The strategic advantage of stabilizing errors in uncertain environments
This weekend I was talking to a friend from graduate school about how to stabilize errors in uncertain environments by sacrificing accuracy, the subject of the previous two posts. During the conversation, I realized another advantage of stabilizing errors: it makes it easier to modify your detector/classifier in an adversarial situation.
The “arms race” game is a common one in conflict situations. You have some technological advantage over an opponent. The opponent adapts and changes their strategy. This is different from the issue of a noisy environment. Here the signal used by your detector is intentionally modified to circumvent its performance level. The enemy camouflages their tanks better. New strategies are deployed. All of which leads to an increase in the error rate of your detector.
A user confronted with this situation would want to slow down their error rate increase so it can be below their learning rate. Such a user would be willing to sacrifice accuracy for the ability to adapt to an adversary. There is a strategic advantage to stabilizing errors.
