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.
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.