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This is tightly related to AI as well: it's the question of local vs. global optimization in a non-differentiable system, open-ended goal-setting, objective propagation from one module to its neighbors, parent, and internal submodules...
Backprop is a centralized answer, that only applies to differentiable functions where "control" means adjusting some parameters and where the optimization objective is already known. Its range of applicability is minuscule
The objective function isn't always known e.g. GANs. RL problems also involve objectives that can only be sampled from. I get the local vs global argument, but it's unclear what you mean about backprop being limited by objectives.
"known" objective doesn't mean "can be expressed analytically as a func of the data", it means you know what you're optimizing for from the start. Your GAN implementation itself is an expression of a known optimization objective. "Unknown objective" refers to open-ended problems.
Hallo there is your unroll: https://threadreaderapp.com/thread/1013361461316476928.html …
See you soon. 
@threadreaderapp this is too good to only be a tweet thread, please unroll it, at least until it is a full blog post.
Saluti the unroll you asked for: https://threadreaderapp.com/thread/1013361461316476928.html …
Have a good day.
This thread is about exactly the kinds of technology decisions I'm constantly weighing the pros and cons of. The core domain we work on has non-differentiable functions that need optimization. But I believe there are ways to solve it well in a split way using both modes properly.
Central planning fails because it cannot allocate resources that are not directly consumable such as freight transport because it lacks a price system to effectively compare alternatives. See "Economic Calculation in the Socialist Commonwealth" by Von Mises.
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