I was just mentioning that any feedback loops have the potential downside of coupling things together in ways that make experiments (and analysis, improvements, etc.) difficult.
I used control theory as an example of another type of feedback loop because we were using controllers in other parts of the system. One was roughly the proportional term of a PID controller. Another was both the proportional and integral terms, but with a lot of domain specific heuristics added on (e.g., ML models to set baselines that help the controllers understand how quickly to move values). This was all incredibly powerful, but also required experiment design and analysis that was far more complicated than we would have liked.
This was not a ride-sharing company, but you can imagine dynamic pricing for Uber or Lyft working this way. Use a controller to try to keep the number of available riders and drivers in balance. If riders exceed drivers, increase payment to drivers (and therefore cost to riders), thereby attracting more riders. If drivers are sitting idle, decrease payment. Ideally, this is done constantly to respond to time of day, weather, traffic, etc. (with some predictive baseline expectations to keep it from being too slow to respond). When you get it right, riders get the cheapest price that ensures a driver is available. In economic terminology, this is finding the market clearing price. A controller can get you a long way in a hurry on such a system. But it's certainly not the only approach that can work, and it takes a lot to get it right (I have no idea how Uber and Lyft actually do any of this).
