My (then 20 yo) brother broke down seduction into a bunch of rules and steps like an engineer. In fact the diary was written down in a lab book he got at the campus bookstore. Step 1 do this, step 2 do that, then step 3 say this. He pulled from men we knew and the seduction techniques of Richard Feynman (bro was at Caltech in the 80s). Funny part is it did work. He had a high body count. But it was this rotating door of the same kind of gal over and over. They all had serious relationship issues. Go figure they were horny for a robot.

I got the idea he was following the same recipe based on the same universal understanding of how women ticked and it got him the same result. It was a repeatable experiment. He got laid. By the same gal in a different body. The relationships all had similar problems and ended exactly the same way. Then he’d rinse and repeat. Madness. He never saw the big picture because his system “worked” at getting him laid!

I’m not terribly experienced in these things. I kind of treat it like dynamic response and feedback which I am familiar with. For me each instance is an unknown feedback system where I have to turn the gain way down if I have any hope at stability. The rules or physics depends a lot on what is in the system. And, the plant(process accumulator) dynamics can vary widely. If you don’t know what response you’ll get it’s good to start with a very low amplitude impulse and heavily process the response before making adjustments to system coefficients. This will deal with a good number of systems unlike hard physical laws which tend to throw dynamic systems off the rails. I think the convolutional neural nets of late approach reality this way using a lot of real downstream data to train the upstream processing filter thus resulting in a system that works as a single unit. The analogy to human relationships is strong because AI people are trying to emulate biology with circuitry.
When I look at my brother and dad’s design of experiments they only use feed forward schemes. Dad never could understand why feedback resulted in high accuracy and linearity across a broad variety of plants. His ideas were locked in precision feed forward designs that could only handle specific system dynamics. When things didn’t work they just swapped in another plant till they got the desired result. Which was inevitably the same plant.