2 ms·

Indeterminacy here isn't "a roll of the die" in some significant sense. All these things, "genetics, neurones" etc. are chaotic systems. Thus there is only /ne

Indeterminacy here isn't "a roll of the die" in some significant sense. All these things, "genetics, neurones" etc. are chaotic systems. Thus there is only /necessarily/ probabilistic "conditions" on who you are; event at the classical level. It's classical physics which is necessarily indeterminate here /because/ we cannot measure precisely enough. I think this is significant enough to somehow fit it into the picture on free will. Not least, because as you say elsewhere, free will actually requires a level of determinism which may actually be absent from the world. The meaning of the future being "open" here isnt reducible to "a roll of the die". Almost all significant macroscopic systems have a "deterministic gap" between their state today and their state tomorrow. In some sense chaotic systems must be able to "forget their history", as at some point, their historical state sequences will be insufficient to determine their present state. There may be something in this which enables a certain sense of freedom, and a certain lack of it.

simonh 5y ago
I don't see how these are all chaotic systems. There are plenty of systems in the universe that are not chaotic. In fact living things, and brains in particular expend a lot of energy expelling entropy. These systems are complex for sure, but that's not at all the same thing. They are very highly ordered, and could not produce consistent repeatable outputs if they weren't. We can't predict very much about the state of a hurricane a week in advance, but my calendar at work is full of events weeks and months in advance that experience tells me have a very high likelihood of happening much as expected. Our projects are extremely complex and can take years to complete. All of that doesn't look much like a chaotic system to me.
- mjburgess 5y ago
  I need to do more research on the topic, but I think this is a little bit of an illusion. A chaotic system, K, is one in which the probability of measuring a "high order digit" (eg., 10,000) in the output is sensitive to a "low order digit" in the input (eg., 0.000001). Something like: P(log_o K(state) | log_i state) < 1 where o >> i It seems true that a non-chaotic deterministic function, eg., f(t) can be created such that "K(state) is bounded by f(approximation)". Eg., K(state) is the weather, and f(approximation) is the climate model. Or K(state) is the behaviour of gas particles, and f(approximation) is the gases temperature model. Or K(state) is the changing matter distribution of the universe, and f(approximation) is the stable gravitational field model in our solar system. f() is long-run predictable -- but K() isnt. But we know f() fails in the long-run -- K() does eventually change against the predictions of our model f(). Eg., over long periods of time, the climate changes; the non-ideal gasses may spontaneously condense; the sun "explodes". f() is only ever a locally/apparently deterministic approximation.

simonh 5y ago

I don't see how these are all chaotic systems. There are plenty of systems in the universe that are not chaotic. In fact living things, and brains in particular expend a lot of energy expelling entropy. These systems are complex for sure, but that's not at all the same thing. They are very highly ordered, and could not produce consistent repeatable outputs if they weren't. We can't predict very much about the state of a hurricane a week in advance, but my calendar at work is full of events weeks and months in advance that experience tells me have a very high likelihood of happening much as expected. Our projects are extremely complex and can take years to complete. All of that doesn't look much like a chaotic system to me.

mjburgess 5y ago
I need to do more research on the topic, but I think this is a little bit of an illusion. A chaotic system, K, is one in which the probability of measuring a "high order digit" (eg., 10,000) in the output is sensitive to a "low order digit" in the input (eg., 0.000001). Something like: P(log_o K(state) | log_i state) < 1 where o >> i It seems true that a non-chaotic deterministic function, eg., f(t) can be created such that "K(state) is bounded by f(approximation)". Eg., K(state) is the weather, and f(approximation) is the climate model. Or K(state) is the behaviour of gas particles, and f(approximation) is the gases temperature model. Or K(state) is the changing matter distribution of the universe, and f(approximation) is the stable gravitational field model in our solar system. f() is long-run predictable -- but K() isnt. But we know f() fails in the long-run -- K() does eventually change against the predictions of our model f(). Eg., over long periods of time, the climate changes; the non-ideal gasses may spontaneously condense; the sun "explodes". f() is only ever a locally/apparently deterministic approximation.