pattern. And in that vein of memes, having arguments whether a feature is subjective or objective can be confusing if the phenomenon in question is transjective. Whether a being can effectively relate to a feature of the world depends upon the shape of both. “Slip” in this picture can be thought of as ineffectualness or wastefulness in the relatedness, an inferior stance for which a better relationship exists and is known.
I realize this is not the most important point but man I love that your name is Slider and you’re having this convo.
I no longer believe that I understand what point you are trying to make, but a few remarks that each seem relevant to at least one of those examples:
Slipperiness is a continuum, not a binary trait.
Slipperiness is just one factor that affects your probability of success. For instance, a skilled ice-skater has less chance of falling down than a newbie, but a given patch of ice is equally “slippery” for both. (As I use the word.)
I could buy that slipperiness is a function of two surfaces interacting, rather than something that a single surface can unambiguously be said to possess. (Though knowing just one of the inputs seems to provide a lot of information about the output; e.g. wet soap is unusually slippery when combined with lots of other surfaces, not just a few.)
All of the above seem to me to be fully compatible with my original model of slipperiness as a control problem rather than a modeling problem. Climbing a steep hill is harder than climbing a gentle slope for reasons that have nothing to do with epistemology. Climbing a slippery hill is also harder for reasons that have nothing to do with epistemology.
You think it is more important to emphasise the control problem and I think that modelling is better / there is no need to priotise control. Its going to be coupled because control will utilise feedback so its hard to tease appart.
In principle I could look at the sole of my shoe with a microscope and do the same for the ground. However often I decide not to gather this information (which is a quite sensible decision) and choose to be ignorant about the microstructures. Rather I use a quick sketch of guess of the statistical properties. The cost of the ignorance is that reality does take the details into account, so in that aspect I lose (partially) track of reality. Unexpected behaviour is when that model leakage becomes apparent to me.
With the iceskating it provides a very clear example of a clear surface between metal and ice burshing against each other. This is where slipperyness should shine but if the skater retains control we don’t call this slipping. There is also the phenomenon where the ice unexpectedly lacking in slipperiness can make a skater trip (slips due to stickyness have their own unique name). I am really not exited about the concepts that would asssign “inherent difficulty” to parts of terrain so easy/hard not a fan of (stuff like friction coefficients are real though). With ice for example a skater can travel unhindered in smooth ice and has trouble covering bumpy ice while a pedestrian maintains easy access in bumpy ice and slips on smooth ice. This is a counterexample on newbie vs pro iceskater.