The weather, or the behavior of any economy larger than village size, for example—systems so chaotically interdependent that exact prediction is effectively impossible (not just in fact but in principle).
Flagging that those two examples seem false. The weather is chaotic, yes, and there’s a sense in which the economy is anti-inductive, but modeling methods are advancing, and will likely find more loop-holes in chaos theory.
For example, in thermodynamics, temperature is non-chaotic while the precise kinetic energies and locations of all particles are. A reasonable candidate similarity in weather are hurricanes.
Similarly as our understanding of the economy advances it will get more efficient which means it will be easier to model. eg (note: I’ve only skimmed this paper). And definitely large economies are even more predictable than small villages, talk about not having a competitive market!
Thanks for the pointer to that paper, the abstract makes me think there’s a sort of slow-acting self-reinforcing feedback loop between predictive error minimisation via improving modelling and via improving the economy itself.
re: weather, I’m thinking of the chart below showing how little gain we get in MAE vs compute, plus my guess that compute can’t keep growing far enough to get MAE < 3 °F a year out (say). I don’t know anything about advancements in weather modelling methods though; maybe effective compute (incorporating modelling advancements) may grow indefinitely in terms of the chart.
I didn’t say anything about temperature prediction, and I’d also like to see any other method (intuition based or otherwise) do better than the current best mathematical models here. It seems unlikely to me that the trends in that graph will continue arbitrarily far.
Thanks for the pointer to that paper, the abstract makes me think there’s a sort of slow-acting self-reinforcing feedback loop between predictive error minimisation via improving modelling and via improving the economy itself.
Flagging that those two examples seem false. The weather is chaotic, yes, and there’s a sense in which the economy is anti-inductive, but modeling methods are advancing, and will likely find more loop-holes in chaos theory.
For example, in thermodynamics, temperature is non-chaotic while the precise kinetic energies and locations of all particles are. A reasonable candidate similarity in weather are hurricanes.
Similarly as our understanding of the economy advances it will get more efficient which means it will be easier to model. eg (note: I’ve only skimmed this paper). And definitely large economies are even more predictable than small villages, talk about not having a competitive market!
Thanks for the pointer to that paper, the abstract makes me think there’s a sort of slow-acting self-reinforcing feedback loop between predictive error minimisation via improving modelling and via improving the economy itself.
re: weather, I’m thinking of the chart below showing how little gain we get in MAE vs compute, plus my guess that compute can’t keep growing far enough to get MAE < 3 °F a year out (say). I don’t know anything about advancements in weather modelling methods though; maybe effective compute (incorporating modelling advancements) may grow indefinitely in terms of the chart.
I didn’t say anything about temperature prediction, and I’d also like to see any other method (intuition based or otherwise) do better than the current best mathematical models here. It seems unlikely to me that the trends in that graph will continue arbitrarily far.
Yeah, that was my claim.