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>complex systems, stochasticity, nonlinearity, feedback loops and higher-order effects ... emergent behavior Plasma physics & fusion energy (my field) is chall
by lambdatronics 6y ago
>complex systems, stochasticity, nonlinearity, feedback loops and higher-order effects ... emergent behavior
Plasma physics & fusion energy (my field) is challenging for exactly these reasons (despite being 95% classical physics). It's very rare that we can do a nice controlled experiment where only a single variable is changed at a time. I joke that it's really a subset of biology, not physics.
> probably more useful to focus on instrumental causality
I partly agree -- we humans do seem to get by on rudimentary reasoning. On the other hand, the issue of back-progagation is quite similar to identifying 'root causes.' There's also an issue of combinatorial explosion of the number of possible sets of variables that interact with each other, coupled with the fact that data becomes exponentially sparse as the dimension of the space grows. The human ability to detect causal relations is really stunning when you realize how tough it is -- I wouldn't want to bet that we can reproduce it by trial and error. Evolution had plenty of time to get it right, but we don't.
- wenc 6y agoVery much so. If we could quantitatively characterize every little effect, we might be able to model complex systems accurately, but it isn't always possible (exceptions exist, like meteorological modeling). For the longest time in biology, the modeling approach has tended to be more qualitative than quantitative, though this is changing. Yes, identifying root-causes from the outcomes is a model inversion problem (here's the data, find the generating model), and model inversion problems tend to be ill-conditioned. In complex systems, there's so many possible combinations of causes that could have led to a single outcome that characterizing the entire set is difficult.