Feedback loops can be...
- Fast / Slow
- Accurate / Inaccurate
Tight feedback loop means accurate and fast. This is in theory great to have. But the more advanced your skill level is, the less useful it becomes. The feedback will become noisy when you are very proficient at your field.
In noisy feedback loops, the best option is hypotheses and pruning. Create multiple hypotheses of the future - and prune the list based on feedback. If collected data does not work well with a future you had in mind, eliminate that hypotheses. Noisy information may be bad for feedback loops, but it’s great for elimination, because the wider range of outcomes is more effective for pruning.
When we are close to the time for a decision, select a hypotheses from the remaining list.
Process improvement happens by creating many parallel processes and discarding the ones that are unfit, not by iteratively improving a single process.