different ways to get to the same endpoint—…as far as anyone can measure it
I would say the territory has no cycles but any map of it does. You can have a butterfly effect where a small nudge is amplified to some measurable difference but you cannot predict the result of that measurement. So the agent’s revealed preferences can only be modeled as a graph where some states are reachable through multiple paths.
I think it’s both in the map, as a description, but I also think the behavior itself is in the territory, and my point is that you can get the same result but have different paths to get to the result, which is in the territory.
Also, I treat the map-territory difference in a weaker way than LW often assumes, where things in the map can also be in the territory, and vice versa.
I would say the territory has no cycles but any map of it does. You can have a butterfly effect where a small nudge is amplified to some measurable difference but you cannot predict the result of that measurement. So the agent’s revealed preferences can only be modeled as a graph where some states are reachable through multiple paths.
I actually disagree that there are no cycles/multiple paths to the same endpoint in the territory too.
In particular, I’m thinking of function extensionality, where multiple algorithms with wildly different run-times can compute the same function.
This is an easy source of examples where there are multiple starting points but there exists 1 end result (at least probabilistically).
A function in this context is a computational abstraction. I would say this is in the map.
I think it’s both in the map, as a description, but I also think the behavior itself is in the territory, and my point is that you can get the same result but have different paths to get to the result, which is in the territory.
Also, I treat the map-territory difference in a weaker way than LW often assumes, where things in the map can also be in the territory, and vice versa.