Very cool that experimental design can be viewed through the lens of inverse problems and optimization! In practice, how often can we use optimization to inform our experimental design choices? For example, I think optimization could lend itself very naturally to physics experimental design, but would it be equally effective for sociology experiments? I feel it would be harder to quantify and optimize our design if our measurement tool was a Google form asking "how did you feel on a scale from 1 to 10 after this?" compared to using an oscilloscope. Do we as the researchers have to make a judgement call on this, independent of the optimization side of things?
Very cool that experimental design can be viewed through the lens of inverse problems and optimization! In practice, how often can we use optimization to inform our experimental design choices? For example, I think optimization could lend itself very naturally to physics experimental design, but would it be equally effective for sociology experiments? I feel it would be harder to quantify and optimize our design if our measurement tool was a Google form asking "how did you feel on a scale from 1 to 10 after this?" compared to using an oscilloscope. Do we as the researchers have to make a judgement call on this, independent of the optimization side of things?