Putting randomness in your algorithms is only useful when there are second-order effects, when somehow reality changes based on the content of your algorithm in some way other than you executing your algorith. We see this in Rock-Paper-Scissors, where you use randomness to keep your opponent from predicting your moves based on learning your algorithm.
Barring these second order effects, it should be plain that randomness can’t be the best strategy, or at least that there’s a non-random strategy that’s just as good. By adding randomness to your algorithm, you spread its behaviors out over a particular distribution, and there must be at least one point in that distribution whose expected value is at least as high as the average expected value of the distribution.
Putting randomness in your algorithms is only useful when there are second-order effects, when somehow reality changes based on the content of your algorithm in some way other than you executing your algorith. We see this in Rock-Paper-Scissors, where you use randomness to keep your opponent from predicting your moves based on learning your algorithm.
Barring these second order effects, it should be plain that randomness can’t be the best strategy, or at least that there’s a non-random strategy that’s just as good. By adding randomness to your algorithm, you spread its behaviors out over a particular distribution, and there must be at least one point in that distribution whose expected value is at least as high as the average expected value of the distribution.